Bachelor of Engineering

In
Computer Engineering

Third Year
Semester – V (Rollover Batch)
REVISION: SJCEM RO – 24

Effective from Academic Year 2024-25

Board of Studies Approval:

Academic Council Approval:

DEPARTMENT OF COMPUTER ENGINEERING

 Program Structure for Third Year Computer Engineering
St. John College of Engineering and Management (Autonomous)

(With Effect from 2024-2025)

Contact Hrs Credit Allotted Total

Course Code Vertical Course Name
Credits
Th Tut Pr Th Tut Pr
24CSPCC501 PCC Theoretical Computer Science 3 - - 3 - - 3

24CSPCC502 PCC Software Engineering 3 - 2 3 - 1 4

24CSPCC503 PCC Computer Network 3 - 2 3 - 1 4

24CSPCC504 PCC Data Warehousing and Mining 3 - 2 3 - 1 4

24CSPEC501
PEC Department Level Optional Course 1 3 - - 3 - - 3
X
Corporate Communication & Employ-
24CSAEC501 AEC - - 2 - - 1 1
ability Skills – I
Employability Enhancement Program
24CSVSE501 VSEC - - 4 - - 2 2
(Technical)
24CSVSE501 VSEC Skill Based Lab with Mini Project - - 2 - - 1 1
Total 15 0 14 15 0 7 22

Evaluation Scheme
Course Code Vertical Course Name
IAE 1 IAE 2 ESE CA OR/PR Total

24CSPCC501 PCC Theoretical Computer Science 20 20 60 - - 100

24CSPCC502 PCC Software Engineering 20 20 60 25 25 150
24CSPCC503 PCC Computer Network 20 20 60 25 25 150
24CSPCC504 PCC Data Warehousing and Mining 20 20 60 25 25 150
24CSPEC501
PEC Department Level Optional Course 1 20 20 60 - - 100
X
Corporate Communication & Em-
24CSAEC501 AEC - - - 25 - 25
ployability Skills – I
Employability Enhancement Program
24CSVSE501 VSEC - - - 25 - 25
(Technical)
24CSVSE501 VSEC Skill Based Lab with Mini Project - - - 50 25 75
Total 100 100 300 175 100 775

DEPARTMENT OF COMPUTER ENGINEERING

Department Level Optional Course 1

Course Code Department Level Optional Course

24CSPEC5011 Probabilistic Graphical Models

24CSPEC5012 Internet Programming

24CSPEC5013 Advance Database Management System

DEPARTMENT OF COMPUTER ENGINEERING

 Syllabus

For
Third Year Computer Engineering
Semester – V (Rollover Batch)

(With Effect from 2024-2025)

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Theoretical Computer Science
Semester: V Term: ODD Course Code: 24CSPCC501
Teaching Scheme Evaluation Scheme
Credit
Contact Hrs. Total Oral/Pract/
Allotted IAE 1 IAE 2 ESE CA Total
Credit Tut.
Th Tu Pr Th Tu Pr
3 - - 3 - - 3 20 20 60 - - 125

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Course Objectives:
1 Acquire conceptual understanding of fundamentals of grammars and languages.
2 Build concepts of theoretical design of deterministic and non-deterministic finite
automata and push down automata.
3 Develop understanding of different types of Turing machines and applications.
4 Understand the concept of undecidability.

Course Outcomes:
At the end of the course students will be able to:
1 Understand concepts of Theoretical Computer Science, difference and equivalence of DFA and NFA.
2 Understand the languages described by finite automata and regular expressions.
3 Design Context free grammar.
4 Design pushdown automata to recognize the language.
5 Develop an understanding of computation through Turing Machine.
6 Acquire fundamental understanding of decidability and undecidability.

DEPARTMENT OF COMPUTER ENGINEERING

Module Contents Hours COs
I Basic Concepts and Finite Automata 09 CO1
 Introduction of TCS, Alphabets, Strings, Languages Closure
properties, Finite Automata (FA) and Finite State machine
(FSM).
 Deterministic Finite Automata (DFA) and Nondeterministic Fi-
nite Automata (NFA): Definitions, transition diagrams and Lan-
guage recognizers, Equivalence between NFA with and without
ε- transitions, NFA to DFA Conversion, Minimization of DFA,
FSM with output: Moore and Mealy machines.
Self-Learning Topics: Applications and limitations of FA.

II Regular Expressions and Languages 08 CO2

 Equivalence of RE and FA, Arden‘s Theorem, RE Applications
 Closure properties of RLs, Decision properties of RLs, Pumping
lemma for RLs.
Self-Learning Topics: Regular Expressions, Regular Language
III Grammars 09 CO3
 Grammars and Chomsky hierarchy
 Regular Grammar (RG), Equivalence of Left and Right linear
grammar, Equivalence of RG and FA.
 Context Free Grammars (CFG)
Definition, Sentential forms, Leftmost and Rightmost deriva-
tions, Parse tree, Ambiguity, Simplification and Applications,
Normal Forms: Chomsky Normal Forms (CNF) and Greibach
Normal Forms (GNF), Context Freelanguage (CFL) - Pumping
lemma, Closure properties.
Self-Learning Topics: Grammars, Context free Grammar defi-
nition
IV Pushdown Automata(PDA) 05 CO4
Language of PDA, PDA as generator, decider and acceptor of CFG,
Deterministic PDA, Non-Deterministic PDA.
Self-Learning Topics: Definition PDA, Applications of PDA
V Turing Machine (TM) 09 CO5
Definition, Design of TM as generator, decider and acceptor, Var-
iants of TM: Multitrack, Multitape, Universal TM, Applications.
Self-Learning Topics: Turing Machine definition, Power and
Limitations of TMs
VI Undecidability 05 CO6
Recursive and Recursively Enumerable Languages, Halting Prob-
lem, Rice‘s Theorem, Post Correspondence Problem.
Self-Learning Topics: Decidability and Undecidability
Total 45

DEPARTMENT OF COMPUTER ENGINEERING

Evaluation and Assessment Scheme:

A. Internal Assessment Examination (IAE):

Assessment consists of two class tests, each 20 marks. The IAE 1 will cover any three Course
Outcomes (COs) and IAE 2 will cover the remaining three Course Outcomes (COs). Each test
will have a duration of one hour.

B. End Semester Theory Examination (ESE):

End Semester exam of 60 Marks will be conducted based on entire syllabus.

Reference Books:
1. J. C. Martin, “Introduction to Languages and the Theory of Computation”, 4th Edition, Tata McGraw
Hill Publication, 2013.
2. Kavi Mahesh, “Theory of Computation: A Problem Solving Approach”, KindleEdition, Wiley-In-
dia, 2011.

Text Books:

1. John E. Hopcroft, Rajeev Motwani, Jeffery D. Ullman, “Introduction to AutomataTheory, Lan-

guages and Computation”, 3rd Edition, Pearson Education, 2008.
2. A Michael Sipser, “Theory of Computation”, 3rd Edition, Cengage learning. 2013.

3. Vivek Kulkarni, “Theory of Computation”, Illustrated Edition, Oxford UniversityPress,

(12 April 2013) India.

Useful Links
1. www.jflap.org
2. https://nptel.ac.in/courses/106/104/106104028/
3. https://nptel.ac.in/courses/106/104/106104148/

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Software Engineering
Semester: V Term: ODD Course Code: 24CSPCC502
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
3 - 2 3 - 1 4 20 20 60 25 25 150

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Course Objectives:
1. To provide the knowledge of software engineering discipline.
2. To apply analysis, design and testing principles to software project development.
3. To demonstrate and evaluate real world software projects.

Course Outcomes:
At the end of the course students will be able to:
1. Describe software engineering process.
2. Identify requirements & assess the process models.
3. Plan, schedule and track the progress of the projects.
4. Design the software projects.
5. Do testing of software project.
6. Identify risks, manage the change to assure quality in software projects.

DEPARTMENT OF COMPUTER ENGINEERING

Module Content Hours COs
I Introduction To Software Engineering and Process Models 08 CO1
 Software Engineering-process framework, the Capability
Maturity Model(CMM)
 Prescriptive Process Models: The Waterfall, Incremental
Process Models, Evolutionary Process Models: RAD & Spi-
ral
 Agile process model: Extreme Programming (XP), Scrum,
Kanban

Self-Learning Topics: Advanced Trends in Software Engi-

neering
II Software Requirements Analysis and Modeling 05 CO2
 Requirement Engineering, Requirement Modeling, Data
flow diagram, Scenario based model

Self-Learning Topics: Software Requirement Specifica-

tion document format(IEEE)
III Software Estimation Metrics 08 CO3
 Software Metrics, Software Project Estimation (LOC, FP,
COCOMO II )

Self-Learning Topics: Project Scheduling & Tracking

IV Software Design 08 CO4
 Effective Modular Design, Cohesion and Coupling, Archi-
tectural design

Self-Learning Topics: Design Principles & Concepts

V Software Testing 08 CO5
 Unit testing, Integration testing, Validation testing, System
testing
 Testing Techniques, white-box testing: Basis path, Control
structure testing black-box testing: Graph based, Equiva-
lence, Boundary Value
 Types of Software Maintenance, Re-Engineering, Reverse
Engineering

Self-Learning Topics: Types of Software Testing

Software Configuration Management, Quality Assurance
VI 08 CO6
andMaintenance
 Risk Analysis & Management: Risk Mitigation, Moni-
toring and Management Plan (RMMM).
 Quality Concepts and Software Quality assurance Met-
rics, Formal Technical Reviews, Software Reliability
 The Software Configuration Management (SCM) ,Ver-
sion Control and Change Control

Self-Learning Topics: Quality Assurance

Total 45

DEPARTMENT OF COMPUTER ENGINEERING

List of Experiments:

Exp. No. List of Experiments

1 Application of at least two traditional process models.
2 Application of the Agile process models.
3 Preparation of software requirement specification (SRS) document in IEEE format.
4 Structured data flow analysis.
5 Use of metrics to estimate the cost.
6 Scheduling & tracking of the project.
7 Write test cases for black box testing.
8 Write test cases for white box testing.
9 Preparation of Risk Mitigation, Monitoring and Management Plan (RMMM).
10 Version controlling of the project.

Evaluation and Assessment Scheme:

A. Internal Assessment Examination (IAE):

Assessment consists of two class tests, each 20 marks. The IAE 1 will cover any three Course
Outcomes (COs) and IAE 2 will cover the remaining three Course Outcomes (COs). Each test
will have a duration of one hour.

B. End Semester Theory Examination (ESE):

End Semester exam of 60 Marks will be conducted based on entire syllabus.

C. Continuous Assessment (CA) :

Continuous Assessment should consist of the following
Experiments / Tutorials (8 to 10): 10 marks (All COs / LOs should be covered)
Attendance (Theory & Practical): 05 marks
Teacher Assessment Examination (TAE): 10 Marks

List of Teacher Assessment Examination (TAE):

1. Assignment
2. Case Study
3. Debate
4. Solution for Social Problems
5. Field Visit
6. Group Project
7. Flip Classroom
8. Topic Review
9. Quiz
10. Mind Mapping

DEPARTMENT OF COMPUTER ENGINEERING

 11. Any other
Note: Number of activities to be conducted under TAE would be as per the subject need.

D. Oral & Practical Exam

Based on the entire syllabus, oral (10 marks) & practical/implementation (15 marks)
examination will be conducted.

Reference Books:
1. Pankaj Jalote, "An integrated approach to Software Engineering", 3rd edition, Springer, 2005
2. Rajib Mall, "Fundamentals of Software Engineering", 5th edition, Prentice Hall India, 2014
3. Jibitesh Mishra and Ashok Mohanty, “Software Engineering”, Pearson , 2011
4. Ugrasen Suman, “Software Engineering – Concepts and Practices”, Cengage Learning, 2013
5. Waman S Jawadekar, “Software Engineering principles and practice”, McGraw Hill Education, 2004

Text Books:

1. Roger Pressman, “Software Engineering: A Practitioner‘s Approach”, 9th edition ,McGraw-Hill Pub-
lications, 2019
2. Ian Sommerville, “Software Engineering”, 9th edition, Pearson Education, 2011
3. Ali Behfrooz and Fredeick J. Hudson, "Software Engineering Fundamentals", OxfordUniversity
Press, 1997
4. Grady Booch, James Rambaugh, Ivar Jacobson, “The unified modeling language user guide”, 2nd edi-
tion, Pearson Education, 2005

Useful Links

1. https://nptel.ac.in/courses/106/105/106105182/
2. https://onlinecourses.nptel.ac.in/noc19_cs69/preview
3. https://www.mooc-list.com/course/software-engineering-introduction-edx

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Computer Network
Semester: V Term: ODD Course Code: 24CSPCC503
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
3 - 2 3 - 1 4 20 20 60 25 25 150

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Course Objectives:

1. To introduce concepts and fundamentals of data communication and computer networks.

2. To explore the inter-working of various layers of OSI.
3. To explore the issues and challenges of protocols design while delving into TCP/IP protocol
suite
4. To assess the strengths and weaknesses of various routing algorithms.
5. To understand various transport layer and application layer protocols.

Course Outcomes:

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

1. Understand the ISO-OSI reference model.
2. Demonstrate the concepts of data communication at physical layer and compare ISO – OSI model
with TCP/IP model.
3. Explore different design issues at data link layer.
4. Design the network using IP addressing and sub netting / super netting schemes.
5. Analyze transport layer protocols and congestion control algorithms.
6. Explore protocols at application layer

DEPARTMENT OF COMPUTER ENGINEERING

Module Content Hours COs
I Introduction to Networking 05 CO1
 Introduction to computer network ,Network software and hard-
ware components (Interconnection networking devices), Net-
work topology, protocol hierarchies, design issues for the lay-
ers, connection oriented and connectionless services
 Reference models: Layer details of OSI. Communication be-
tween layers
Self-Learning Topics: TCP/IP models.
II Physical Layer 04 CO2
 Guided Transmission Media: Twisted pair, Coaxial, Fiber op-
tics.
Self-Learning Topics: Introduction to Communication Elec-
tromagnetic Spectrum
III Data Link Layer 09 CO3
 DLL Design Issues (Services, Framing, Error Control, Flow
Control), Error Detection and Correction(Hamming Code,
CRC, Checksum) , Elementary Data Link protocols , Stop and
Wait, Sliding Window(Go Back N, Selective Repeat)
 Medium Access Control sublayer
 Multiple access Protocol (Aloha, CarrierSense Multiple Ac-
cess (CSMA/CD)
Self-Learning Topics: Channel Allocation problem
IV Network layer 13 CO4
 Communication Primitives: Unicast, Multicast, Broadcast.
IPv4 Addressing (classfull and classless), Subnetting, Super-
netting design problems ,IPv4 Protocol, Network Address
Translation (NAT), IPv6
 Routing algorithms : Shortest Path (Dijkastra‘s), Link state
routing,Distance Vector Routing
 Protocols - ARP,RARP, ICMP, IGMP
 Congestion control algorithms: Open loop congestion con-
trol, Closedloop congestion control, QoS parameters, Token &
Leaky bucket algorithms
Self-Learning Topics: Network Layer design issues
V Transport Layer 07 CO5
 The Transport Service: Berkeley Sockets, Connection man-
agement (Handshake), UDP, TCP, TCP state transition, TCP
timers
 TCP Flow control (sliding Window), TCP Congestion Control:
Slow Start
Self-Learning Topics: Transport service primitives
DEPARTMENT OF COMPUTER ENGINEERING
 VI Application Layer 07 CO6

 Resource Record and Types of Name Server. HTTP, SMTP,

Telnet, FTP, DHCP
 Self-Learning Topics: DNS: Name Space
Total 45

List of Experiments:

Exp.No. List of Experiments

1. Study of RJ45 and CAT6 Cabling and connection using crimping tool.
2. Use basic networking commands in Linux (ping, tracert, nslookup, netstat, ARP,
RARP, ip, ifconfig, dig, route )
3. Build a simple network topology and configure it for static routing protocol using packet
tracer. Setup a network and configure IP addressing, subnetting, masking.
4. Perform network discovery using discovery tools (eg. Nmap, mrtg)
5. Use Wire shark to understand the operation of TCP/IP layers:
 Ethernet Layer: Frame header, Frame size etc.
 Data Link Layer: MAC address, ARP (IP and MAC address binding)
 Network Layer: IP Packet (header, fragmentation), ICMP (Query and Echo)
 Transport Layer: TCP Ports, TCP handshake segments etc.
 Application Layer: DHCP, FTP, HTTP header formats
6. Use simulator (Eg. NS2) to understand functioning of ALOHA, CSMA/CD.
7. Study and Installation of Network Simulator (NS3)
8. Set up multiple IP addresses on a single LAN.
Using nestat and route commands of Linux, do the following:
 View current routing table
 Add and delete routes
 Change default gateway
Perform packet filtering by enabling IP forwarding using IPtables in Linux.
9 Design VPN and Configure RIP/OSPF using Packet tracer.
10. Socket programming using TCP or UDP
11. Perform File Transfer and Access using FTP
12. Perform Remote login using Telnet server

DEPARTMENT OF COMPUTER ENGINEERING

Evaluation and Assessment Scheme:

A. Internal Assessment Examination (IAE):

Assessment consists of two class tests, each 20 marks. The IAE 1 will cover any three Course
Outcomes (COs) and IAE 2 will cover the remaining three Course Outcomes (COs). Each test
will have a duration of one hour.

B. End Semester Theory Examination (ESE):

End Semester exam of 60 Marks will be conducted based on entire syllabus.

C. Continuous Assessment (CA) :

Continuous Assessment should consist of the following
Experiments / Tutorials (8 to 10): 10 marks (All COs / LOs should be covered)
Attendance (Theory & Practical): 05 marks
Teacher Assessment Examination (TAE): 10 Marks

List of Teacher Assessment Examination (TAE):

1. Assignment
2. Case Study
3. Debate
4. Solution for Social Problems
5. Field Visit
6. Group Project
7. Flip Classroom
8. Topic Review
9. Quiz
10. Mind Mapping
11. Any other
Note: Number of activities to be conducted under TAE would be as per the subject need.

D. Oral & Practical Exam

Based on the entire syllabus, oral (10 marks) & practical/implementation (15 marks)
examination will be conducted.

Reference Books:

1. S. Keshav, An Engineering Approach To Computer Networking, Pearson

Natalia Olifer & Victor Olifer, Computer Networks: Principles, Technologies &
2. Protocols for Network Design, Wiley India, 2011.
3. Larry L. Peterson, Bruce S. Davie, Computer Networks: A Systems Approach, Second
Edition ,The Morgan Kaufmann Series in Networking

DEPARTMENT OF COMPUTER ENGINEERING

Text Books:

1. A.S. Tanenbaum, Computer Networks,4th edition Pearson Education

2. B.A. Forouzan, Data Communications and Networking, 5th edition, TMH
3. James F. Kurose, Keith W. Ross, Computer Networking, A Top-Down Approach
Featuring the Internet,6th edition, Addison Wesley

Useful Links

1. https://www.netacad.com/courses/networking/networking-essentials
2. https://www.coursera.org/learn/computer-networking
3. https://nptel.ac.in/courses/106/105/106105081
4. https://www.edx.org/course/introduction-to-networking

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Data Warehousing and Mining
Semester: V Term: ODD Course Code: 24CSPCC504
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
3 - 2 3 - 1 4 20 20 60 25 25 150

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment,

Course Objectives:

1. To identify the significance of Data Warehousing and Mining.

2. To analyze data, choose relevant models and algorithms for respective applications.
3. To study web data mining.
4. To develop research interest towards advances in data mining.

Course Outcomes:

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

1. Understand data warehouse fundamentals and design data warehouse with dimensionalmodel-
ling and apply OLAP operations.
2. Understand data mining principles and perform Data preprocessing and Visualization.
3. Apply classification algorithms for better decision making.
4. Apply clustering methods for unsupervised learning.
5. Describe associations between attributes of data.
6. Describe complex information and social networks with respect to web mining.

DEPARTMENT OF COMPUTER ENGINEERING

Module Content Hours COs
I Data Warehousing Fundamentals 09 CO1
Data warehouse architecture, Data warehouse versus Data Marts, E-
R Modeling versus Dimensional Modeling, Information Package Di-
agram, Data Warehouse Schemas; Star Schema, Snowflake Schema,
Factless Fact Table, Fact Constellation Schema. Update to the di-
mension tables. Major steps in ETL process, OLTP versus OLAP,
OLAP operations: Slice, Dice,
Rollup, Drilldown and Pivot.
Self-Learning Topics: Introduction to Data Warehouse
II Introduction to Data Mining, Data Exploration and Data Pre- 09
CO2
processing
Architecture, KDD process, Issues in Data Mining, Applications of
Data Mining, Data Exploration: Types of Attributes, Statistical De-
scription of Data, Data Visualization, Data Preprocessing: Descrip-
tive data summarization, Cleaning, Integration & transformation,
Data reduction, Data Discretization and Concept hierarchy genera-
tion.
Self-Learning Topics: Data Mining Task Primitives
III Classification 07 CO3
Basic Concepts, Decision Tree Induction, Naïve Bayesian
Classification,
Accuracy and Error measures, Evaluating the Accuracy of a Clas-
sifier: Holdout& Random Subsampling, Cross Validation, Boot-
strap.
Self-Learning Topics: Basic Concepts of Classification
IV Clustering 07 CO4
Partitioning Methods (k-Means, k-Medoids),Hierarchical Methods
(Agglomerative, Divisive).
Self-Learning Topics: Types of data in Cluster analysis
V Mining frequent patterns and associations 07 CO5
Market Basket Analysis, Closed Item sets, and Association Rule,
Frequent Pattern Mining, Apriori Algorithm, Association Rule Gen-
eration,Improving the Efficiency of Apriori, Mining Frequent Item-
sets without candidategeneration, Introduction to Mining Multilevel
Association Rules and Mining
Multidimensional Association Rules.
Self-Learning Topics: Frequent Item sets
VI Web Mining 06 CO6
Web Content Mining: Crawlers, Harvest System, Virtual Web View,
Personalization, Web Structure Mining: Page Rank, Clever, Web
Usage Mining.
Self-Learning Topics: Introduction to Web Mining
Total 45
DEPARTMENT OF COMPUTER ENGINEERING
List of Experiments:

Exp. No. List of Experiments

1 One case study on building Data warehouse/Data Mart
Write Detailed Problem statement and design dimensional modelling (creation of starand
snowflake schema)
2 Implementation of all dimension table and fact table based on experiment 1 case study
3 Implementation of OLAP operations: Slice, Dice, Rollup, Drilldown and Pivot based on
experiment 1 case study
4 Implementation of Bayesian algorithm
5 Implementation of Data Discretization (any one) & Visualization (any one)
6 Perform data Pre-processing task and demonstrate Classification, Clustering, Association
algorithm on data sets using data mining tool (WEKA/R tool)
7 Implementation of Clustering algorithm (K-means/K-medoids)
8 Implementation of any one Hierarchical Clustering method
9 Implementation of Association Rule Mining algorithm (Apriori)
10 Implementation of Page rank/HITS algorithm

Evaluation and Assessment Scheme:

A. Internal Assessment Examination (IAE):

Assessment consists of two class tests, each 20 marks. The IAE 1 will cover any three Course
Outcomes (COs) and IAE 2 will cover the remaining three Course Outcomes (COs). Each test
will have a duration of one hour.

B. End Semester Theory Examination (ESE):

End Semester exam of 60 Marks will be conducted based on entire syllabus.

C. Continuous Assessment (CA) :

Continuous Assessment should consist of the following
Experiments / Tutorials (8 to 10): 10 marks (All COs / LOs should be covered)
Attendance (Theory & Practical): 05 marks
Teacher Assessment Examination (TAE): 10 Marks

List of Teacher Assessment Examination (TAE):

1. Assignment
2. Case Study
3. Debate
4. Solution for Social Problems
5. Field Visit
6. Group Project
DEPARTMENT OF COMPUTER ENGINEERING
 7. Flip Classroom
8. Topic Review
9. Quiz
10. Mind Mapping
11. Any other
Note: Number of activities to be conducted under TAE would be as per the subject need.

D. Oral & Practical Exam

Based on the entire syllabus, oral (10 marks) & practical/implementation (15 marks)
examination will be conducted.

Reference Books:

1. Reema Theraja, “Data warehousing”, Oxford University Press 2009.

2. Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, Pear-
son
Publisher 2nd edition.
3. Ian H. Witten, Eibe Frank and Mark A. Hall, “Data Mining”, Morgan Kaufmann 3rd edition.

Text Books:

1. Paulraj Ponniah, “ Data Warehousing: Fundamentals for IT Professionals”, Wiley India.

2. Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 2nd edition.
3. M.H. Dunham, “Data Mining Introductory and Advanced Topics”, Pearson Education.

Useful Links

1. https://onlinecourses.nptel.ac.in/noc20_cs12/preview
2. https://www.coursera.org/specializations/data-mining

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Probabilistic Graphical Models
Semester: V Term: ODD Course Code: 24CSPEC5011
Teaching Scheme Evaluation Scheme
Credit
Contact Hrs. Total Oral/Pract/
Allotted IAE 1 IAE 2 ESE CA Total
Credit Tut.
Th Tu Pr Th Tu Pr
3 - - 3 - - 3 20 20 60 - - 100

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Course Objectives:
1. To give comprehensive introduction of probabilistic graphical models
2. To make inferences, learning, actions and decisions while applying these models
3. To introduce real-world trade-offs when using probabilistic graphical models in practice
4. To develop the knowledge and skills necessary to apply these models to solve real world problems.

Course Outcomes:
At the end of the course students will be able to:
1. Understand basic concepts of probabilistic graphical modelling.
2. Model and extract inference from various graphical models like Bayesian Networks
3. Extract inference from MarkovModels.
4. Perform learning and take actions and decisions using probabilistic graphical models
5. Represent real world problems using graphical models; design inference algorithms; and learnthe
structure of the graphical model from data.
6. Design real life applications using probabilistic graphical models.

DEPARTMENT OF COMPUTER ENGINEERING

Module Content Hours COs
I. Introduction to Probabilistic Graphical Modeling 06 CO1
 Probability Theory, Basic Concepts in Probability, Random
Variables and Joint Distribution, Independence and Condi-
tional Independence, Continuous Spaces, Expectation and Var-
iances
 Introduction to Graphs: Nodes and Edges, Subgraphs,
Paths andTrails, Cycles and Loops
 Introduction to Probabilistic Graph Models: Bayesian Net-
work,Markov Model, Hidden Markov Model Applications of
PGM

Self-Learning Topics: Introduction to Probability Theory

II. Bayesian Network Model and Inference 10 CO2
 Directed Graph Model: Bayesian Network-Exploiting Inde-
pendenceProperties, Naive Bayes Model, Bayesian Network
Model, Reasoning Patterns, Basic Independencies in Bayesian
Networks, Bayesian Network Semantics, Graphs and Distribu-
tions. Modelling:Picking variables, Picking Structure, Picking
Probabilities, D- separation
 Local Probabilistic Models: Tabular CPDs, Deterministic
CPDs,Context Specific CPDs, Generalized Linear Models.
 Exact inference variable elimination: Analysis of Com-
plexity,Variable Elimination, Conditioning, Inference with
Structured CPDs.

Self-Learning Topics: Concept of Directed Graph

III. Markov Network Model and Inference 09 CO3

 Undirected Graph Model : Markov Model-Markov Network,

Parameterization of Markov Network, Gibb's distribution, Re-
duced Markov Network, Markov Network Independencies,
FromDistributions to Graphs, Fine Grained Parameterization,
Over Parameterization
 Exact inference variable elimination: Graph Theoretic Anal-
ysis forVariable Elimination, Conditioning

Self-Learning Topics: Concept of undirected graph

IV. Hidden Markov Model and Inference 07 CO4

 HMM- Temporal Models, Template Variables and Template

Factors, Directed Probabilistic Models, Undirected Represen-
tation, Structural Uncertainty.

DEPARTMENT OF COMPUTER ENGINEERING

 Self-Learning Topics: Template Based Graph Model
V. Learning and Taking Actions and Decisions 07 CO5

 Learning Graphical Models: Density Estimation,Specific Pre-

diction Tasks, Knowledg Discovery. Learning as Optimiza-
tion: Empirical Risk, over fitting, Generalization, Evaluating
Generalization Performance, Selecting a Learning Procedure,
Goodness of fit, Learning Tasks. Parameter Estimation: Max-
imum Likelihood Estimation, MLE for Bayesian Networks
 Causality: Conditioning and Intervention, Correlation and Cau-
sation,Causal Models, Structural Causal Identifiability, Mech-
anisms and Response Variables, Learning Causal Models.
Utilities and Decisions: Maximizing Expected Utility, Utility
Curves, Utility Elicitation. Structured Decision Problems: De-
cision Tree

Self-Learning Topics: Goals of Learning

VI. Applications 06
CO6

 Application of Bayesian Networks: Classification, Forecast-

ing, Decision Making
 Application of Markov Models: Cost Effectiveness Analy-
sis,Relational Markov Model and its Applications, Application
in Portfolio Optimization
 Application of HMM: Speech Recognition, Part of Speech
Tagging, Bioinformatics.

Self-Learning Topics: Concept of Baysian

Total 45

DEPARTMENT OF COMPUTER ENGINEERING

Evaluation and Assessment Scheme:

A. Internal Assessment Examination (IAE):

Assessment consists of two class tests, each 20 marks. The IAE 1 will cover any three Course
Outcomes (COs) and IAE 2 will cover the remaining three Course Outcomes (COs). Each
test will have a duration of one hour.

B. End Semester Theory Examination (ESE):

End Semester exam of 60 Marks will be conducted based on entire syllabus.

Reference Books:

1. Finn Jensen and Thomas Nielsen, "Bayesian Networks and Decision Graphs (Information
Science and Statistics )", 2nd Edition, Springer, 2007.
2. Kevin P. Murphy, "Machine Learning: A Probabilistic Perspective", MIT Press, 2012.
3. Martin Wainwright and Michael Jordan, M., "Graphical Models, ExponentialFamilies,
and Variational Inference", 2008.

Text Books:

1. Daphne Koller and Nir Friedman, "Probabilistic Graphical Models: Principles and Tech-
niques”, Cambridge, MA: The MIT Press, 2009 (ISBN 978-0-262-0139-2).
2. David Barber, "Bayesian Reasoning and Machine Learning", Cambridge University Press,
1st edition, 2011.

Useful Links
1. https://www.coursera.org/specializations/probabilistic-graphical-models
2. https://www.mooc-list.com/tags/probabilistic-graphical-models
3. https://scholarship.claremont.edu/cgi/viewcontent.cgi?referer=https://www.google.c om/&httpsre-
dir=1&article=2690&context=cmc_theses
4. https://www.upgrad.com/blog/bayesian-networks/
5. https://www.utas.edu.au/ data/assets/pdf_file/0009/588474/TR_14_BNs_a_resource_guide.pdf
6. https://math.libretexts.org/Bookshelves/Applied_Mathematics/Book%3A_Applied_Finite_Mathe-
matics_(Sekhon_and_Bloom)/10%3A_Markov_Chains/10.02%3A_Applications_of_Mar-
kov_Chains/10.2.01%3A_Applications_of_Markov_Chains_(E xercises)
7. https://link.springer.com/chapter/10.1007/978-3-319-43742-2_24
8. https://homes.cs.washington.edu/~pedrod/papers/kdd02a.pdf
9. https://core.ac.uk/download/pdf/191938826.pdf
10. https://cs.brown.edu/research/pubs/theses/ugrad/2005/dbooksta.pdf
11. https://web.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20ap-
plications.pdf
12. https://mi.eng.cam.ac.uk/~mjfg/mjfg_NOW.pdf
13. http://bioinfo.au.tsinghua.edu.cn/member/jgu/pgm/materials/Chapter3-LocalProbabilisticModels.pdf

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Internet Programming
Semester: V Term: ODD Course Code: 24CSPEC5012
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
3 - - 3 - - 3 20 20 60 - - 100

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Course Objectives:

1. To get familiar with the basics of Internet Programming.

2. To acquire knowledge and skills for creation of web site considering both client and server-
3. side programming
4. To gain ability to develop responsive web applications
5. To explore different web extensions and web services standards
6. To learn characteristics of RIA
7. To learn React JS

Course Outcomes:

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

1. Implement interactive web page(s) using HTML and CSS.
2. Design a responsive web site using JavaScript
3. Demonstrate database connectivity using JDBC
4. Demonstrate Rich Internet Application using Ajax
5. Demonstrate and differentiate various Web Extensions.
6. Demonstrate web application using Reactive JS

DEPARTMENT OF COMPUTER ENGINEERING

Module Content Hours COs
I Introduction to Web Technology 11 CO1
Web Essentials: Basic Internet protocols, World wide web,
HTTP Request Message, HTTP Response Message, Web Cli-
ents, Web Servers
HTML5 – fundamental syntax and semantics, Tables, Lists,
Image, HTML5 control elements, Semantic elements, Drag
and Drop, Audio –Video controls
CSS3 – Inline, embedded and external style sheets – Rule
cascading, Inheritance, Backgrounds, Border Images, Colors,
Shadows, Text, Transformations, Transitions, Animation,
Basics of Bootstrap.

Self-Learning Topics: Clients, Servers and Communication,

The Internet,
II Front End Development 8 CO2
Java Script: JavaScript DOM Model- Date and Objects-Reg-
ular Expressions- Exception Handling- Validation-Built-in
objects-Event Handling, DHTML with JavaScript-
JSON introduction – Syntax – Function Files – Http Request
–SQL.

Self-Learning Topics: An introduction to JavaScript

III Back End Development 8 CO3
Servlets: Java Servlet Architecture, Servlet Life Cycle, Form
GET and POST actions, Session Handling, Understanding
Cookies, Installing and Configuring Apache Tomcat Web
Server,
Database Connectivity: JDBC perspectives, JDBC program
example
JSP: Understanding Java Server Pages, JSP Standard Tag
Library (JSTL), Creating HTML forms by embedding JSP
code.

Self-Learning Topics: Introduction to Servlet

IV Rich Internet Application (RIA) 5 CO4
Introduction to AJAX: AJAX design basics, AJAX vs Tra-
ditional Approach, Rich User Interface using Ajax, jQuery
framework with AJAX.

Self-Learning Topics: Characteristics of RIA

V Web Extension: PHP and XML 7 CO5
 XML –DTD (Document Type Definition), XML
Schema, Document Object Model, Presenting XML, Us-
ing XML Parsers: DOM and SAX, XSL- eXtensible
Stylesheet Language
 Introduction to PHP-building web applications using
PHP- tracking users, PHP and MySQL database connec-
tivity with example.
DEPARTMENT OF COMPUTER ENGINEERING
 Self-Learning Topics: Data types, control structures, built in
functions
VI React JS 6 CO6
Introduction, React features, App “Hello World”
Application, Introduction to JSX, Simple Application using
JSX.

Self-Learning Topics: Introduction to React JS

Total 45

Evaluation and Assessment Scheme:

A. Internal Assessment Examination (IAE):

Assessment consists of two class tests, each 20 marks. The IAE 1 will cover any three Course
Outcomes (COs) and IAE 2 will cover the remaining three Course Outcomes (COs). Each
test will have a duration of one hour.

B. End Semester Theory Examination (ESE):

End Semester exam of 60 Marks will be conducted based on entire syllabus.

Reference Books:
1. Harvey & Paul Deitel & Associates, Harvey Deitel and Abbey Deitel, Internet and World Wide
Web - How To Program, Fifth Edition, Pearson Education, 2011.
2. Achyut S Godbole and Atul Kahate, ―Web Technologies, Second Edition, Tata McGraw Hill,
2012.
3. Thomas A Powell, Fritz Schneider, ―JavaScript: The Complete Reference, Third Edition, Tata
McGraw Hill, 2013
4. David Flanagan, ―JavaScript: The Definitive Guide, Sixth Edition, O'Reilly Media, 2011
5. Steven Holzner ―The Complete Reference - PHP, Tata McGraw Hill, 2008
6. Mike Mcgrath―PHP & MySQL in easy Steps, Tata McGraw Hill, 2012.

Text Books:

1. Ralph Moseley, M.T. Savliya, “Developing Web Applications”, Willy India, Second
Edition, ISBN: 978-81-265-3867-6
2. “Web Technology Black Book”, Dremtech Press, First Edition, 978-7722-997
3. Robin Nixon, "Learning PHP, MySQL, JavaScript, CSS & HTML5 "Third Edition,
O'REILLY, 2014.
4. (http://www.ebooksbucket.com/uploads/itprogramming/javascript/Learning_PHP_MySQ L_Javas-
cript_CSS_HTML5 Robin_Nixon_3e.pdf)
5. Dana Moore, Raymond Budd, Edward Benson, Professional Rich Internet Applications:
6. AJAX and Beyond Wiley publications. https://ebooks-it.org/0470082801-ebook.htm
7. Alex Banks and Eve Porcello, Learning React Functional Web Development with React and Re-
dux, OREILLY, First Edition

DEPARTMENT OF COMPUTER ENGINEERING

Useful Links

1. https://books.goalkicker.com/ReactJSBook/
2. https://www.guru99.com/reactjs-tutorial.html
3. www.nptelvideos.in
4. www.w3schools.com
5. https://spoken-tutorial.org/
6. www.coursera.org

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Advance Database Management System
Semester: V Term: ODD Course Code: 24CSPEC5013
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
3 - - 3 - - 3 20 20 60 - - 100

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Course Objectives:

1. To provide insights into distributed database designing

2. To specify the various approaches used for using XML and JSON technologies.
3. To apply the concepts behind the various types of NoSQL databases and utilize it for Mongodb
4. To learn about the trends in advance databases

Course Outcomes:

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

1. Design distributed database using the various techniques for query processing
2. Measure query cost and perform distributed transaction management
3. Organize the data using XML and JSON database for better interoperability
4. Compare different types of NoSQL databases
5. Formulate NoSQL queries using Mongodb
6. Describe various trends in advance databases through temporal, graph based and spatial
based databases

DEPARTMENT OF COMPUTER ENGINEERING

Module Content Hours Cos
I Distributed Databases 4 CO1
Distributed DBMS Architecture, Data Fragmentation, Replication
and Allocation Techniques for Distributed Database Design.
Self-Learning Topics: Introduction to Distributed Databases
II Distributed Database Handling 9 CO2
 Distributed Transaction Management –architecture Distributed
Query Processing – Characterization of Query Processors,Layers/
phases of query processing.
 Distributed Concurrency Control- Taxonomy, Locking based, Basic
TO algorithm, Recovery in Distributed Databases: Failures in distrib-
uted database, 2PCand 3PC protocol.
 Self-Learning Topics: Distributed Transaction Management – Def-
inition, properties, types
III Data interoperability – XML and JSON 7 CO3
 XML Databases: Querying and Transformation: Xpath and Xquery.
 Basic JSON syntax, (Java Script Object Notation),JSON data types,
Stringifying and parsing the JSON for sending & receiving, JSON
Object retrieval using key-value pair and Jquery, XML Vs JSON
 Self-Learning Topics: Document Type Definition, XML Schema
IV NoSQL Distribution Model 11 CO4
 NoSQL database concepts: NoSQL data modeling, Benefits of
NoSQL, comparison between SQL and NoSQL database system.
 Replication and sharding, Distribution Models Consistency in dis-
tributeddata, CAP theorem, Notion of ACID Vs BASE, handling
Transactions, consistency and eventual consistency
 Types of NoSQL databases: Key-value data store, Document database
andColumn Family Data store, Comparison of NoSQL databases
w.r.t CAP theorem and ACID properties.
 Self-Learning Topics: Introduction to NoSQL
V NoSQL using MongoDB 7 CO5
 NoSQL using MongoDB: Introduction to MongoDB Shell, Running
the MongoDB shell, MongoDB client, Basic operations with Mon-
goDB shell, Basic Data Types, Arrays, Embedded Documents
 Querying MongoDB using find() functions, advanced queries using
logical operators and sorting,. MongoDB Distributed environment:
Concepts of replication and 30orizontalscaling through sharding in
MongoDB
 Self-Learning Topics: simple aggregate functions, saving and updat-
ing document
VI Trends in advance databases 7 CO6
 Temporal database: Concepts, time representation, time dimen-
sion, incorporating time in relational databases.
 Graph Database: Introduction, Features, Transactions, con-
sistency, Availability, Querying, Case Study Neo4J
 Spatial database: Introduction, data types, models, operators and
queries
 Self-Learning Topics: Concept of Temporal Database
Total 45

DEPARTMENT OF COMPUTER ENGINEERING

Evaluation and Assessment Scheme:

A. Internal Assessment Examination (IAE):

Assessment consists of two class tests, each 20 marks. The IAE 1 will cover any three Course
Outcomes (COs) and IAE 2 will cover the remaining three Course Outcomes (COs). Each test will
have a duration of one hour.

B. End Semester Theory Examination (ESE):

End Semester exam of 60 Marks will be conducted based on entire syllabus.

Reference Books:
1. Peter Rob and Carlos Coronel,Database Systems Design, Implementation and Management, Thom-
son Learning, 5thEdition.
2. Dr. P.S. Deshpande, SQL and PL/SQL for Oracle 10g, Black Book, Dreamtech Press.
3. Adam Fowler, NoSQL for dummies, John Wiley & Sons, Inc.
4. Shashank Tiwari, Professional NOSQL, John Willy & Sons. Inc
5. Raghu Ramkrishnan and Johannes Gehrke, Database Management Systems, TMH
6. MongoDB Manual : https://docs.mongodb.com/manual

Text Books:

1. Korth, Siberchatz,Sudarshan, “Database System Concepts”, 6thEdition, McGraw Hill

Elmasri and Navathe, “Fundamentals of Database Systems”, 5thEdition, Pearson Education
2. Ozsu, M. Tamer, Valduriez, Patrick, “Principles of distributed database systems”,3rd Edition,
Pearson Education, Inc.
3. PramodSadalge, Martin Fowler, NoSQL Distilled: A Brief Guide to the Emerging World of
Polyglot Persistence, Addison Wesely/ Pearson
4. Jeff Friesen , Java XML and JSON,Second Edition, 2019, après Inc.

Useful Links
1. https://cassandra.apache.org
2. https://www.mongodb.com
3. https://riak.com
4. https://neo4j.com
5. https://martinfowler.com/articles/nosql-intro-original.pdf

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Corporate Communication & Employability Skills – I (CCES – I)
Semester: V Term: ODD Course Code: 24CSAEC501
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
- - 2 - - 1 01 -- -- -- 25 -- 25

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Course Objectives:
1. To understand and apply LSRW skills in academic, professional and social situations.
2. To apply grammar and vocabulary correctly in oral and written communication situations.
3. To equip students with the techniques of inter-personal skills and employability skills for their
personal and organizational development.

Course Outcomes:
At the end of the course students will be able to:
1. Demonstrate effective inter-personal skills, such as active listening, empathy, and conflict resolu-
tion, to build and maintain positive relationships.
2. Use punctuation, syntax, and other language conventions correctly to produce polished and pro-
fessional documents.
3. Develop employability skills, including teamwork, leadership, problem-solving, and adaptability,
to thrive in the workplace.

DEPARTMENT OF COMPUTER ENGINEERING

Module Contents Hours COs
I Developing Speaking & Presentation Skills 02 CO1
1) Formal & Informal: Situational Dialogues
2) Elements of Speaking Skills: Pronunciation, Fluency,
Tone
3) Types of Speech: Welcome Speech, Introductory Speech,
Impromptu, Public Speech, Farewell Speech, Vote of
Thanks, Eulogy & Condolence Speech. Tips for enhanc-
ing speeches.
Self-Learning Topics: Elements of Speaking Skills:
Vocabulary, Grammar,
II Verbal Aptitude on Grammar & Vocabulary 02 CO2
Grammar
1) Parts of Speech
2) Modal Auxiliaries- Primary & Secondary

Vocabulary
1) Synonyms, Antonyms, Prefixes, Suffixes
2) Homophones, Homonyms, Acronyms
Self-Learning Topics: Articles: Definite & Indefinite,
Exceptions of using articles
Uses of Tenses: Present & Past Tense

III Writing Skills 03 CO2

1) Email Writing: Etiquette, Netiquette, Do’s & Don’ts
2) Writing Notice, Agenda & Minutes of a Meeting
3) Introduction to Proposal Writing, Types of Proposal: Re-
search, Business
4) Writing Article Review
Self-Learning Topics: Parts of a Proposal
IV
Interpersonal Skills 04 CO3
1) Presentation Skills: Demo Presentation
2) Managerial Skills: Time Management, Goal Setting, De-
cision Making, Conflict Resolution, Team Building,
Leadership, Emotional Intelligence, Critical Thinking,
Assertiveness, Negotiation
Self-Learning Topics: Presentation Skills: Power Point
V Employability Skills 04 CO3

DEPARTMENT OF COMPUTER ENGINEERING

 1) SWOT Analysis: Personal & Organizational
2) Verbal Aptitude Test
3) Group Discussion Skills: Do’s and Don’ts, Tips for crack-
ing a GD
4) Resume Writing
5) Interview Techniques
Self-Learning Topics: Types of GD
Total 15

Tut No. List of Tutorials Hours CO

Listening Skills
1 1. IELTS Listening 1 1

Speaking Skills
2 1.Speech: Welcome/Farewell/Vote of Thanks 1 1
3 2.Technical Poster Presentation 1 1
4 3.Book Review 1 1
5 4.Newspaper Article Presentation 1 2
6 5.Group Discussion (2 rounds) 2 3
7 6.Mock Interview (HR Round ) 1 3
8 7. Mock Interview (HR + Technical 2Hours) 2 3

Reading Skills
9 1.Newspaper Article 1 1
Writing Skills
10 1.Email Writing 1 2
11 2.Writing & Presenting Article Review 1 2
12 3. Notice, Agenda & Minutes 1 2

DEPARTMENT OF COMPUTER ENGINEERING

Evaluation and Assessment Scheme:

TERM WORK: 25 Marks

Assignment + Attendance= 5 + 5=10 Marks

Technical Poster Presentation = 05 Marks
IELTS Listening + Newspaper Article Presentation = 2.5 + 2.5 = 05 Marks
Speech Welcome/VOT/Farewell = 05 Marks

Reference Books:
1. M Ashraf Rizvi, Effective Technical Communication, Tata McGraw Hill, 2008
2. Gadyalji Vaishali K, Communication Skills, Nandu Publications, 2010
3. Rai Urmila & Rai S.M, Business Communication, Himalaya Publishing House, 2007
4. Rai Urmila & Rai S.M, Business Communication, Himalaya Publishing House, 2008
5. Raman Meenakshi & Sharma Sangeeta, Technical Communication Principles and Practice, Oxford
University Press, 2015
6. Raman Meenakshi & Singh Prakash, Business Communication, Oxford University Press, 2008
7. Locker O Kitty & Kaczmarek Kyo Stephen, Business Communication Building Critical Skills,
McGraw Hill Education Private Limited, 2007
8. Chaturvedi P D & Chaturvedi Mukesh, Business Communication Concepts, Cases and Applica-
tions, Pearson Education, 2008

Text Books:

Luthans Fred, Organizational Behavior An Evidence-Based Approach, McGraw Hill Education Private
Limited, 2013

Useful Links:

1. https://youtu.be/-x125fNrFxM?si=StDEUJZSDbC2wVxc
2. https://youtu.be/aD6sBAsYnYE?si=LNMXzC89QCvYa0mh
3. https://youtu.be/aD6sBAsYnYE?si=Ixc9FOdV0WBzQTRg
4. https://youtu.be/mQL3aZa21EY?si=4cqWLxgAnSv6hkFC
5. https://youtu.be/V2azCSchs58?si=j3uzd-Wsl8DReuPW
6. https://youtu.be/3w32jIsRlsw?si=v9t3VYNEv-bezxLG

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Employability Enhancement Program (Technical)
Semester: V Term: ODD Course Code: 24CSVSE501
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
- - 4 - - 2 02 -- -- -- 25 -- 25

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment

Category of Technology Sub - Topic Hands-On Project/Assignment

Python History and Introduction of Python. Practical Programs machine test of
Application of Python Features of each topics.
Python.
Flavours of Python, Data types, Practice on Hacker rank and infytq
Identifier company based questions
Reserve Keywords, PVM, String
Slicing Typecasting, String Func-
tion
Python (Logic Building) Collection Data types: List, Tuple, Practical programs machine test of
Set, frozen set, Dictionary, Opera- each topics.
tors, Conditional statements, simple
if, if else, nested if else Practice on Hacker rank and infytq
company based questions
Python (Logic Building) Looping Statement: for loop, while Practical programs machine test of
and Library loop, nested for loop, Transfer state- each topics.
ment, break & continue, Numpy, Practice on Hacker rank and infytq
Pandas company based questions
Python ( functional and ) Types of Function, types of argu- Practical Programs machine test of
conceptual Implementa- ments, default, keyword, and posi- each topics.
tion tional, variable length argument
lambda function. Generator fuction, Practice on Hacker rank and infytq
Decorster, Predefine and User de- company based questions
fine module concept, Package con-
cept.

DEPARTMENT OF COMPUTER ENGINEERING

 Course Title: Skill Based Lab with Mini Project
Semester: V Term: ODD Course Code: 24CSVSE501
Teaching Scheme Evaluation Scheme
Credit Total Oral/Pract/
Contact Hrs. IAE 1 IAE 2 ESE CA Total
Allotted Credit Tut.
Th Tu Pr Th Tu Pr
- - 2 - - 1 1 - - - 25 25 50

IAE: Internal Assessment Examination

ESE: End Semester Examination
CA: Continuous Assessment,

Course Objectives:

1. To understand and identify the problem

2. To apply basic engineering fundamentals and attempt to find solutions to the problems.
3. Identify, analyze, formulate and handle programming projects with a comprehensive and systematic
approach.
4. To develop communication skills and improve teamwork amongst group members and inculcate the
process of self-learning and research.

Course Outcomes:

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

1. Identify societal/research/innovation/entrepreneurship problems through appropriate literature sur-
veys
2. Identify Methodology for solving above problem and apply engineering knowledge and skills to solve
it
3. Validate, Verify the results using test cases/benchmark data / theoretical / inferences / experiments /
simulations
4. Analyze and evaluate the impact of solution/product/research/innovation /entrepreneurship towards
societal / environmental / sustainable development
5. Use standard norms of engineering practices and project management principles during project work
6. Communicate through technical report writing and oral presentation.
 The work may result in research/white paper/ article/blog writing and publication
 The work may result in business plan for entrepreneurship product created
 The work may result in patent filing.
7. Gain technical competency towards participation in Competitions, Hackathons, etc.
8. Demonstrate capabilities of self-learning, leading to lifelong learning.
9. Develop interpersonal skills to work as a member of a group or as leader.

DEPARTMENT OF COMPUTER ENGINEERING

 Mini Project shall be assessed based on following points

1 Clarity of problem and quality of literature Survey for problem identification

2 Requirement Gathering via SRS/ Feasibility Study

3 Completeness of methodology implemented

4 Design, Analysis and Further Plan

5 Novelty, Originality or Innovativeness of project

6 Societal / Research impact

7 Effective use of skill set : Standard engineering practices and Project management
standard

8 Contribution of an individual’s as member or leader

9 Clarity in written and oral communication

10 Verification and validation of the solution/ Test Cases

11 Full functioning of working model as per stated requirements

12 Technical writing /competition/hackathon outcome being met

Evaluation and Assessment Scheme:

Continuous Assessment (CA): 25 Marks

1. Marks awarded by guide/supervisor based on logbook: 10 marks
2. Marks awarded by review committee: 10 Marks
3. Quality of Project Report: 05 Marks

Oral/Practical Examination: 25 Marks

Evaluation is based on the project demonstration and presentation assessed by expert.

DEPARTMENT OF COMPUTER ENGINEERING