24.09.

2022
6. Organizational Excellence through TQM, H. Lal, New age Publications, 2008.
7. Introduction to Operations Research- Concepts and Cases, F.S. Hillier. G.J. Lieberman, Tata McGraw Hill, 9th
Edition, 2010
Web links and Video Lectures (e-Resources):
 https://www.investopedia.com/terms/t/total-quality-management-tqm.asp
 https://www.youtube.com/watch?v=VD6tXadibk0
 https://aboutthree.com/blog/five-important-factors-in-total-quality-management/
 https://www.youtube.com/watch?v=renlXcpK9sk
 https://www.youtube.com/watch?v=umqtSNPp5Dk
 https://study.com/academy/lesson/five-principles-of-total-quality-management-tqm.html
 https://www.greenlight.guru/blog/total-quality-management-principles

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

At the end of the lecture/presentation, numerical exercises are to be taken up to solve problems related to the
topics covered. Additional problems are to be given for practice and also as assignments under each of the topics
covered.

Value Engineering
Course Code 21IP / IM652 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 2:2:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course objectives:
 Be able to relate value engineering to costs, and its application to decision making.
 Be able to use value engineering as an economic analysis tool.
 Be able to apply SMART methodology in group decision environment.

Teaching-Learning Process (General Instructions)

 Lectures and discussions
 Self study assignments
 Case studies and group discussions.

Module-1
INTRODUCTION TO VALUE ANALYSIS: Definition of Value, Value Analysis, Value Engineering, Value management,
Value Analysis versus Value Engineering, Value Analysis versus Traditional cost reduction techniques, uses,
Applications, advantages and limitations of Value analysis. Symptoms to apply value analysis, Coaching of
Champion concept.
TYPE OF VALUES: Reasons for unnecessary cost of product, Peeling cost Onion concept, unsuspected areas
responsible for higher cost, Value Analysis Zone, attractive features of value analysis. Meaning of Value, types of
value & their effect in cost reduction. Value analysis procedure by simulation. Detailed case studies of simple
products
Teaching-Learning Chalk and Talk, Power point presentation and Lab Visit.
Process
Module-2

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FUNCTIONAL COST AND ITS EVALUATION: Meaning of Function and Functional cost, Rules for functional
definition, Types of functions, primary and secondary functions using verb and Noun, Function evaluation process,
Methods of function evaluation. Evaluation of function by comparison, Evaluation of Interacting functions,
Evaluation of function from available data, matrix technique, MISS technique, Numerical evaluation of functional
relationships and case studies.
PROBLEM SETTING & SOLVING SYSTEM: A problem solvable stated is half solved, Steps in problem setting
system, Identification, Separationand Grouping of functions. Case studies.
PROBLEM SETTING & SOLVING SYSTEM: Goods system contains everything the task requires. Various steps in
problem solving, case studies.
Teaching-Learning Chalk and Talk, Power point presentation and Lab Visit.
Process
Module-3
VALUE ENGINEERING JOB PLAN: Meaning and Importance of Value Engineering Job plan. Phases of job plan
proposed by different value engineering experts, Information phase, Analysis phase, Creative phase, Judgment
phase, Development planning phase, and case studies. Cost reduction programs, criteria for cost reduction
program, Value analysis change proposal.
Teaching-Learning Chalk and Talk, Power point presentation and Lab Visit.
Process
Module-4
VALUE ENGINEERING TECHNIQUES: Result Accelerators or New Value Engineering Techniques, Listing, Role of
techniques in Value Engineering, Details with Case examples for each of the Techniques.
ADVANCED VALUE ANALYSIS TECHNIQUES: Functional analysis system technique and case studies, Value
analysis of Management practice (VAMP), steps involved in VAMP, application of VAMP to Government, University,
College, Hospitals, School Problems etc., (service type problems).
TOTAL VALUE ENGINEERING: Concepts, need, Methodology and benefits.
Teaching-Learning
Process Chalk and Talk, Power point presentation and Lab Visit.

Module-5
APPLICATION OF VALUE ANALYSIS: Application of Value analysis in the field of Accounting, Appearance Design,
Cost reduction, Engineering, manufacturing, Management, Purchasing, Quality Control, Sales, marketing, Material
Management Etc., Comparison of approach of Value analysis & other management techniques.
Teaching-Learning Chalk and Talk, Power point presentation and Lab Visit.
Process
Course outcome (Course Skill Set)
At the end of the course the student will be able to :
1. Able to understand the importance of value of a product
2. Find out unnecessary cost/ function involved in the product
3. Conduct value engineering methodology
4. Do value analysis using advanced value engineering techniques
5. Become a certified value engineer with additional course /training

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Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%. The
minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be deemed to have
satisfied the academic requirements and earned the credits allotted to each subject/ course if the student secures
not less than 35% ( 18 Marks out of 50)in the semester-end examination(SEE), and a minimum of 40% (40 marks
out of 100) in the sum total of the CIE (Continuous Internal Evaluation) and SEE (Semester End Examination) taken
together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20 Marks
(duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks and will be
scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the methods of
the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper is designed to attain the different levels of Bloom’s taxonomy as per the
outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question papers for the
subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks.
2. There will be 2 questions from each module. Each of the two questions under a module (with a maximum of 3
sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module. Marks scored out of
100 shall be proportionally reduced to 50 marks.
Suggested Learning Resources:
Books
 Techniques of Value Analysis and Engineering, Lawrence D.Miles, 2nd Edn.
 Value engineering for Cost Reduction and Product, M.S. Vittal, Systems Consultancy Services Edn, 1991.
 Value anagement, Value Engineering and Cost Reduction, Edward D Heller, Addison Wesley Publishing
Company, 1991
 Value Analysis for Better Management, Warren J Ridge, American Management Association Edn, 1969.
 Getting More at Less Cost (The Value Engineering Way), G.Jagannathan, Tata Mcgraw Hill Pub.Comp. Edn,
1995.
 Value Engineering, Arther E Mudge, McGraw Hill Book Comp.Edn, 1981
Web links and Video Lectures (e-Resources):
 https://www.youtube.com/watch?v=L-TfAfip1ME
 https://www.youtube.com/watch?v=mJoaZ4GewyI
 http://www.simplynotes.in/e-notes/mbabba/productivity-management/value-analysis/
 https://www.youtube.com/watch?v=mJoaZ4GewyI
 https://www.value-eng.org/page/AboutVM

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 VI Semester

SOFTWARE ENGINEERING & PROJECT MANAGEMENT

Course Code 21CS61 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 2:2:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives
CLO 1. Outline software engineering principles and activities involved in building large software
programs. Identify ethical and professional issues and explain why they are of concern to
Software Engineers.
CLO 2. Describe the process of requirement gathering, requirement classification, requirement
specification and requirements validation.
CLO 3. Infer the fundamentals of object oriented concepts, differentiate system models, use UML
diagrams and apply design patterns.5
CLO 4. Explain the role of DevOps in Agile Implementation.
CLO 5. Discuss various types of software testing practices and software evolution processes.
CLO 6. Recognize the importance Project Management with its methods and methodologies.
CLO 7. Identify software quality parameters and quantify software using measurements and
metrics. List software quality standards and outline the practices involved
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Software and Software Engineering: The nature of Software, The unique nature of WebApps,
Software Engineering, The software Process, The software Engineering practice, The software
myths, How it all starts
Textbook 1: Chapter 1: 1.1 to 1.7
Process Models: A generic process model, Process assessment and improvement, Prescriptive
process models, Waterfall model, Incremental process models, Evolutionary process models, Concurrent
models, Specialized process models.
Textbook 1: Chapter 2: 2.1 to 2.4

30.04.2024
 Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
Understanding Requirements: Requirements Engineering, Establishing the ground work,
Eliciting Requirements, Developing use cases, Building the requirements model, Negotiating
Requirements, Validating Requirements
Textbook 1: Chapter 5: 5.1 to 5.7
Requirements Modeling Scenarios, Information and Analysis classes: Requirement Analysis,
Scenario based modeling, UML models that supplement the Use Case, Data modeling Concepts class
Based Modeling.
Textbook 1: Chapter 6: 6.1 to 6.5
Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
AGILE DEVELOPMENT: What is Agility?, Agility and the cost of change. What is an agile Process?,
Extreme Programming (XP), Other Agile Process Models, A tool set for Agile process
Principles that guide practice: Software Engineering Knowledge, Core principles, Principles that
guide each framework activity
Textbook 1: Chater 3: 3.1 to 3.6, Chapter 4: 4.1 to 4.4
Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-4
Introduction to Project Management:
Introduction, Project and Importance of Project Management, Contract Management, Activities Covered
by Software Project Management, Plans, Methods and Methodologies, Some ways of categorizing
Software Projects, Stakeholders, Setting Objectives, Business Case, Project Success and Failure,
Management and Management Control, Project Management life cycle, Traditional versus Modern
Project Management Practices.
Textbook 2: Chapter 1: 1.1 to 1.17
Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-5
Software Quality:
Introduction, The place of software quality in project planning, Importance of software quality, Defining
software quality, quality models, ISO 9126, product and process metrics, product versus process quality
management, Quality Management systems, process capability models, techniques to enhance software
quality, testing, Software reliability, quality plans.
Textbook 2: Chapter 13: (13.1 to 13.14)

30.04.2024
 Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Course Outcomes
At the end of the course the student will be able to:
CO 1. Understand the activities involved in software engineering and analyze the role of various
process models
CO 2. Explain the basics of object-oriented concepts and build a suitable class model using modelling
techniques
CO 3. Describe various software testing methods and to understand the importance of agile
methodology and DevOps
CO 4. Illustrate the role of project planning and quality management in software development
CO 5. Understand the importance of activity planning and different planning models

Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s
taxonomy as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored
shall be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module
Suggested Learning Resources:
Textbooks
1. Roger S. Pressman: Software Engineering-A Practitioners approach, 7th Edition, Tata McGraw
Hill.

30.04.2024
 2. Bob Hughes, Mike Cotterell, Rajib Mall: Software Project Management, 6 th Edition, McGraw Hill
Education, 2018.
Reference:
1. Pankaj Jalote: An Integrated Approach to Software Engineering, Wiley India.

Weblinks and Video Lectures (e-Resources):

1. https://onlinecourses.nptel.ac.in/noc20_cs68/preview
2. https://www.youtube.com/watch?v=WxkP5KR_Emk&list=PLrjkTql3jnm9b5nr-
ggx7Pt1G4UAHeFlJ
3. http://elearning.vtu.ac.in/econtent/CSE.php
4. http://elearning.vtu.ac.in/econtent/courses/video/CSE/15CS42.html
5. https://nptel.ac.in/courses/128/106/128106012/ (DevOps)
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

Case study, Field visit

30.04.2024
 03092022

VI Semester

FULLSTACK DEVELOPMENT
Course Code 21CS62 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:2:0 SEE Marks 50
Total Hours of Pedagogy 40 T + 20 P Total Marks 100
Credits 04 Exam Hours 03
Course Learning Objectives:
CLO 1. Explain the use of learning full stack web development.
CLO 2. Make use of rapid application development in the design of responsive web pages.
CLO 3. Illustrate Models, Views and Templates with their connectivity in Django for full stack web
development.
CLO 4. Demonstrate the use of state management and admin interfaces automation in Django.
CLO 5. Design and implement Django apps containing dynamic pages with SQL databases.
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) does not mean only traditional lecture method, but different type of
teaching methods may be adopted to develop the outcomes.
2. Show Video/animation films to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes critical
thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
thinking skills such as the ability to evaluate, generalize, and analyze information rather than
simply recall it.
6. Topics will be introduced in a multiple representation.
7. Show the different ways to solve the same problem and encourage the students to come up
with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it helps
improve the students' understanding.
Module-1: MVC based Web Designing
Web framework, MVC Design Pattern, Django Evolution, Views, Mapping URL to Views, Working of
Django URL Confs and Loose Coupling, Errors in Django, Wild Card patterns in URLS.

Textbook 1: Chapter 1 and Chapter 3

Laboratory Component:
1. Installation of Python, Django and Visual Studio code editors can be demonstrated.
2. Creation of virtual environment, Django project and App should be demonstrated
3. Develop a Django app that displays current date and time in server
4. Develop a Django app that displays date and time four hours ahead and four hours before as
an offset of current date and time in server.
Teaching-Learning Process 1. Demonstration using Visual Studio Code
2. PPT/Prezi Presentation for Architecture and Design
Patterns
3. Live coding of all concepts with simple examples
Module-2: Django Templates and Models
Template System Basics, Using Django Template System, Basic Template Tags and Filters, MVT
Development Pattern, Template Loading, Template Inheritance, MVT Development Pattern.
 03092022

Configuring Databases, Defining and Implementing Models, Basic Data Access, Adding Model String
Representations, Inserting/Updating data, Selecting and deleting objects, Schema Evolution
Textbook 1: Chapter 4 and Chapter 5
Laboratory Component:
1. Develop a simple Django app that displays an unordered list of fruits and ordered list of
selected students for an event
2. Develop a layout.html with a suitable header (containing navigation menu) and footer with
copyright and developer information. Inherit this layout.html and create 3 additional pages:
contact us, About Us and Home page of any website.
3. Develop a Django app that performs student registration to a course. It should also display list
of students registered for any selected course. Create students and course as models with
enrolment as ManyToMany field.
Teaching-Learning Process 1. Demonstration using Visual Studio Code
2. PPT/Prezi Presentation for Architecture and Design
Patterns
3. Live coding of all concepts with simple examples
4. Case Study: Apply concepts learnt for an Online Ticket
Booking System
Module-3: Django Admin Interfaces and Model Forms
Activating Admin Interfaces, Using Admin Interfaces, Customizing Admin Interfaces, Reasons to use
Admin Interfaces.

Form Processing, Creating Feedback forms, Form submissions, custom validation, creating Model
Forms, URLConf Ticks, Including Other URLConfs.

Textbook 1: Chapters 6, 7 and 8

Laboratory Component:
1. For student and course models created in Lab experiment for Module2, register admin
interfaces, perform migrations and illustrate data entry through admin forms.
2. Develop a Model form for student that contains his topic chosen for project, languages used
and duration with a model called project.
Teaching-Learning Process 1. Demonstration using Visual Studio Code
2. PPT/Prezi Presentation for Architecture and Design
Patterns
3. Live coding of all concepts with simple examples
Module-4: Generic Views and Django State Persistence
Using Generic Views, Generic Views of Objects, Extending Generic Views of objects, Extending Generic
Views.

MIME Types, Generating Non-HTML contents like CSV and PDF, Syndication Feed Framework, Sitemap
framework, Cookies, Sessions, Users and Authentication.
Textbook 1: Chapters 9, 11 and 12
Laboratory Component:
1. For students enrolment developed in Module 2, create a generic class view which displays list
of students and detailview that displays student details for any selected student in the list.
2. Develop example Django app that performs CSV and PDF generation for any models created in
previous laboratory component.
Teaching-Learning Process 1. Demonstration using Visual Studio Code
2. PPT/Prezi Presentation for Architecture and Design
Patterns
 03092022

3. Live coding of all concepts with simple examples

4. Project Work: Implement all concepts learnt for Student
Admission Management.
Module-5: jQuery and AJAX Integration in Django
Ajax Solution, Java Script, XHTMLHttpRequest and Response, HTML, CSS, JSON, iFrames, Settings of
Java Script in Django, jQuery and Basic AJAX, jQuery AJAX Facilities, Using jQuery UI Autocomplete in
Django

Textbook 2: Chapters 1, 2 and 7.

Laboratory Component:
1. Develop a registration page for student enrolment as done in Module 2 but without page
refresh using AJAX.
2. Develop a search application in Django using AJAX that displays courses enrolled by a student
being searched.
Teaching-Learning Process 1. Demonstration using Visual Studio Code
2. PPT/Prezi Presentation for Architecture and Design
Patterns
3. Live coding of all concepts with simple examples
4. Case Study: Apply the use of AJAX and jQuery for
development of EMI calculator.
Course outcome (Course Skill Set)
At the end of the course the student will be able to:
CO 1. Understand the working of MVT based full stack web development with Django.
CO 2. Designing of Models and Forms for rapid development of web pages.
CO 3. Analyze the role of Template Inheritance and Generic views for developing full stack web
applications.
CO 4. Apply the Django framework libraries to render nonHTML contents like CSV and PDF.
CO 5. Perform jQuery based AJAX integration to Django Apps to build responsive full stack web
applications,

Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is
50%. The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student
shall be deemed to have satisfied the academic requirements and earned the credits allotted to each
subject/ course if the student secures not less than 35% (18 Marks out of 50) in the semester-end
examination (SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE
(Continuous Internal Evaluation) and SEE (Semester End Examination) taken together

Continuous Internal Evaluation:

Three Unit Tests each of 20 Marks (duration 01 hour)

1. First test at the end of 5th week of the semester

2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester

Two assignments each of 10 Marks

4. First assignment at the end of 4th week of the semester

5. Second assignment at the end of 9th week of the semester
 03092022

Practical Sessions need to be assessed by appropriate rubrics and viva-voce method. This will
contribute to 20 marks.

 Rubrics for each Experiment taken average for all Lab components – 15 Marks.
 Viva-Voce– 5 Marks (more emphasized on demonstration topics)

The sum of three tests, two assignments, and practical sessions will be out of 100 marks and will be
scaled down to 50 marks
(to have a less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).

CIE methods /question paper has to be designed to attain the different levels of Bloom’s
taxonomy as per the outcome defined for the course.

Semester End Examination:

Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)

1. The question paper will be set for 100 marks. The question paper will have ten questions. Each
question is set for 20 marks. Marks scored shall be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Adrian Holovaty, Jacob Kaplan Moss, The Definitive Guide to Django: Web Development Done
Right, Second Edition, Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Publishers, 2009
2. Jonathan Hayward, Django Java Script Integration: AJAX and jQuery, First Edition, Pack
Publishing, 2011
Reference Books
1. Aidas Bendroraitis, Jake Kronika, Django 3 Web Development Cookbook, Fourth Edition, Packt
Publishing, 2020
2. William Vincent, Django for Beginners: Build websites with Python and Django, First Edition,
Amazon Digital Services, 2018
3. Antonio Mele, Django3 by Example, 3rd Edition, Pack Publishers, 2020
4. Arun Ravindran, Django Design Patterns and Best Practices, 2nd Edition, Pack Publishers, 2020.
5. Julia Elman, Mark Lavin, Light weight Django, David A. Bell, 1 st Edition, Oreily Publications,
2014
Weblinks and Video Lectures (e-Resources):
1. MVT architecture with Django: https://freevideolectures.com/course/3700/django-tutorials
2. Using Python in Django: https://www.youtube.com/watch?v=2BqoLiMT3Ao
3. Model Forms with Django: https://www.youtube.com/watch?v=gMM1rtTwKxE
4. Real time Interactions in Django: https://www.youtube.com/watch?v=3gHmfoeZ45k
5. AJAX with Django for beginners: https://www.youtube.com/watch?v=3VaKNyjlxAU
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning
 03092022

1. Real world problem solving - applying the Django framework concepts and its integration with
AJAX to develop any shopping website with admin and user dashboards.

Short Preamble on Full Stack Web Development:

Website development is a way to make people aware of the services and/or products they are offering,
understand why the products are relevant and even necessary for them to buy or use, and highlight the
striking qualities that set it apart from competitors. Other than commercial reasons, a website is also needed
for quick and dynamic information delivery for any domain. Development of a well-designed, informative,
responsive and dynamic website is need of the hour from any computer science and related engineering
graduates. Hence, they need to be augmented with skills to use technology and framework which can help
them to develop elegant websites. Full Stack developers are in need by many companies, who knows and can
develop all pieces of web application (Front End, Back End and business logic). MVT based development with
Django is the cutting-edge framework for Full Stack Web Development. Python has become an easier
language to use for many applications. Django based framework in Python helps a web developer to utilize
framework and develop rapidly responsive and secure web applications.
 03092022

VI Semester

SOFTWARE TESTING
Course Code 21IS63 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives:

CLO 1. Explain different testing techniques.

CLO 2. Differentiate the various testing techniques.
CLO 3. Apply suitable technique for designing of flow graph.
CLO 4. Analyze the problem and derive suitable test cases.
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teacher can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) does not mean only traditional lecture method, but different type of
teaching methods may be adopted to develop the outcomes.
2. Show Video/animation films to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOTS (Higher order Thinking) questions in the class, which promotes critical
thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop thinking
skills such as the ability to evaluate, generalize, and analyze information rather than simply recall
it.
6. Topics will be introduced in a multiple representation.
7. Show the different ways to solve the same problem and encourage the students to come up with
their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it helps
improve the students' understanding.
Module-1
Basics of Software Testing: Humans, Errors and Testing, Software Quality, Requirements Behavior and
Correctness, Correctness versus Reliability, Testing and Debugging, Test Metrics, Testing and Verification,
Test-generation Strategies, Static Testing.

A Perspective on Testing: Definitions, Test Cases, Insights from Venn Diagram, Identifying Test Cases,
Error and fault taxonomies, Levels of testing.

Examples: Generalized pseudocode, the Triangle problem, the NextDate function, the Commission
problem, the SATM system, the Currency converter, Saturn windshield wiper

Textbook 1:Ch1,Ch2 Textbook 2:Ch. 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.8, 1.11, 1.12
Teaching-Learning Process Chalk and talk method/Project based Learning
Module-2
Functional Testing: Boundary Value Testing - Boundary value analysis, Robustness testing, Worst-case
testing, Special Value Testing, Examples, Random Testing, Guidelines.

Equivalence Class Testing - Equivalence classes, Equivalence test cases for the triangle problem,
NextDate function, and the commission problem, Guidelines and observations,

Decision Table Based Testing - Decision tables, Test cases for the triangle problem, NextDate function,
and the commission problem, Guidelines and observations.
 03092022

Textbook 1: Ch. 5, 6, 7
Teaching-Learning Process Chalk and talk method/Project based Learning
Module-3
Structural Testing: Overview, Statement testing, Program testing, Condition testing,

Path testing - DD paths, Test coverage metrics, Basis path testing, guidelines and observations,

Dataflow testing: Definition-Use testing, Slice-based testing, Guidelines and observations.

Textbook 1: Ch 9,10 Textbook 2:Ch. 6.2.1, 6.2.4

Teaching-Learning Process Chalk and talk method/Project based Learning
Module-4
Levels of Testing: Traditional view of testing levels, Alternative life-cycle models, The SATM system,
Separating integration and system testing.

Integration Testing: A closer look at the SATM system, Decomposition-based, call graph-based, Path-
based integrations.

Textbook 1: Ch. 12 & 13.1,13.2,13.3,13.4

Teaching-Learning Process Chalk and talk method/Project based Learning
Module-5
System Testing: Threads, Requirement Specification, Finding Threads, Structural strategies for thread
tesing, SATM test threads System testing guidelines, ASF testing example.

Interaction Testing: Context of interaction, A taxonomy of interactions, Interaction, composition, and

determinism, Client/Server Testing

Textbook 1: Ch 14,15
Teaching-Learning Process Chalk and talk method/Project based Learning
Course Outcomes:
At the end of the course students should be able to:

CO 1. Explain the significance of software testing and quality assurance in software development
CO 2. Apply the concepts of software testing to assess the most appropriate testing method.
CO 3. Analyze the importance of testing in software development.
CO 4. Evaluate the suitable testing model to derive test cases for any given software
CO 5. Develop appropriate document for the software artefact.

Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together.
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20 Marks
(duration 01 hours)
6. At the end of the 13th week of the semester
 03092022

The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(To have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module..

Suggested Learning Resources:

Textbooks:
1. Paul C. Jorgensen: Software Testing, A Craftsman‟s Approach, 3rd Edition, Auerbach Publications,
2008.
2. Aditya P Mathur: Foundations of Software Testing, Pearson Education, 2008.

Reference Books:
1. Mauro Pezze, Michal Young: Software Testing and Analysis – Process, Principles and Techniques,
Wiley India, 2009.
2. Software testing Principles and Practices – Gopalaswamy Ramesh, Srinivasan Desikan, 2 nd
Edition, Pearson, 2007.
3. Software Testing – Ron Patton, 2nd edition, Pearson Education, 2004.
4. The Craft of Software Testing – Brian Marrick, Pearson Education, 1995.
5. Anirban Basu, Software Quality Assurance, Testing and Metrics, PHI, 2015.
Web links and Video Lectures (e-Resources):
1. https://nptel.ac.in/courses/106/105/106105150/
2. https://onlinecourses.nptel.ac.in/noc19_cs71/preview
3. https://www.youtube.com/watch?v=OGImfxO2TEU&t=10s
4. https://www.youtube.com/watch?v=Q50ZyydS7pI
5. VTU e-Shikshana Program
6. VTU EDUSAT Program
Activity-Based Learning (Suggested Activities in Class)/ Practical Based learning
 Flip Class
 Seminar/Poster Presentation
 Role play/Team Demonstration/Collaborative Activity
 Mini Project
 Case study
 Learn by Doing
 03092022

VI Semester

DATA SCIENCE AND VISUALIZATION

Course Code 21CS644 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. To introduce data collection and pre-processing techniques for data science
CLO 2. Explore analytical methods for solving real life problems through data exploration
techniques
CLO 3. Illustrate different types of data and its visualization
CLO 4. Find different data visualization techniques and tools
CLO 5. Design and map element of visualization well to perceive information

Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Introduction to Data Science
Introduction: What is Data Science? Big Data and Data Science hype – and getting past the hype,
Why now? – Datafication, Current landscape of perspectives, Skill sets. Needed Statistical Inference:
Populations and samples, Statistical modelling, probability distributions, fitting a model.

Textbook 1: Chapter 1
Teaching-Learning Process 1. PPT – Recognizing different types of data, Data science
process
2. Demonstration of different steps, learning definition and
relation with data science

Module-2
Exploratory Data Analysis and the Data Science Process
Basic tools (plots, graphs and summary statistics) of EDA, Philosophy of EDA, The Data Science
Process, Case Study: Real Direct (online real estate firm). Three Basic Machine Learning Algorithms:
Linear Regression, k-Nearest Neighbours (k- NN), k-means.

Textbook 1: Chapter 2, Chapter 3

Teaching-Learning Process 1. PPT –Plots, Graphs, Summary Statistics
2. Demonstration of Machine Learning Algorithms
 03092022

Module-3
Feature Generation and Feature Selection
Extracting Meaning from Data: Motivating application: user (customer) retention. Feature
Generation (brainstorming, role of domain expertise, and place for imagination), Feature Selection
algorithms. Filters; Wrappers; Decision Trees; Random Forests. Recommendation Systems: Building
a User-Facing Data Product, Algorithmic ingredients of a Recommendation Engine, Dimensionality
Reduction, Singular Value Decomposition, Principal Component Analysis, Exercise: build your own
recommendation system.

Textbook 1: Chapter 6
Teaching-Learning Process 1. PPT – Feature generation, selection
2. Demonstration recommendation engine
Module-4
Data Visualization and Data Exploration

Introduction: Data Visualization, Importance of Data Visualization, Data Wrangling, Tools and Libraries
for Visualization

Comparison Plots: Line Chart, Bar Chart and Radar Chart; Relation Plots: Scatter Plot, Bubble Plot ,
Correlogram and Heatmap; Composition Plots: Pie Chart, Stacked Bar Chart, Stacked Area Chart, Venn
Diagram; Distribution Plots: Histogram, Density Plot, Box Plot, Violin Plot; Geo Plots: Dot Map,
Choropleth Map, Connection Map; What Makes a Good Visualization?

Textbook 2: Chapter 1, Chapter 2

Teaching-Learning Process 1. Demonstration of different data visualization tools.

Module-5
A Deep Dive into Matplotlib

Introduction, Overview of Plots in Matplotlib, Pyplot Basics: Creating Figures, Closing Figures, Format
Strings, Plotting, Plotting Using pandas DataFrames, Displaying Figures, Saving Figures; Basic Text and
Legend Functions: Labels, Titles, Text, Annotations, Legends; Basic Plots:Bar Chart, Pie Chart, Stacked
Bar Chart, Stacked Area Chart, Histogram, Box Plot, Scatter Plot, Bubble Plot; Layouts: Subplots, Tight
Layout, Radar Charts, GridSpec; Images: Basic Image Operations, Writing Mathematical Expressions

Textbook 2: Chapter 3

Teaching-Learning Process 1. PPT – Comparison of plots

2. Demonstration charts
Course Outcomes
At the end of the course the student will be able to:
CO 1. Understand the data in different forms
CO 2. Apply different techniques to Explore Data Analysis and the Data Science Process
CO 3. Analyze feature selection algorithms & design a recommender system.
CO 4. Evaluate data visualization tools and libraries and plot graphs.
CO 5. Develop different charts and include mathematical expressions.
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
 03092022

1. First test at the end of 5th week of the semester

2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Doing Data Science, Cathy O’Neil and Rachel Schutt, O'Reilly Media, Inc O'Reilly Media, Inc,
2013
2. Data Visualization workshop, Tim Grobmann and Mario Dobler, Packt Publishing, ISBN
9781800568112
Reference:
1. Mining of Massive Datasets, Anand Rajaraman and Jeffrey D. Ullman, Cambridge University
Press, 2010
2. Data Science from Scratch, Joel Grus, Shroff Publisher /O’Reilly Publisher Media
3. A handbook for data driven design by Andy krik
Weblinks and Video Lectures (e-Resources):

1. https://nptel.ac.in/courses/106/105/106105077/
2. https://www.oreilly.com/library/view/doing-data-science/9781449363871/toc01.html
3. http://book.visualisingdata.com/
4. https://matplotlib.org/
5. https://docs.python.org/3/tutorial/
6. https://www.tableau.com/

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

Demonstration using projects
 03092022

VI Semester

SOFTWARE TESTING LABORATORY

Course Code 21ISL66 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 0:0:2:0 SEE Marks 50
Total Hours of Pedagogy 24 Total Marks 100
Credits 1 Exam Hours 03
Course Objectives:

CLO 1. Explain the test cases for any given problem

CLO 2. Analyze the requirements for the given problem statement.
CLO 3. Design the solution and write test cases for the given problem.
CLO 4. Construct control flow graphs for the solution that is implemented.
CLO 5. Create appropriate document for the software artifact

Note: two hours tutorial is suggested for each laboratory sessions.

Prerequisite
● Students should be familiar with programming languages like C, C++, Java,
Python etc.
● Usage of IDEs like Eclipse, Netbeans and software testing tools should be
introduced

Sl. No. PART A – List of problems for which student should develop program and execute in
theLaboratory
Design, develop, code and run the program in any suitable language to solve the
1 commission problem. Analyze it from the perspective of boundary value testing, derive
different test cases, execute these test cases and discuss the test results.

Design, develop, code and run the program in any suitable language to implement the
NextDate function. Analyze it from the perspective of equivalence class value testing,
2
derive different test cases, execute these test cases and discuss the test results.

Design, develop, code and run the program in any suitable language to solve the
3 commission problem. Analyze it from the perspective of decision table-based testing,
derive different test cases, execute these test cases and discuss the test results.

Design and develop a program in a language of your choice to solve the triangle problem
defined as follows: Accept three integers which are supposed to be the three sides of a
triangle and determine if the three values represent an equilateral triangle, isosceles
4 triangle, scalene triangle, or they do not form a triangle at all. Assume that the upper limit
for the size of any side is 10. Derive test cases for your program based on boundary-value
analysis, equivalence class partitioning and decision-table approach and execute the
test
cases and discuss the results.
Design, develop, code and run the program in any suitable language to solve the
5 commission problem. Analyze it from the perspective of dataflow testing, derive different
test cases, execute these test cases and discuss the test results.
Design, develop, code and run the program in any suitable language to implement
6 the binary search algorithm. Determine the basis paths and using them derive different
test
cases, execute these test cases and discuss the test results.
PART B – Practical Based
Learning
Develop a Mini Project with documentation of suitable test-cases and their results to
01 perform automation testing of anyE-commerce or social media web page.
 03092022

Suggested Guidelines:
● Create a WebDriver session.
● Navigate to a Web page.
● Locate the web elements on the navigated page.
● Perform an actions on the located elements.
● Assert the performed actions did the correct thing.
● Report the results of the assertions.
● End the session.

Each inputs / data feeds (ex: website, username, password, mobile no, product name,
etc.,) must be provided through a file linked with code and neither to be entered manually
nor to be included in the code
Use any software testing tool like selenium, Katalon, etc.,

Course Outcome (Course Skill Set)

At the end of the course the student will be able to:

CO 1. List out the requirements for the given problem and develop test cases for any given
problem .
CO 2. Design and implement the solution for given problem and to design flow graph
CO 3. Use Eclipse/NetBeans IDE and testing tools to design, develop, debug the Project and create
appropriate document for the software artifact.
CO 4. Use the appropriate functional testing strategies. Compare the different testing techniques.
CO 5. Classify and Compare the problems according to a suitable testing model applying the test
coverage metrics.

Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is
50%. The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student
shall be deemed to have satisfied the academic requirements and earned the credits allotted to each
course. The student has to secure not less than 35% (18 Marks out of 50) in the semester-end
examination (SEE).

Continuous Internal Evaluation (CIE):

CIE marks for the practical course is 50 Marks.

The split-up of CIE marks for record/ journal and test are in the ratio 60:40.

 Each experiment to be evaluated for conduction with observation sheet and record write-up.
Rubrics for the evaluation of the journal/write-up for hardware/software experiments
designed by the faculty who is handling the laboratory session and is made known to students
at the beginning of the practical session.
 Record should contain all the specified experiments in the syllabus and each experiment
write-up will be evaluated for 10 marks.
 Total marks scored by the students are scaled downed to 30 marks (60% of maximum marks).
 Weightage to be given for neatness and submission of record/write-up on time.
 Department shall conduct 02 tests for 100 marks, the first test shall be conducted after the 8 th
week of the semester and the second test shall be conducted after the 14 th week of the
semester.
 In each test, test write-up, conduction of experiment, acceptable result, and procedural
knowledge will carry a weightage of 60% and the rest 40% for viva-voce.
 The suitable rubrics can be designed to evaluate each student’s performance and learning
ability. Rubrics suggested in Annexure-II of Regulation book
 03092022

 The average of 02 tests is scaled down to 20 marks (40% of the maximum marks).The Sum of
scaled-down marks scored in the report write-up/journal and average marks of two tests is
the total CIE marks scored by the student.

Semester End Evaluation (SEE):

 SEE marks for the practical course is 50 Marks.

 SEE shall be conducted jointly by the two examiners of the same institute, examiners are
appointed by the University
 All laboratory experiments are to be included for practical examination.
 (Rubrics) Breakup of marks and the instructions printed on the cover page of the answer script
to be strictly adhered to by the examiners. OR based on the course requirement evaluation
rubrics shall be decided jointly by examiners.
 Students can pick one question (experiment) from the questions lot prepared by the internal
/external examiners jointly.
 Evaluation of test write-up/ conduction procedure and result/viva will be conducted jointly by
examiners.
 General rubrics suggested for SEE are mentioned here, writeup-20%, Conduction procedure
and result in -60%, Viva-voce 20% of maximum marks. SEE for practical shall be evaluated for
100 marks and scored marks shall be scaled down to 50 marks (however, based on course
type, rubrics shall be decided by the examiners)
 Students can pick one experiment from the questions lot of PART A with equal choice to all the
students in a batch.
 PART B : Student should develop a mini project and it should be demonstrated in the laboratory
examination (with report and presentation).
 Weightage of marks for PART A is 60% and for PART B is 40%. General rubrics suggested to be
followed for part A and part B.
 Change of experiment is allowed only once (in part A) and marks allotted to the procedure part
to be made zero.
 The duration of SEE is 03 hours.

Suggested Learning Resources:

1. Paul C. Jorgensen: Software Testing, A Craftsman’s Approach, 3rd Edition, Auerbach
Publications, 2008.
2. Herbert Schildt, C:JavaThe Complete Reference,McGraw Hill,7thEdition

Web links and Video Lectures (e-Resources):

● https://www.javatpoint.com/selenium-tutorial
● References
● Introduction to Selenium - https://www.youtube.com/watch?v=FRn5J31eAMw
● Introduction to programming -https://www.youtube.com/watch?v=2Xa3Y4xz8_s
● Introduction to OOPS - https://www.youtube.com/watch?v=pBlH24tFRQk
● Introduction to Java - https://www.youtube.com/watch?v=mAtkPQO1FcA
● Eclipse for java - https://www.youtube.com/watch?v=8cm1x4bC610
 03092022

VII Semester

CRYPTOGRAPHY AND NETWORK SECURITY

Course Code 21IS71 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives:

CLO 1. To understand Cryptography, Network Security and its principles

CLO 2. To Analyse different Cryptography algorithms
CLO 3. To Illustrate Public and Private key cryptography
CLO 4. To Explain Key management, distribution and certification
CLO 5. To understand necessary Approaches and Techniques to build protection mechanisms in order
to secure computer networks.
Teaching-Learning Process (General Instructions)

These are sample Strategies; which teacher can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) needs not to be only traditional lecture method, but alternative effective
teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes critical
thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop design
thinking skills such as the ability to design, evaluate, generalize, and analyse information rather
than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different encryption techniques and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it helps
improve the students' understanding.
Module-1
Classical Encryption Techniques: Symmetric Cipher Model, Cryptography, Cryptanalysis and Brute-
Force Attack, Substitution Techniques, Caesar Cipher, Monoalphabetic Cipher, Playfair Cipher, Hill Cipher,
Polyalphabetic Cipher, One Time Pad.

Block Ciphers and the Data Encryption Standard: Traditional block Cipher structure, Stream Ciphers
and Block Ciphers, Motivation for the Feistel Cipher structure, the Feistel Cipher, The data encryption
standard, DES encryption, DES decryption, A DES example, results, the avalanche effect, the strength of
DES, the use of 56-Bit Keys, the nature of the DES algorithm, timing attacks, Block cipher design
principles, number of rounds, design of function F, key schedule algorithm

Textbook 1: Chapter 2, 3
Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
Public-Key Cryptography and RSA: Principles of public-key cryptosystems. Public-key cryptosystems.
Applications for public-key cryptosystems, requirements for public-key cryptosystems. public-key
cryptanalysis. The RSA algorithm, description of the algorithm, computational aspects, the security of
RSA.

Other Public-Key Cryptosystems: Diffie-Hellman key exchange, The algorithm, key exchange protocols,
man in the middle attack, Elgamal Cryptographic systems.

Textbook 1: Chapter 9, 10
Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
 03092022

Key Management and Distribution: Symmetric key distribution using Symmetric encryption, A key
distribution scenario, Hierarchical key control, session key lifetime, a transparent key control scheme,
Decentralized key control, controlling key usage, Symmetric key distribution using asymmetric
encryption, simple secret key distribution, secret key distribution with confidentiality and authentication,
A hybrid scheme, distribution of public keys, public announcement of public keys, publicly available
directory, public key authority, public keys certificates.

Textbook 1: Chapter 14.1 – 14.3

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration
Module-4
X-509 certificates. Certificates, X-509 version 3

Public key infrastructure.

User Authentication: Remote user Authentication principles, Mutual Authentication, one-way

authentication, remote user Authentication using Symmetric encryption, Mutual Authentication, one-way
Authentication,

Kerberos, Motivation, Kerberos version 4, Kerberos version 5, Remote user Authentication using
Asymmetric encryption, Mutual Authentication, one-way Authentication.

Textbook 1: Chapter 14.4 – 15.4

Teaching-Learning Process Chalk& board, Problem based learning
Module-5
Electronic Mail Security: Pretty good privacy, S/MIME,

IP Security: IP Security overview, IP Security policy, Encapsulating Security payload, Combining security
associations, Internet key exchange.

Textbook 1: Chapter 19.1, 19.2, 20.1 – 20.5

Teaching-Learning Process Chalk and board, Problem based learning
Course Outcomes
At the end of the course the student will be able to:

CO 1. Understand Cryptography, Network Security theories, algorithms and systems

CO 2. Apply different Cryptography and Network Security operations on different applications
CO 3. Analyse different methods for authentication and access control
CO 4. Evaluate Public and Private key, Key management, distribution and certification
CO 5. Design necessary techniques to build protection mechanisms to secure computer networks
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
 03092022

6. At the end of the 13th week of the semester

The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall be
proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. William Stallings: Cryptography and Network Security, Pearson 6th edition.

Reference:
1. V. K Pachghare: Cryptography and Information Security, PHI 2nd Edition
2. BehrouzA.Foruzan, Cryptography and Network Security, Tata McGraw Hill 2007.
Web links and Video Lectures (e-Resources):

 https://nptel.ac.in/courses/106105031
 https://onlinecourses.nptel.ac.in/noc21_cs16
 https://www.digimat.in/nptel/courses/video/106105031
 https://www.youtube.com/watch?v=DEqjC0G5KwU
 https://www.youtube.com/watch?v=FqQ7TWvOaus
 https://www.youtube.com/watch?v=PHsa_Ddgx6w

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning:

Project based learning:
 Implement classical, symmetric and asymmetric algorithms in any preferred language
 Evaluate network security protocol using any simulator available
 Conduct a comprehensive literature survey on the protocols and algorithms
 Identify the security threats and models of security threats
 Implement factorization algorithms and evaluate their complexity, identify a technologies to
factorize a large prime number.
 03092022

VII Semester

CLOUD COMPUTING
Course Code 21CS72 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 2:0:0:0 SEE Marks 50
Total Hours of Pedagogy 24 Total Marks 100
Credits 02 Exam Hours 03
Course Learning Objectives:

CLO 1. Introduce the rationale behind the cloud computing revolution and the business drivers
CLO 2. Introduce various models of cloud computing
CLO 3. Introduction on how to design cloud native applications, the necessary tools and the design
tradeoffs.
CLO 4. Realize the importance of Cloud Virtualization, Abstraction`s and Enabling Technologies and
cloud security
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) does not mean only traditional lecture method, but different type of
teaching methods may be adopted to develop the outcomes.
2. Show Video/animation films to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes critical
thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop thinking
skills such as the ability to evaluate, generalize, and analyze information rather than simply recall
it.
6. Topics will be introduced in a multiple representation.
7. Show the different ways to solve the same problem and encourage the students to come up with
their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it helps
improve the students' understanding.
Module-1
Introduction:
Introduction ,Cloud Computing at a Glance, Historical Developments, Building Cloud Computing
Environments, Amazon Web Services (AWS), Google AppEngine, Microsoft Azure, Hadoop, Force.com
and Salesforce.com, Manjrasoft Aneka

Textbook 1: Chapter 1: 1.1,1.2 and 1.3

Teaching-Learning Process Chalk and board, Active Learning

Module-2
Virtualization: Introduction, Characteristics of Virtualized, Environments Taxonomy of
Virtualization Techniques, Execution Virtualization, Other Types of Virtualization,
Virtualization and Cloud Computing, Pros and Cons of Virtualization, Technology Examples

Textbook 1 : Chapter 3: 3.1 to 3.6

Teaching-Learning Process Chalk and board, Active Learning
Module-3
Cloud Computing Architecture: Introduction, Cloud Reference Model, Types of Clouds, Economics of
the Cloud, Open Challenges

Textbook 1: Chapter 4: 4.1 to 4.5

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Teaching-Learning Process Chalk and board, Demonstration

Module-4
Cloud Security: Risks, Top concern for cloud users, privacy impact assessment, trust, OS security, VM
Security, Security Risks posed by shared images and management OS.

Textbook 2: Chapter 9: 9.1 to 9.6, 9.8, 9.9

Teaching-Learning Process Chalk and board

Module-5
Cloud Platforms in Industry
Amazon web services: - Compute services, Storage services, Communication services, Additional
services. Google AppEngine: - Architecture and core concepts, Application life cycle, Cost model,
Observations.

Textbook 1: Chapter 9: 9.1 to 9.2

Cloud Applications:
Scientific applications: - HealthCare: ECG analysis in the cloud, Biology: gene expression data analysis for
cancer diagnosis, Geoscience: satellite image processing. Business and consumer applications: CRM and
ERP, Social networking, media applications.

Textbook 1: Chapter 10: 10.1 to 10.2

Teaching-Learning Process Chalk and board

Course outcome (Course Skill Set)

At the end of the course the student will be able to:
CO 1. Understand and analyze various cloud computing platforms and service provider.
CO 2. Illustrate various virtualization concepts.
CO 3. Identify the architecture, infrastructure and delivery models of cloud computing.
CO 4. Understand the Security aspects of CLOUD.
CO 5. Define platforms for development of cloud applications
Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together

Continuous Internal Evaluation:

Three Unit Tests each of 20 Marks (duration 01 hour)

1. First test at the end of 5th week of the semester

2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks

4. First assignment at the end of 4th week of the semester

5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
 03092022

6. At the end of the 13th week of the semester

The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks

(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).

CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.

Semester End Examination:

Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)

1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:

Textbooks
1. Rajkumar Buyya, Christian Vecchiola, and Thamrai Selvi Mastering Cloud Computing McGraw Hill
Education.
2. Dan C. Marinescu, Cloud Compting Theory and Practice, Morgan Kaufmann, Elsevier 2013

Reference Books
1. Toby Velte, Anthony Velte, Cloud Computing: A Practical Approach, McGraw-Hill Osborne Media.
2. George Reese, Cloud Application Architectures: Building Applications and Infrastructure in the
Cloud, O'Reilly Publication.
3. John Rhoton, Cloud Computing Explained: Implementation Handbook for Enterprises, Recursive
Press.
Weblinks and Video Lectures (e-Resources):
 https://www.youtube.com/watch?v=1N3oqYhzHv4
 https://www.youtube.com/watch?v=RWgW-CgdIk0

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

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VII Semester

OBJECT ORIENTED MODELING AND DESIGN

Course Code 21CS731 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. Describe the concepts involved in Object-Oriented modelling and their benefits.
CLO 2. Demonstrate concept of use-case model, sequence model and state chart model for a given
problem.
CLO 3. Explain the facets of the unified process approach to design and build a Software system.
CLO 4. Translate the requirements into implementation for Object Oriented design.
CLO 5. Choose an appropriate design pattern to facilitate development procedure.

Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Advanced object and class concepts; Association ends; N-ary associations; Aggregation; Abstract classes;
Multiple inheritance; Metadata; Reification; Constraints; Derived Data; Packages. State Modeling: Events,
States, Transistions and Conditions, State Diagrams, State diagram behaviour.

Textbook-1: 4, 5

Teaching-Learning Process Chalk and board, Demonstration

Module-2
UseCase Modelling and Detailed Requirements: Overview; Detailed object-oriented Requirements
definitions; System Processes-A use case/Scenario view; Identifying Input and outputs-The System
sequence diagram; Identifying Object Behaviour-The state chart Diagram; Integrated Object-oriented
Models.

Textbook-2:Chapter- 6:Page 210 to 250

Teaching-Learning Process Chalk and board, Demonstration

Module-3
Process Overview, System Conception and Domain Analysis: Process Overview: Development stages;
Development life Cycle; System Conception: Devising a system concept; elaborating a concept; preparing
a problem statement. Domain Analysis: Overview of analysis; Domain Class model: Domain state model;
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Domain interaction model; Iterating the analysis.

Textbook-1:Chapter- 10,11,and 12
Teaching-Learning Process Chalk and board, Demonstration

Module-4
Use case Realization :The Design Discipline within up iterations: Object Oriented Design-The Bridge
between Requirements and Implementation; Design Classes and Design within Class Diagrams;
Interaction Diagrams-Realizing Use Case and defining methods; Designing with Communication
Diagrams; Updating the Design Class Diagram; Package Diagrams-Structuring the Major Components;
Implementation Issues for Three-Layer Design.
Textbook-2: Chapter 8: page 292 to 346

Teaching-Learning Process Chalk and board, Demonstration

Module-5
Design Patterns: Introduction; what is a design pattern?, Describing design patterns, the catalogue of
design patterns, Organizing the catalogue, How design patterns solve design problems, how to select a
design patterns, how to use a design pattern; Creational patterns: prototype and singleton (only);
structural patterns adaptor and proxy (only).
Textbook-3: Ch-1: 1.1, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8,Ch-3,Ch-4.

Teaching-Learning Process Chalk and board, Demonstration

Course Outcomes
At the end of the course the student will be able to:
CO 1. Describe the concepts of object-oriented and basic class modelling.
CO 2. Draw class diagrams, sequence diagrams and interaction diagrams to solve problems.
CO 3. Choose and apply a befitting design pattern for the given problem.
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
 03092022

papers for the subject (duration 03 hours)

1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Michael Blaha, James Rumbaugh: Object Oriented Modelling and Design with UML,2 nd Edition,
Pearson Education,2005
2. Satzinger, Jackson and Burd: Object-Oriented Analysis & Design with the Unified Process,
Cengage Learning, 2005.
3. Erich Gamma, Richard Helm, Ralph Johnson and john Vlissides: Design Patterns –Elements of
Reusable Object-Oriented Software, Pearson Education,2007.
Reference:
1. Grady Booch et. al.: Object-Oriented Analysis and Design with Applications,3rd Edition,Pearson
Education,2007.
2. Frank Buschmann, RegineMeunier, Hans Rohnert, Peter Sommerlad, Michel Stal: Pattern –
Oriented Software Architecture. A system of patterns , Volume 1, John Wiley and Sons.2007.
3. Booch, Jacobson, Rambaugh : Object-Oriented Analysis and Design with Applications, 3rd
edition, pearson, Reprint 2013
Weblinks and Video Lectures (e-Resources):

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

 03092022

VII Semester

DIGITAL IMAGE PROCESSING

Course Code 21CS732 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1.
Understand the fundamentals of digital image processing
CLO 2.
Explain the image transform techniques used in digital image processing
CLO 3.
Apply different image enhancement techniques on digital images
CLO 4.
Evaluate image restoration techniques and methods used in digital imageprocessing
CLO 5.
Understand the Morphological Operations and Segmentation used in digital
imageprocessing
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Digital Image Fundamentals: What is Digital Image Processing? Originsof Digital Image Processing,
Examples of fields that use DIP, FundamentalSteps in Digital Image Processing, Components of an Image
ProcessingSystem, Elements of Visual Perception, Image Sensing and Acquisition, Image Sampling and
Quantization, Some Basic Relationships BetweenPixels, Linear and Nonlinear Operations.

Textbook 1: Chapter 1 and Chapter 2: Sections 2.1 to 2.5, 2.6.2

Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
Spatial Domain: Some Basic Intensity Transformation Functions, Histogram Processing, Fundamentals of
Spatial Filtering, SmoothingSpatial Filters, Sharpening Spatial Filters
Frequency Domain: Preliminary Concepts, The Discrete FourierTransform (DFT) of Two Variables,
Properties of the 2-D DFT, Filtering inthe Frequency Domain, Image Smoothing and Image Sharpening
UsingFrequency Domain Filters, Selective Filtering.
Textbook 1: Chapter 3: Sections 3.2 to 3.6 and Chapter 4: Sections 4.2, 4.5 to 4.10
Teaching-Learning Process 1. Chalk and board, Active Learning, Demonstration
2. Laboratory Demonstration
Module-3
Restoration: Noise models, Restoration in the Presence of Noise Onlyusing Spatial Filtering and
 03092022

Frequency Domain Filtering, Linear, Position-Invariant Degradations, Estimating the Degradation

Function, InverseFiltering, Minimum Mean Square Error (Wiener) Filtering, ConstrainedLeast Squares
Filtering.

Textbook 1: Chapter 5: Sections 5.2, to 5.9

Teaching-Learning Process 1. Chalk and board
Module-4
Color Image Processing: Color Fundamentals, Color Models, Pseudo color Image Processing. Wavelets:
Background, Multiresolution Expansions.

Morphological Image Processing: Preliminaries, Erosion and Dilation, Opening and Closing, The Hit-or-
Miss Transforms, Some Basic Morphological Algorithms.

Text: Chapter 6: Sections 6.1 to 6.3, Chapter 7: Sections 7.1 and 7.2, Chapter 9: Sections 9.1 to 9.5
Teaching-Learning Process 1.Chalk& board
2.Demonstartion of Case study /Application for wavelet transfer
method
Module-5
Segmentation: Introduction, classification of image segmentation algorithms, Detection of
Discontinuities, Edge Detection, Hough Transforms and Shape Detection, Corner Detection, Principles of
Thresholding.
Representation and Description: Representation, Boundary descriptors.
Text2: Chapter 9: Sections 9.1, to 9.7 and Text 1: Chapter 11: Sections 11.1and 11.2
Teaching-Learning Process 1.Chalk and board, MOOC.
2. Poster making activity for various image segmentation
algorithms
Course Outcomes
At the end of the course the student will be able to:
CO 1. Understand the fundamentals of Digital Image Processing.
CO 2. Apply different Image transformation techniques
CO 3. Analyze various image restoration techniques
CO 4. Understand colour image and morphological processing
CO 5. Design image analysis and segmentation techniques
Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together

Continuous Internal Evaluation:

Three Unit Tests each of 20 Marks (duration 01 hour)

1. First test at the end of 5th week of the semester

2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks

4. First assignment at the end of 4th week of the semester

5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
 03092022

Marks (duration 01 hours)

6. At the end of the 13th week of the semester

The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks

(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).

CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.

Semester End Examination:

Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)

1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Textbooks
3. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Third Ed., Prentice Hall, 2008.
4. S. Sridhar, Digital Image Processing, Oxford University Press, 2 ndEdition, 2016

Reference:
1. Digital Image Processing- S.Jayaraman, S.Esakkirajan, T.Veerakumar, TataMcGraw Hill 2014.
2. Fundamentals of Digital Image Processing-A. K. Jain, Pearson 2004
Weblinks and Video Lectures (e-Resources):
1. https://https://nptel.ac.in/courses/106/105/106105032/
2. https://github.com/PrajwalPrabhuiisc/Image-processing-assignments

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

Demonstration of finding the histogram from grayscale image, to check the low pass filter properties,
filtering the images using Gaussian low pass filter, etc… using Python programming

Practical Based Assignment like following or any topic which is in-line with the course requirement.
Students shall present and demonstrate their work at the end of semester.

 Program to show rotation, scaling, and translation of an image.

 Read an image and extract and display low-level features such as edges, textures using filtering
techniques
 Demonstrate enhancing and segmenting low contrast 2D images.
 To Read an image, first apply erosion to the image and then subtract the result from the
original.
 03092022

VII Semester

USER INTERFACE DESIGN

Course Code 21IS733 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives:

CLO 1. To study the concept of menus, windows, interfaces.

CLO 2. To study about business functions.
CLO 3. To study the characteristics and components of windows and the various controls for the
windows.
CLO 4. To study about various problems in windows design with color, text, graphics and
CLO 5. To study the testing methods.

Teaching-Learning Process (General Instructions)

These are sample Strategies, which teacher can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) needs not to be only traditional lecture method, but alternative effective
teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes critical
thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop design
thinking skills such as the ability to design, evaluate, generalize, and analyse information rather
than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and encourage
the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it helps
improve the students' understanding.
Module-1
The User Interface-Introduction, Overview, The importance of user interface Defining the user interface,
The importance of Good design, Characteristics of graphical and web user interfaces, Principles of user
interface design.

Textbook 1: Ch. 1,2

Teaching-Learning Process Chalk and board, Demonstration, MOOC
Module-2
The User Interface Design process- Obstacles, Usability, Human characteristics in Design, Human
Interaction speeds, Business functions-Business definition and requirement analysis, Basic business
functions, Design standards.

Textbook 1: Part-2
Teaching-Learning Process Chalk and board, Active Learning
Module-3
System menus and navigation schemes- Structures of menus, Functions of menus, Contents of menus,
Formatting of menus, Phrasing the menu, Selecting menu choices, Navigating menus, Kinds of graphical
 03092022

menus.

Textbook 1: Part-2
Teaching-Learning Process Chalk and board, Demonstration
Module-4
Windows - Characteristics, Components of window, Window presentation styles, Types of window,
Window management, Organizing window functions, Window operations, Web systems, Characteristics
of device based controls.

Textbook 1: Part-2
Teaching-Learning Process Chalk& board, Problem based learning, Demonstration
Module-5
Screen based controls- Operable control, Text control, Selection control, Custom control, Presentation
control, Windows Tests-prototypes, kinds of tests.

Textbook 1: Part-2
Teaching-Learning Process Chalk and board, Demonstration, MOOC
Course Outcomes:

At the end of the course the student will be able to:

CO 1. Understand importance and characteristics of user interface design
CO 2. Apply user interface design process on business functions
CO 3. Demonstrate system menus, navigation schemes and windows characteristics
CO 4. Analyze screen based controls and device based controls
CO 5. Design the prototypes and test plans of user interface

Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together

Continuous Internal Evaluation:

Three Unit Tests each of 20 Marks (duration 01 hour)

7. First test at the end of 5th week of the semester

8. Second test at the end of the 10th week of the semester
9. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks

10. First assignment at the end of 4th week of the semester

11. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)

12. At the end of the 13th week of the semester

The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks

(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
 03092022

methods of the CIE. Each method of CIE should have a different syllabus portion of the course).

CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.

Semester End Examination:

Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)

3. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
4. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks:
1. Wilbert O, Galitz, “The Essential Guide to User Interface Design”, John Wiley & Sons, Second
Edition 2002
Reference Books:
1. Ben Sheiderman, “Design the User Interface”, Pearson Education, 1998
2. Alan Cooper, “ The Essential of User Interface Design”, Wiley-Dream Tech Ltd.,2002

Web links and Video Lectures (e-Resources):

1. https://nptel.ac.in/noc/courses/noc19/SEM1/noc19-ar10/
2. https://www.vtupulse.com/cbcs-cse-notes/17cs832-user-interface-design-uid-notes/
3. https://www.brainkart.com/subject/User-Interface-Design_145/
4. https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-831-user-
interface-design-and-implementation-spring-2011/lecture-notes/
5. https://lecturenotes.in/download/material/21405-user-interface-design

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

 03092022

VII Semester

BLOCKCHAIN TECHNOLOGY
Course Code 21CS734 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. Explain the fundamentals of distributed computing and blockchain

CLO 2. Discuss the concepts in bitcoin
CLO 3. Demonstrate Ethereum platform
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Blockchain 101: Distributed systems, History of blockchain, Introduction to blockchain, Types of
blockchain, CAP theorem and blockchain, Benefits and limitations of blockchain.

Decentralization and Cryptography: Decentralization using blockchain, Methods of decentralization,

Routes to decentralization, Decentralized organizations.

Textbook 1: Chapter 1, 2
Teaching-Learning Process Chalk and board, Active Learning – Oral presentations.
Module-2
Introduction to Cryptography & Cryptocurrencies: Cryptographic Hash Functions, Hash Pointers and
Data Structures, Digital Signatures, Public Keys as Identities, A Simple Cryptocurrency,

How Bitcoin Achieves Decentralization: Distributed consensus, Consensus without identity using a
block chain, Incentives and proof of work, Putting it all together,

Textbook 2: Chapter 1, 2
Teaching-Learning Process Chalk and board, Demonstration
Module-3
Mechanics of Bitcoin: Bitcoin transactions, Bitcoin Scripts, Applications of Bitcoin scripts, Bitcoin blocks,
The Bitcoin network, Limitations and improvements

How to Store and Use Bitcoins: Simple Local Storage, Hot and Cold Storage, Splitting and Sharing Keys,
 03092022

Online Wallets and Exchanges, Payment Services, Transaction Fees, Currency Exchange Markets

Textbook2: Chapter 3,4

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration, MOOC
Module-4
Bitcoin Mining: The task of Bitcoin miners, Mining Hardware, Energy consumption and ecology, Mining
pools, Mining incentives and strategies,

Bitcoin and Anonymity: Anonymity Basics, How to De-anonymize Bitcoin, Mixing, Decentralized Mixing,
Zerocoin and Zerocash,

Textbook2: Chapter 5,6

Teaching-Learning Process Chalk& board, Problem based learning, MOOC
Module-5
Smart Contracts and Ethereum 101:
Smart Contracts: Definition, Ricardian contracts.

Ethereum 101: Introduction, Ethereum blockchain, Elements of the Ethereum blockchain, Precompiled
contracts.

Textbook 1: Chapter 10
Teaching-Learning Process Chalk and board, MOOC, Practical Demonstration
Course Outcomes
At the end of the course the student will be able to:
CO 1. Describe the concepts of Distributed computing and its role in Blockchain
CO 2. Describe the concepts of Cryptography and its role in Blockchain
CO 3. List the benefits, drawbacks and applications of Blockchain
CO 4. Appreciate the technologies involved in Bitcoin
CO 5. Appreciate and demonstrate the Ethereum platform to develop blockchain application.
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
 03092022

as per the outcome defined for the course.

Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Mastering Blockchain - Distributed ledgers, decentralization and smart contracts explained,
Imran Bashir, Packt Publishing Ltd, Second Edition, ISBN 978-1-78712-544-5, 2017.
2. Arvind Narayanan, Joseph Bonneau, Edward W. Felten, Andrew Miller, Steven Goldfeder and
Jeremy Clark., Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction.
Princeton University Press, 2016.
Reference:
1. Mastering Bitcoins: Unlocking Digital Cryptocurrencies by Andreas Antonopoulos. O’Reilly Media,
Inc, 2013.
Weblinks and Video Lectures (e-Resources):

1. http://bitcoinbook.cs.princeton.edu/?_ga=2.8302578.1344744326.1642688462-
86383721.1642688462
2. https://nptel.ac.in/courses/106/105/106105184/
3. https://ethereum.org/en/developers/
4. https://developer.ibm.com/components/hyperledger-fabric/tutorials/
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning
 03092022

VII Semester

INTERNET OF THINGS
Course Code 21CS735 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. Understand about the fundamentals of Internet of Things and its building blocks along with
their characteristics.
CLO 2. Understand the recent application domains of IoT in everyday life.
CLO 3. Understand the protocols and standards designed for IoT and the current research on it.
CLO 4. Understand the other associated technologies like cloud and fog computing in the domain of
IoT.
CLO 5. Improve their knowledge about the various cutting-edge technologies in the field IoT and
machine learning applications.
CLO 6. Gain insights about the current trends of machine learning and AI techniques used in IoT to
orient towards the present industrial scenario.
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Emergence of IoT: Introduction, Evolution of IoT, Enabling IoT and the Complex Interdependence of
Technologies, IoT Networking Components, Addressing Strategies in IoT.

Textbook 1: Chapter 4 – 4.1 to 4.5

Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
IoT Sensing and Actuation: Introduction, Sensors, Sensor Characteristics, Sensorial Deviations, Sensing
Types, Sensing Considerations, Actuators, Actuator Types, Actuator Characteristics.

Textbook 1: Chapter 5 – 5.1 to 5.9

Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
IoT Processing Topologies and Types: Data Format, Importance of Processing in IoT, Processing
Topologies, IoT Device Design and Selection Considerations, Processing Offloading.
 03092022

Textbook 1: Chapter 6 – 6.1 to 6.5

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration
Module-4
IoT Connectivity Technologies: Introduction, IEEE 802.15.4, Zigbee, Thread, ISA100.11A,
WirelessHART, RFID, NFC, DASH7, Z-Wave, Weightless, Sigfox, LoRa, NB-IoT, Wi-Fi, Bluetooth

Textbook 1: Chapter 7 – 7.1 to 7.16

Teaching-Learning Process Chalk & board, Problem based learning
Module-5
IoT Communication Technologies: Introduction, Infrastructure Protocols, Discovery Protocols, Data
Protocols, Identification Protocols, Device Management, Semantic Protocols

IoT Interoperability: Introduction, Taxonomy of interoperability, Standards, Frameworks

Textbook 1: Chapter 8 – 8.1, 6.2, 8.3, 8.4, 8.5, 8.6, .7

Textbook 1: Chapter 9 – 9.1, 9.2, 9.3
Teaching-Learning Process Chalk and board, MOOC
Course Outcomes
At the end of the course the student will be able to:
CO 1. Understand the evolution of IoT, IoT networking components, and addressing strategies in IoT.
CO 2. Analyze various sensing devices and actuator types.
CO 3. Demonstrate the processing in IoT.
CO 4. Apply different connectivity technologies.
CO 5. Understand the communication technologies , protocols and interoperability in IoT.
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
6. At the end of the 13th week of the semester- Group discussion/Seminar/quiz any one of three
suitably planned to attain the COs and POs for 20 Marks (duration 01 hours)
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall be
 03092022

proportionally reduced to 50 marks

2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Sudip Misra, Anandarup Mukherjee, Arijit Roy, “Introduction to IoT”, Cambridge University Press
2021.

Reference:
1. S. Misra, C. Roy, and A. Mukherjee, 2020. Introduction to Industrial Internet of Things and Industry
4.0. CRC Press.
2. Vijay Madisetti and Arshdeep Bahga, “Internet of Things (A Hands-on-Approach)”,1st Edition, VPT,
2014.
3. Francis daCosta, “Rethinking the Internet of Things: A Scalable Approach to Connecting Everything”,
1st Edition, Apress Publications, 2013.
Weblinks and Video Lectures (e-Resources):

1. https://nptel.ac.in/noc/courses/noc19/SEM1/noc19-cs31/
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning
 03092022

VII Semester

SOFTWARE ARCHITECTURE AND DESIGN PATTERNS

Course Code 21CS741 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. Learn How to add functionality to designs while minimizing complexity.

CLO 2. What code qualities are required to maintain to keep code flexible?
CLO 3. To Understand the common design patterns.
CLO 4. To explore the appropriate patterns for design problems
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
9. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
10. Use of Video/Animation to explain functioning of various concepts.
11. Encourage collaborative (Group Learning) Learning in the class.
12. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
13. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
14. Introduce Topics in manifold representations.
15. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
16. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Introduction: what is a design pattern? describing design patterns, the catalog of design pattern,
organizing the catalog, how design patterns solve design problems, how to select a design pattern, how
to use a design pattern. A Notation for Describing Object-Oriented Systems

Textbook 1: Chapter 1 and 2.7

Analysis a System: overview of the analysis phase, stage 1: gathering the requirements functional
requirements specification, defining conceptual classes and relationships, using the
knowledge of the domain. Design and Implementation, discussions and further reading.

Textbook 1: Chapter 6

Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
Design Pattern Catalog: Structural patterns, Adapter, bridge, composite, decorator, facade,
flyweight, proxy.

Textbook 2: chapter 4

Teaching-Learning Process Chalk and board, Active Learning, Demonstration

Module-3
BehavioralPatterns: Chain of Responsibility, Command, Interpreter, Iterator, Mediator, Memento,
Observer, State, Template Method
 03092022

Textbook 2: chapter 5

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration

Module-4
Interactive systems and the MVC architecture: Introduction, The MVC architectural pattern,
analyzing a simple drawing program, designing the system, designing of the subsystems, getting into
implementation, implementing undo operation, drawing incompleteitems, adding a new feature,
pattern-based solutions.

Textbook 1: Chapter 11

Teaching-Learning Process Chalk & board, Problem based learning

Module-5
Designing with Distributed Objects: Client server system, java remote method invocation,
implementing an object-oriented system on the web (discussions and further reading) a note
on input and output, selection statements, loops arrays.

Textbook 1: Chapter 12

Teaching-Learning Process Chalk and board

Course Outcomes
At the end of the course the student will be able to:
CO 1. Design and implement codes with higher performance and lower complexity
CO 2. Be aware of code qualities needed to keep code flexible
CO 3. Experience core design principles and be able to assess the quality of a design with
respect to these principles.
CO 4. Capable of applying these principles in the design of object oriented systems.
CO 5. Demonstrate an understanding of a range of design patterns. Be capable of
comprehending a design presented using this vocabulary.
CO 6. Be able to select and apply suitable patterns in specific contexts
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
6. At the end of the 13th week of the semester- Group discussion/Seminar/quiz any one of three
suitably planned to attain the COs and POs for 20 Marks (duration 01 hours)
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
 03092022

Semester End Examination:

Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks

1. Brahma Dathan, Sarnath Rammath, Object-oriented analysis, design and

implementation, Universities Press,2013
2. Erich Gamma, Richard Helan, Ralph Johman, John Vlissides , Design Patterns, Pearson
Publication,2013.

Reference:
1. Frank Bachmann, RegineMeunier, Hans Rohnert “Pattern Oriented Software
Architecture” –Volume 1, 1996.
2. William J Brown et al., "Anti-Patterns: Refactoring Software, Architectures and Projects in Crisis",
John Wiley, 1998.
Weblinks and Video Lectures (e-Resources):

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

 03092022

VII Semester

FILE STRUCTURES
Course Code 21IS742 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives
CLO 1. Provide an introduction to the fundamental file operations and storage systems.
CLO 2. Introducing fundamental concepts of file structure.
CLO 3. Introducing the most important high-level file structures tools which include indexing, co
sequential processing, B trees, Hashing.
CLO 4. Applying the techniques in the design of C++ programs for solving various file management
problems.
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative effective
teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes critical
thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop design
thinking skills such as the ability to design, evaluate, generalize, and analyze information rather
than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and encourage
the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it helps
improve the students' understanding.
Module-1
Introduction: File Structures: The Heart of the file structure Design, A Short History of File Structure
Design, A Conceptual Toolkit; Fundamental File Operations: Physical Files and Logical Files, Opening
Files, Closing Files, Reading and Writing, Seeking, Special Characters, The Unix Directory Structure,
Physical devices and Logical Files, File-related Header Files, UNIX file System Commands; Secondary
Storage and System Software: Disks

Fundamental File Structure Concepts, Managing Files of Records: Field and Record Organization,
Using Classes to Manipulate Buffers, Using Inheritance for Record Buffer Classes, Managing Fixed Length,
Fixed Field Buffers, An Object-Oriented Class for Record Files, Record Access, more about Record
Structures, Encapsulating Record Operations in a Single Class, File Access and File Organization

Text book 1: Chapter 1, Chapter 2, Chapter 3 (3.1, 3.7 - 3.10) Chapter 4, Chapter 5 (5.1-5.4)
Teaching-Learning Process Chalk and board, Active Learning, Problem based learning

Module-2
Organization of Files for Performance, Indexing: Data Compression, Reclaiming Space in files, Internal
Sorting and Binary Searching, Key sorting; What is an Index? A Simple Index for Entry-Sequenced File,
Using Template Classes in C++ for Object I/O, Object-Oriented support for Indexed, Entry-Sequenced Files
of Data Objects, Indexes that are too large to hold in Memory, Indexing to provide access by Multiple keys,
Retrieval Using Combinations of Secondary Keys, Improving the Secondary Index structure: Inverted
 03092022

Lists, Selective indexes, Binding.

Text book 1: Chapter 6, Chapter 7

Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
Co-sequential Processing and the Sorting of Large Files: A Model for Implementing Co-Sequential
Processes, Application of the Model to a General Ledger Program, Extension of the Model to include
Multiway Merging, A Second Look at Sorting in Memory, Merging as a Way of Sorting Large Files on Disk.
Multi-Level Indexing and B-Trees: The invention of B-Tree, Statement of the problem, Indexing with
Binary Search Trees; Multi-Level Indexing

Text book 1: Chapter 8 – 8.1 to 8.5.4, Chapter 9 – 9.1 – 9.4

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration
Module-4
Multi-Level Indexing and B-Trees: B-Trees, Example of Creating a B-Tree, An Object-Oriented
Representation of B-Trees, B-Tree Methods; Nomenclature, Formal Definition of B-Tree Properties,
Worst-case Search Depth, Deletion, Merging and Redistribution, Redistribution during insertion; B*
Trees, Buffering of pages; Virtual B-Trees; Variable-length Records and keys.

Indexed Sequential File Access and Prefix B + Trees: Indexed Sequential Access, maintaining a
Sequence Set, adding a Simple Index to the Sequence Set, The Content of the Index: Separators Instead of
Keys, The Simple Prefix B+ Tree and its maintenance, Index Set Block Size, Internal Structure of Index Set
Blocks: A Variable-order B- Tree, Loading a Simple Prefix B+ Trees, B-Trees, B+ Trees and Simple Prefix
B+ Trees in Perspective.

Text book 1: Chapter 8 - 9.5 - 9.16, Chapter 10.

Teaching-Learning Process Chalk and board, Problem based learning
Module-5
Hashing: Introduction, A Simple Hashing Algorithm, Hashing Functions and Record Distribution, how
much Extra Memory should be used? Collision resolution by progressive overflow, Buckets, Making
deletions, Other collision resolution techniques, Patterns of record access.
Extendible Hashing: How Extendible Hashing Works, Implementation, Deletion, Extendible Hashing
Performance, Alternative Approaches.

Text Book 1: Chapter 11, Chapter 12

Teaching-Learning Process Chalk and board, MOOC
Course Outcomes
At the end of the course the student will be able to:
CO 1. Understand the fundamental concepts of file processing operations and storage structures
CO 2. Apply object orientation concepts to manipulate records
CO 3. Apply concepts of sorting and merging on multiple files
CO 4. Analyze the sequential and indexing file accessing techniques with appropriate data structures
CO 5. Illustrate the usage of hashing techniques to organize file structures
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
 03092022

2. Second test at the end of the 10th week of the semester

3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
6. At the end of the 13th week of the semester- Group discussion/Seminar/quiz any one of three
suitably planned to attain the COs and POs for 20 Marks (duration 01 hours)
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
3. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
4. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Michael J. Folk, Bill Zoellick, Greg Riccardi: File Structures-An Object Oriented Approach with
C++, 3rd Edition, Pearson Education, 1998

Reference Books:
1. K.R. Venugopal, K.G. Srinivas, P.M. Krishnaraj: File Structures Using C++, Tata McGraw-Hill, 2008.
2. Scot Robert Ladd: C++ Components and Algorithms, BPB Publications, 1993.
3. Raghu Ramakrishan and Johannes Gehrke: Database Management Systems, 3rd Edition, McGraw
Hill, 2003.

Web links and Video Lectures (e-Resources):

1. https://www.slideshare.net/shyamujaco/file-structures
2. https://www.vtuplanet.com/m/browse.php?type=papers&dir=B.E
+%28Engineering%29%2FInformation+Science+%28ISE%29%2FSem+6%2FFile+structures
3. https://isenotes.weebly.com/file-structures.html
4. https://www.vssut.ac.in/lecture_notes/lecture1428550942.pdf
5. https://www.azdocuments.in/2021/05/file-structures-18is61.html
6. http://www.engppt.com/2010/01/file-structures-pdf.html

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

 03092022

VII Semester

DEEP LEARNING
Course Code 21CS743 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 3 Exam Hours 3
Course Learning Objectives

CLO 1.Understand the fundamentals of deep learning.

CLO 2.Know the theory behind Convolutional Neural Networks, Autoencoders, RNN.
CLO 3.Illustrate the strength and weaknesses of many popular deep learning approaches.
CLO 4.Introduce major deep learning algorithms, the problem settings, and their applications to
solve real world problems.
CLO 5. Learn the open issues in deep learning, and have a grasp of the current research directions.
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Introduction to Deep Learning: Introduction, Deep learning Model, Historical Trends in Deep Learning,

Machine Learning Basics: Learning Algorithms, Supervised Learning Algorithms,

Unsupervised Learning Algorithms.

Textbook 1: Chapter1 – 1.1, 1.2, 5.1,5.7-5.8.

Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
Feedforward Networks: Introduction to feedforward neural networks, Gradient-Based Learning, Back-
Propagation and Other Differentiation Algorithms. Regularization for Deep Learning,

Textbook 1: Chapter 6, 7
Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
Optimization for Training Deep Models: Empirical Risk Minimization, Challenges in Neural Network
Optimization, Basic Algorithms: Stochastic Gradient Descent, Parameter Initialization Strategies,
Algorithms with Adaptive Learning Rates: The AdaGrad algorithm, The RMSProp algorithm, Choosing the
Right Optimization Algorithm.
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Textbook 1: Chapter: 8.1-8.5

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration
Module-4
Convolutional Networks: The Convolution Operation, Pooling, Convolution and Pooling as an Infinitely
Strong Prior, Variants of the Basic Convolution Function, Structured Outputs, Data Types, Efficient
Convolution Algorithms, Random or Unsupervised Features- LeNet, AlexNet.

Textbook 1: Chapter: 9.1-9.9.

Teaching-Learning Process Chalk& board, Problem based learning

Module-5
Recurrent and Recursive Neural Networks: Unfolding Computational Graphs, Recurrent Neural
Network, Bidirectional RNNs, Deep Recurrent Networks, Recursive Neural Networks, The Long Short-
Term Memory and Other Gated RNNs.

Applications: Large-Scale Deep Learning, Computer, Speech Recognition, Natural Language Processing
and Other Applications.

Textbook 1: Chapter: 10.1-10.3, 10.5, 10.6, 10.10, 12.

Teaching-Learning Process Chalk and board, MOOC
Course Outcomes
CO1: Understand the fundamental issues and challenges of deep learning data, model selection, model
complexity etc.,
CO2: Describe various knowledge on deep learning and algorithms
CO3: Apply CNN and RNN model for real time applications
CO4: Identify various challenges involved in designing and implementing deep learning algorithms.
CO5: Relate the deep learning algorithms for the given types of learning tasks in varied domain

Assessment Details (both CIE and SEE)

The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
 03092022

as per the outcome defined for the course.

Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall be
proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Ian Goodfellow, Yoshua Bengio, Aaron Courville, “Deep Learning”, MIT Press, 2016.
Reference:
1. Bengio, Yoshua. "Learning deep architectures for AI." Foundations and trends in Machine
Learning, 2009.
2. N.D.Lewis, “Deep Learning Made Easy with R: A Gentle Introduction for Data Science”, January
2016.
3. Nikhil Buduma, “Fundamentals of Deep Learning: Designing Next-Generation Machine
Intelligence Algorithms”, O’Reilly publications.
Weblinks and Video Lectures (e-Resources):
● https://faculty.iitmandi.ac.in/~aditya/cs671/index.html
● https://nptel.ac.in/courses/106/106/106106184/
● https://www.youtube.com/watch?v=7x2YZhEj9Dw

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

 03092022

VII Semester

ROBOTIC PROCESS AUTOMATION DESIGN AND DEVELOPMENT

Course Code 21CS744 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 3 Exam Hours 3
Course Learning Objectives

CLO 1. To understand basic concepts of RPA

CLO 2. To Describe RPA, where it can be applied and how its implemented
CLO 3. To Describe the different types of variables, Control Flow and data manipulation techniques
CLO 4. To Understand Image, Text and Data Tables Automation
CLO 5. To Describe various types of Exceptions and strategies to handle
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
RPA Foundations- What is RPA – Flavors of RPA- History of RPA- The Benefits of RPA- The downsides
of RPA- RPA Compared to BPO, BPM and BPA – Consumer Willingness for Automation- The Workforce of
the Future- RPA Skills-On-Premise Vs. the Cloud- Web Technology- Programming Languages and Low
Code- OCR-Databases-APIs- AI-Cognitive Automation-Agile, Scrum, Kanban and Waterfall0 DevOps-
Flowcharts.

Textbook 1: Ch 1, Ch 2
Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
RPA Platforms- Components of RPA- RPA Platforms-About Ui Path- About UiPath - The future of
automation - Record and Play - Downloading and installing UiPath Studio -Learning Ui Path Studio- -
Task recorder - Step-by-step examples using the recorder.

Textbook 2: Ch 1, Ch 2

Teaching-Learning Process Chalk and board, Active Learning, Demonstration

Module-3
Sequence, Flowchart, and Control Flow-Sequencing the workflow-Activities-Control flow, various
types of loops, and decision making-Step-by-step example using Sequence and Flowchart-Step-by-step
 03092022

example using Sequence and Control flow-Data Manipulation-Variables and Scope-Collections-

Arguments – Purpose and use-Data table usage with examples-Clipboard management-File operation
with step-by-step example-CSV/Excel to data table and vice versa (with a step-by-step example).

Textbook 2: Ch 3, Ch 4

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration

Module-4
Taking Control of the Controls- Finding and attaching windows- Finding the control- Techniques for
waiting for a control- Act on controls – mouse and keyboard activities- Working with UiExplorer-
Handling events- Revisit recorder- Screen Scraping- When to use OCR- Types of OCR available- How to
use OCR- Avoiding typical failure points.

Textbook 2: Ch 5

Teaching-Learning Process Chalk& board, Problem based learning

Module-5
Exception Handling, Debugging, and Logging- Exception handling- Common exceptions and ways to
handle them- Logging and taking screensHOT- Debugging techniques- Collecting crash dumps- Error
reporting- Future of RPA

Textbook 2: Ch 8
Textbook 1: Ch 13

Teaching-Learning Process Chalk and board, MOOC

Course Outcomes
CO 1. To Understand the basic concepts of RPA
CO 2. To Describe various components and platforms of RPA
CO 3. To Describe the different types of variables, control flow and data manipulation techniques
CO 4. To Understand various control techniques and OCR in RPA
CO 5. To Describe various types and strategies to handle exceptions
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
 03092022

methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Tom Taulli , The Robotic Process Automation Handbook : A Guide to Implementing RPA Systems,
2020, ISBN-13 (electronic): 978-1-4842-5729-6, Publisher : Apress
2. Alok Mani Tripathi, Learning Robotic Process Automation, Publisher: Packt Publishing Release
Date: March 2018 ISBN: 9781788470940
Reference:
1. Frank Casale, Rebecca Dilla, Heidi Jaynes, Lauren Livingston, “Introduction to Robotic Process
Automation: a Primer”, Institute of Robotic Process Automation.
2. Richard Murdoch, Robotic Process Automation: Guide To Building Software Robots, Automate
Repetitive Tasks & Become An RPA Consultant
3. Srikanth Merianda,Robotic Process Automation Tools, Process Automation and their benefits:
Understanding RPA and Intelligent Automation

Weblinks and Video Lectures (e-Resources):

● https://www.uipath.com/rpa/robotic-process-automation

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

 03092022

VII Semester

NOSQL DATABASE
Course Code: 21CS745 CIE Marks 50
Teaching Hours/Week (L:T:P:S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Objectives:

CLO 1. Recognize and Describe the four types of NoSQL Databases, the Document-oriented, KeyValue
CLO 2. Pairs, Column-oriented and Graph databases useful for diverse applications.
CLO 3. Apply performance tuning on Column-oriented NoSQL databases and Document-oriented NoSQL
Databases.
CLO 4. Differentiate the detailed architecture of column oriented NoSQL database, Document database
and Graph Database and relate usage of processor, memory, storage and file system commands.
CLO 5. Evaluate several applications for location based service and recommendation services. Devise an
application using the components of NoSQL.

Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer methods (L) need not to be only traditional lecture methods, but alternative effective
teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes critical
thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop design
thinking skills such as the ability to design, evaluate, generalize, and analyze information rather
than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem and encourage the students to come up with
their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it helps
improve the students' understanding.
Module-1
Why NoSQL? The Value of Relational Databases, Getting at Persistent Data, Concurrency, Integration, A
(Mostly) Standard Model, Impedance Mismatch, Application and Integration Databases, Attack of the
Clusters, The Emergence of NoSQL,

Aggregate Data Models; Aggregates, Example of Relations and Aggregates, Consequences of Aggregate
Orientation, Key-Value and Document Data Models, Column-Family Stores, Summarizing Aggregate-
Oriented Databases.

More Details on Data Models; Relationships, Graph Databases, Schemaless Databases, Materialized Views,
Modeling for Data Access,
Textbook1: Chapter 1,2,3
Teaching-Learning Process Active learning
Module-2
Distribution Models; Single Server, Sharding, Master-Slave Replication, Peer-to-Peer Replication,
Combining Sharding and Replication.
 03092022

Consistency, Update Consistency, Read Consistency, Relaxing Consistency, The CAP Theorem, Relaxing
Durability, Quorums.

Version Stamps, Business and System Transactions, Version Stamps on Multiple Nodes
Textbook1: Chapter 4,5,6
Teaching-Learning Process Active Learning and Demonstrations
Module-3
Map-Reduce, Basic Map-Reduce, Partitioning and Combining, Composing Map-Reduce Calculations, A
Two Stage Map-Reduce Example, Incremental Map-Reduce

Key-Value Databases, What Is a Key-Value Store, Key-Value Store Features, Consistency, Transactions,
Query Features, Structure of Data, Scaling, Suitable Use Cases, Storing Session Information, User Profiles,
Preference, Shopping Cart Data, When Not to Use, Relationships among Data, Multioperation
Transactions, Query by Data, Operations by Sets

Textbook1: Chapter 7,8

Teaching-Learning Process Active Learning, Problem solving based
Module-4
Document Databases, What Is a Document Database?, Features, Consistency, Transactions, Availability,
Query Features, Scaling, Suitable Use Cases, Event Logging, Content Management Systems, Blogging
Platforms, Web Analytics or Real-Time Analytics, E- Commerce Applications, When Not to Use, Complex
Transactions Spanning Dif erent Operations, Queries against Varying Aggregate Structure

Textbook1: Chapter 9
Teaching-Learning Process Active learning

Module-5
Graph Databases, What Is a Graph Database?, Features, Consistency, Transactions, Availability, Query
Features, Scaling, Suitable Use Cases, Connected Data, Routing, Dispatch, and Location-Based Services,
Recommendation Engines, When Not to Use.
Textbook1: Chapter 11
Teaching-Learning Process Active learning
Course Outcomes (Course Skill Set)

At the end of the course the student will be able to:

CO1. Demonstrate an understanding of the detailed architecture of Column Oriented NoSQL databases,
Document databases, Graph databases.
CO2. Use the concepts pertaining to all the types of databases.
CO3. Analyze the structural Models of NoSQL.
CO4. Develop various applications using NoSQL databases.
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
 03092022

4. First assignment at the end of 4th week of the semester

5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20
Marks (duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall be
proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
Suggested Learning Resources:
Textbooks
1. Sadalage, P. & Fowler, NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot
Persistence, Pearson Addision Wesley, 2012
Reference Books
1. Dan Sullivan, "NoSQL For Mere Mortals", 1st Edition, Pearson Education India, 2015. (ISBN- 13:
978-9332557338)
2. Dan McCreary and Ann Kelly, "Making Sense of NoSQL: A guide for Managers and the Rest of us",
1st Edition, Manning Publication/Dreamtech Press, 2013. (ISBN-13: 978-9351192022)
3. Kristina Chodorow, "Mongodb: The Definitive Guide- Powerful and Scalable Data Storage", 2nd
Edition, O'Reilly Publications, 2013. (ISBN-13: 978-9351102694)
Weblinks and Video Lectures (e-Resources):
1. https://www.geeksforgeeks.org/introduction-to-nosql/ ( and related links in the page)
2. https://www.youtube.com/watch?v=0buKQHokLK8 (How do NoSQL databases work? Simply
explained)
3. https://www.techtarget.com/searchdatamanagement/definition/NoSQL-Not-Only-SQL (What is
NoSQL and How do NoSQL databases work)
4. https://www.mongodb.com/nosql-explained (What is NoSQL)
5. https://onlinecourses.nptel.ac.in/noc20-cs92/preview (preview of Bigdata course contains
NoSQL)

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

● Real world problem solving using group discussion.
 03092022

VII Semester

PROGRAMMING IN PYTHON
Course Code 21CS751 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. To understand why Python is a useful scripting language for developers

CLO 2. To read and write simple Python programs
CLO 3. To learn how to identify Python object types.
CLO 4. To learn how to write functions and pass arguments in Python.
CLO 5. To use Python data structures –- lists, tuples, dictionaries.

Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
INTRODUCTION DATA, EXPRESSIONS, STATEMENTS:08 Hours
Introduction: Creativity and motivation, understanding programming, Terminology: Interpreter and
compiler, Running Python, The First Program; Data types: Int, float, Boolean, string, and list, variables,
expressions, statements, Operators and operands.

Textbook 1: Chapter 1.1,1.2,1.3,1.6, Chapter 2.1-2.6

Textbook 2: Chapter 1
Teaching-Learning Process Chalk and board, Active Learning
Module-2
CONTROL FLOW, LOOPS:
Conditionals: Boolean values and operators, conditional (if), alternative (if-else), chained conditional (if-
elif-else); Iteration: while, for, break, continue, pass statement.

Textbook 1: Chapter 3.1-3.6, chapter 5

Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
FUNCTIONS AND STRINGS:
Functions: Function calls, adding new functions, definition and uses, local and global scope, return values.
Strings: strings, length of string, string slices, immutability, multiline comments, string functions and
methods;
 03092022

Textbook 1: Chapter 6
Textbook 2: Chapter 3
Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-4
LISTS, TUPLES, DICTIONARIES:08 Hours
Lists:List operations, list slices, list methods, list loop, mutability, aliasing, cloning lists, listparameters,
list comprehension;

Tuples: tuple assignment, tuple as return value, tuple comprehension;

Dictionaries: operations and methods, comprehension;

Textbook 2: Chapter 10,11,12

Teaching-Learning Process Chalk& board, Active Learning
Module-5
REGULAR EXPRESSIONS,FILES AND EXCEPTION:
Regular expressions:Character matching in regular expressions, extracting data using regular
expressions, Escape character

Files and exception: Text files, reading and writing files, command line arguments, errors andexceptions,
handling exceptions, modules.

Textbook 1: Chapter 11.1,11.2,11.4

Textbook 2: Chapter 14
Teaching-Learning Process Chalk and board, MOOC
Suggested Course Outcomes
At the end of the course the student will be able to:
CO 1. Understand Python syntax and semantics and be fluent in the use of Python flow control and
functions.
CO 2. Demonstrate proficiency in handling Strings and File Systems.
CO 3. Represent compound data using Python lists, tuples, Strings, dictionaries.
CO 4. Read and write data from/to files in Python Programs
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20 Marks
(duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
 03092022

methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Textbooks
1. Charles R. Severance, “Python for Everybody: Exploring Data Using Python 3”, 1st Edition,
CreateSpace Independent Publishing Platform, 2016.
http://do1.dr-chuck.com/pythonlearn/EN_us/pythonlearn.pdf
2. Allen B. Downey, "Think Python: How to Think Like a Computer Scientist”, 2ndEdition, Green Tea
Press, 2015. (Chapters 15, 16, 17)
http://greenteapress.com/thinkpython2/thinkpython2.pdf
REFERENCE BOOKS:
1. R. Nageswara Rao, “Core Python Programming”, dreamtech
2. Python Programming: A Modern Approach, Vamsi Kurama, Pearson
3. Python Programming , Reema theraja, OXFORD publication
Weblinks and Video Lectures (e-Resources):
1. https://www.w3resource.com/python/python-tutorial.php
2. https://data-flair.training/blogs/python-tutorials-home/
3. https://www.youtube.com/watch?v=c235EsGFcZs
4. https://www.youtube.com/watch?v=v4e6oMRS2QA
5. https://www.youtube.com/watch?v=Uh2ebFW8OYM
6. https://www.youtube.com/watch?v=oSPMmeaiQ68
7. https://www.youtube.com/watch?v=_uQrJ0TkZlc
8. https://www.youtube.com/watch?v=K8L6KVGG-7o
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning
Real world problem solving: Demonstration of projects developed using python language
 03092022

VII Semester

INTRODUCTION TO AI AND ML
Course Code 21CS752 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives
CLO1. Understands the basics of AI, history of AI and its foundations, basic principles of AI for problem
solving
CLO2. Explore the basics of Machine Learning & Machine Learning process, understanding data
CLO3. Understand the Working of Artificial Neural Networks
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
Introduction: What is AI, The foundation of Artificial Intelligence, The history of Artificial Intelligence,
Intelligent Agents: Agents and Environments, Good Behaviour: The concept of rationality, the nature of
Environments, the structure of Agents.

Textbook 1: Chapter: 1 and 2

Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
Problem solving by searching: Problem solving agents, Example problems, Searching for solutions,
Uniformed search strategies, Informed search strategies, Heuristic functions

Textbook 1: Chapter: 3
Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
Introduction to machine learning: Need for Machine Learning, Machine Learning Explained, and
Machine Learning in relation to other fields, Types of Machine Learning. Challenges of Machine Learning,
Machine Learning process, Machine Learning applications.

Understanding Data: What is data, types of data, Big data analytics and types of analytics, Big data
analytics framework, Descriptive statistics, univariate data analysis and visualization

Textbook 2: Chapter: 1 and 2.1 to 2.5

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration
Module-4
 03092022

Understanding Data
Bivariate and Multivariate data, Multivariate statistics , Essential mathematics for Multivariate data,
Overview hypothesis, Feature engineering and dimensionality reduction techniques,

Basics of Learning Theory: Introduction to learning and its types, Introduction computation learning
theory, Design of learning system, Introduction concept learning.

Similarity-based learning: Introduction to Similarity or instance based learning, Nearest-neighbour

learning, weighted k- Nearest - Neighbour algorithm.

Textbook 2: Chapter: 2.6 to 2.10, 3.1 to 3.4, 4.1 to 4.3

Teaching-Learning Process Chalk& board, Problem based learning
Module-5
Artificial Neural Network: Introduction, Biological neurons, Artificial neurons, Perceptron and learning
theory, types of Artificial neural Network, learning in multilayer Perceptron, Radial basis function neural
network, self-organizing feature map,

Textbook 2: Chapter: 10
Teaching-Learning Process Chalk and board, MOOC
Course Outcomes
At the end of the course the student will be able to:
CO 1. Design intelligent agents for solving simple gaming problems.
CO 2. Have a good understanding of machine leaning in relation to other fields and fundamental issues
and
Challenges of machine learning
CO 3. Understand data and applying machine learning algorithms to predict the outputs.
CO 4. Model the neuron and Neural Network, and to analyze ANN learning and its applications.
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20 Marks
(duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
 03092022

papers for the subject (duration 03 hours)

1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Textbooks
1. Stuart Russel, Peter Norvig: “Artificial Intelligence A Modern Approach”, 3 rd Edition, Pearson
Education, 2015.
2. S. Sridhar, M Vijayalakshmi “Machine Learning”. Oxford ,2021
REFERENCE BOOKS:
1. Elaine Rich, Kevin Knight: “Artificial Intelligence”, 3rd Edition, Tata McGraw Hill,
2009, ISBN-10: 0070087709
2. Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier, 1980, ISBN: 978-3-540-11340-9.

Weblinks and Video Lectures (e-Resources):

http://stpk.cs.rtu.lv/sites/all/files/stpk/materiali/MI/Artificial%20Intelligence
%20A%20Modern%20Approach.pdf.
1. http://www.getfreeebooks.com/16-sites-with-free-artificial-intelligence-e
books/https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_overview.ht
m
2. Problem solving agent:https://www.youtube.com/watch?v=KTPmo-KsOis.
3. https://www.youtube.com/watch?v=X_Qt0U66aH0&list=PLwdnzlV3ogoXaceHrrFVZCJKbm_laSH
cH
4. https://www.javatpoint.com/history-of-artificial-intelligence
5. https://www.tutorialandexample.com/problem-solving-in-artificial-intelligence
6. https://techvidvan.com/tutorials/ai-heuristic-search/
7. https://www.analyticsvidhya.com/machine-learning/
8. https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/ml-
decision-tree/tutorial/
9. https://www.javatpoint.com/unsupervised-artificial-neural-networks
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning
Real world problem solving: Demonstration of projects related to AI and ML.
 03092022

VII Semester

INTRODUCTION TO BIG DATA

Course Code 21CS753 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. Understand Hadoop Distributed File system and examine MapReduce Programming
CLO 2. Explore Hadoop tools and manage Hadoop with Sqoop
CLO 3. Appraise the role of data mining and its applications across industries
CLO 4. Identify various Text Mining techniques
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.

Module-1
Hadoop Distributed file system:HDFS Design, Features, HDFS Components, HDFS user commands
Hadoop MapReduce Framework: The MapReduce Model, Map-reduce Parallel Data Flow,Map Reduce
Programming

Textbook 1: Chapter 3,5,68hr

Teaching-Learning Process Chalk and board, Active Learning, Problem based learning
Module-2
Essential Hadoop Tools:Using apache Pig, Using Apache Hive, Using Apache Sqoop, Using Apache
Apache Flume, Apache H Base

Textbook 1: Chapter 78hr

Teaching-Learning Process Chalk and board, Active Learning, Demonstration
Module-3
Data Warehousing: Introduction, Design Consideration, DW Development Approaches, DW
Architectures

Data Mining: Introduction, Gathering, and Selection, data cleaning and preparation, outputs ofData
Mining, Data Mining Techniques

Textbook 2: Chapter 4,5

Teaching-Learning Process Chalk and board, Problem based learning, Demonstration
Module-4
 03092022

Decision Trees: Introduction, Decision Tree Problem, Decision Tree Constructions, Lessons from
Construction Trees. Decision Tree Algorithm

Regressions: Introduction, Correlations and Relationships, Non-Linear Regression, Logistic Regression,

Advantages and disadvantages.

Textbook 2: Chapter 6,7

Teaching-Learning Process Chalk& board, Problem based learning
Module-5
Text Mining: Introduction, Text Mining Applications, Text Mining Process, Term Document Matrix,
Mining the TDM, Comparison, Best Practices

Web Mining: Introduction, Web Content Mining, Web Structured Mining, Web Usage Mining, Web Mining
Algorithms.

Textbook 2: Chapter 11,14

Teaching-Learning Process Chalk and board, MOOC
Suggested Course Outcomes
At the end of the course the students will be able to:
CO 1. Master the concepts of HDFS and MapReduce framework.
CO 2. Investigate Hadoop related tools for Big Data Analytics and perform basic
CO 3. Infer the importance of core data mining techniques for data analytics
CO 4. Use Machine Learning algorithms for real world big data.
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20 Marks
(duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
 03092022

2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.

The students have to answer 5 full questions, selecting one full question from each module.
Textbooks
1. Douglas Eadline,"Hadoop 2 Quick-Start Guide: Learn the Essentials of Big DataComputing in the
Apache Hadoop 2 Ecosystem", 1stEdition, Pearson Education,2016.
2. Anil Maheshwari, “Data Analytics”, 1stEdition, McGraw Hill Education,2017
Weblinks and Video Lectures (e-Resources):
1. https://nptel.ac.in/courses/106/104/106104189/
2. https://www.youtube.com/watch?v=mNP44rZYiAU
3. https://www.youtube.com/watch?v=qr_awo5vz0g
4. https://www.youtube.com/watch?v=rr17cbPGWGA
5. https://www.youtube.com/watch?v=G4NYQox4n2g
6. https://www.youtube.com/watch?v=owI7zxCqNY0
7. https://www.youtube.com/watch?v=FuJVLsZYkuE
Activity Based Learning (Suggested Activities in Class)/ Practical Based learning
Real world problem solving: Demonstration of Big Data related projects
Exploring the applications which involves big data.
 03092022

VII Semester

INTRODUCTION TO DATA SCIENCE

Course Code 21CS754 CIE Marks 50
Teaching Hours/Week (L:T:P: S) 3:0:0:0 SEE Marks 50
Total Hours of Pedagogy 40 Total Marks 100
Credits 03 Exam Hours 03
Course Learning Objectives

CLO 1. To provide a foundation in data Science terminologies

CLO 2. To familiarize data science process and steps
CLO 3. To Demonstrate the data visualization tools
CLO 4. To analyze the data science applicability in real time applications.
Teaching-Learning Process (General Instructions)

These are sample Strategies, which teachers can use to accelerate the attainment of the various course
outcomes.
1. Lecturer method (L) need not to be only a traditional lecture method, but alternative
effective teaching methods could be adopted to attain the outcomes.
2. Use of Video/Animation to explain functioning of various concepts.
3. Encourage collaborative (Group Learning) Learning in the class.
4. Ask at least three HOT (Higher order Thinking) questions in the class, which promotes
critical thinking.
5. Adopt Problem Based Learning (PBL), which fosters students’ Analytical skills, develop
design thinking skills such as the ability to design, evaluate, generalize, and analyze
information rather than simply recall it.
6. Introduce Topics in manifold representations.
7. Show the different ways to solve the same problem with different circuits/logic and
encourage the students to come up with their own creative ways to solve them.
8. Discuss how every concept can be applied to the real world - and when that's possible, it
helps improve the students' understanding.
Module-1
PREPARING AND GATHERING DATA AND KNOWLEDGE
Philosophies of data science - Data science in a big data world - Benefits and uses of data science and big
data - facts of data: Structured data, Unstructured data, Natural Language, Machine generated data, Audio,
Image and video streaming data - The Big data Eco system: Distributed file system, Distributed
Programming framework, Data Integration frame work, Machine learning Framework, NoSQL Databases,
Scheduling tools, Benchmarking Tools, System Deployment, Service programming and Security.

Textbook 1: Ch 1.1 to 1.4

Teaching-Learning Process Chalk and board, Active Learning, PPT Based presentation
Module-2
THE DATA SCIENCE PROCESS-Overview of the data science process- defining research goals and
creating project charter, retrieving data, cleansing, integrating and transforming data, exploratory data
analysis, Build the models, presenting findings and building application on top of them.

Textbook 1:,Ch 2
Teaching-Learning Process Chalk and board, Active Learning, PPT Based presentation
Module-3
MACHINE LEARNING: Application for machine learning in data science- Tools used in machine learning-
Modeling Process – Training model – Validating model – Predicting new observations –Types of machine
learning Algorithm : Supervised learning algorithms, Unsupervised learning algorithms.

Textbook 1: Ch 3.1 to 3.3

 03092022

Teaching-Learning Process Chalk and board, Active Learning, PPT Based presentation, Video
Module-4
VISUALIZATION–Introduction to data visualization – Data visualization options – Filters – MapReduce –
Dashboard development tools.

Textbook 1: Ch 9

Teaching-Learning Process Chalk and board, Active Learning, PPT Based presentation, MOOC
Module-5
CASE STUDIES Distributing data storage and processing with frameworks - Case study: e.g, Assessing risk
when lending money.

Textbook 1: Ch 5.1, 5.2

Teaching-Learning Process Chalk and board, Active Learning, PPT Based presentation, Video
Course Outcomes
At the end of the course the student will be able to:
CO 1. Describe the data science terminologies
CO 2. Apply the Data Science process on real time scenario.
CO 3. Analyze data visualization tools
CO 4. Apply Data storage and processing with frameworks
Assessment Details (both CIE and SEE)
The weightage of Continuous Internal Evaluation (CIE) is 50% and for Semester End Exam (SEE) is 50%.
The minimum passing mark for the CIE is 40% of the maximum marks (20 marks). A student shall be
deemed to have satisfied the academic requirements and earned the credits allotted to each subject/
course if the student secures not less than 35% (18 Marks out of 50) in the semester-end examination
(SEE), and a minimum of 40% (40 marks out of 100) in the sum total of the CIE (Continuous Internal
Evaluation) and SEE (Semester End Examination) taken together
Continuous Internal Evaluation:
Three Unit Tests each of 20 Marks (duration 01 hour)
1. First test at the end of 5th week of the semester
2. Second test at the end of the 10th week of the semester
3. Third test at the end of the 15th week of the semester
Two assignments each of 10 Marks
4. First assignment at the end of 4th week of the semester
5. Second assignment at the end of 9th week of the semester
Group discussion/Seminar/quiz any one of three suitably planned to attain the COs and POs for 20 Marks
(duration 01 hours)
6. At the end of the 13th week of the semester
The sum of three tests, two assignments, and quiz/seminar/group discussion will be out of 100 marks
and will be scaled down to 50 marks
(to have less stressed CIE, the portion of the syllabus should not be common /repeated for any of the
methods of the CIE. Each method of CIE should have a different syllabus portion of the course).
CIE methods /question paper has to be designed to attain the different levels of Bloom’s taxonomy
as per the outcome defined for the course.
Semester End Examination:
Theory SEE will be conducted by University as per the scheduled timetable, with common question
papers for the subject (duration 03 hours)
1. The question paper will have ten questions. Each question is set for 20 marks. Marks scored shall
be proportionally reduced to 50 marks
2. There will be 2 questions from each module. Each of the two questions under a module (with a
maximum of 3 sub-questions), should have a mix of topics under that module.
The students have to answer 5 full questions, selecting one full question from each module.
 03092022

Textbooks
1. Introducing Data Science, Davy Cielen, Arno D. B. Meysman and Mohamed Ali,Manning
Publications, 2016.
Reference Books
1. Doing Data Science, Straight Talk from the Frontline, Cathy O'Neil, Rachel Schutt, O’ Reilly, 1st
edition, 2013.
2. Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, Cambridge
University Press, 2nd edition, 2014
3. An Introduction to Statistical Learning: with Applications in R, Gareth James, Daniela Witten,
Trevor Hastie, Robert Tibshirani, Springer, 1st edition, 2013
4. Think Like a Data Scientist, Brian Godsey, Manning Publications, 2017.
Weblinks and Video Lectures (e-Resources):
1. https://www.simplilearn.com/tutorials/data-science-tutorial/what-is-data-science
2. https://www.youtube.com/watch?v=N6BghzuFLIg
3. https://www.coursera.org/lecture/what-is-datascience/fundamentals-of-data-science-tPgFU
4. https://www.youtube.com/watch?v=ua-CiDNNj30

Activity Based Learning (Suggested Activities in Class)/ Practical Based learning

Real world problem solving using Data science techniques and demonstration of data visualization
methods with the help of suitable project.