Faculty of Science and Technology

Savitribai Phule Pune University, Pune

NEP – 2020 Complaint Curriculum

Draft copy of Syllabus

for
S.Y. Bachelor of Computer Applications (BCA)
(2024 Pattern)

(with effect from A. Y. 2025-26)

1
 Preamble
Dear Students, teachers and all stakeholders

The field of computing is rapidly expanding and changing, especially, since the last decade with continuous
emergence of new disruptive technologies such as artificial intelligence, data science, cyber security, Internet
of things, robotics and so on.
21st Century has witnessed rapid technological developments in every sector including the field of
Computing. Moreover, it has created new job roles and massive job opportunities for budding graduates.
Premium Institutes, public and private Universities, autonomous and affiliated colleges in India have always
played a crucial role in producing human resources with required skill sets by capturing and monitoring these
developments and offered various UG and PG programmes.
The Savitribai Phule Pune University, Pune has made its significant contribution by offering degree
programmes as per the trends from time to time. In the year 1989, it started offering a degree programme
Bachelor of Computer Science (BCS), now called B. Sc. (Computer Science) and was its unique offering in
the state of Maharashtra. Later the University offered undergraduate and graduate programmes such as
Master of Computer Management (MCM), B. Sc. (Computer Applications) and Bachelor of Computer
Applications (BCA), Master of Computer Applications (MCA), M. Sc (Computer Science), M. Sc. (Computer
Applications) etc.
The Savitribai Phule Pune University, Pune has taken a leading role in design and implementation of
Programmes as per the guidelines and recommendations of National Education Policy (NEP) 2020. The
university decided to offer UG and PG programmes with features recommended by NEP-2020 such as
Multiple-entry/exit, inter and multi-disciplinary education, focus on skilling, on-job training/field projects,
research, incorporation of Indian Knowledge System etc. for the holistic development of students.
The university has adopted the guidelines provided by the state Sukanu Samittee and prepared the credit
structure for this UG programmes. The detailed draft for FY BCA was implemented from June 2024. This
document provides detailed draft for SY BCA which will be implemented from June 2025.
The Ad-hoc Board of Studies in Computer Applications has prepared a structure for BCA with following
features
• The structure of the course is designed as per National Education Policy (NEP) 2020 and is in line
with university guidelines.
• The total credits offered for the three years with six semesters are 132 credits with 22 credits
assigned for each of the six semesters. Candidate has an option to continue with fourth year either
for Hon. with research or Hon. degree, each with 176 credits
• The programme has Multiple Entry/exit feature: A candidate may exit the programme after first,
second, third or fourth year and shall be awarded with UG Certification, UG Diploma, Degree and
Hon. Degree with Research / Hon. Degree respectively
• Various types of courses include – Major Core (MJ), Mandatory Elective (ME), Open Electives (OE),
Minor (MN), Ability Enhancement (AEC), Value education (VEC), Vocational Skill (VSC), Skill
enhancement (SEC), Indian Knowledge System (IKS), Co-curricular (CC) courses as well as
courses on On-job Training (OJT), Field Project (FP), Community Engagement Programmes (CEP),
Research Methodology (RM) and Research Project (RP).

I am thankful to Hon. Vice-Chancellor Prof. Dr. S W. Gosavi, Hon. Pro-Vice Chancellor Prof. Dr. Parag Kalkar,
Hon. Dean of FoS&T, Prof. Dr. P D Patil for their guidance. I am thankful to all board members Dr. A B
Nimbalkar, Dr. Razak Sayyad, Prof. Dr. R M Sonar and Prof. Dr. Sachin A. Kadam and all members of
previous BoS for their valuable inputs as well as the teachers from affiliated colleges for their active
participation in preparing the draft syllabus for SY BCA.

Prof. Dr. S. S. Sane

Chairman,
Ad-hoc Board of Studies in Computer Applications
Faculty of Science and Technology, SPPU, Pune

2
 Programme Outcomes
After successful completion of the Programme, the students shall be able to

PO 01: Demonstrate understanding of fundamental concepts in the field of Computing

PO 02: Design and develop computer-based applications.

PO 03: Analyze existing research reported in the literature

PO 04: Propose alternate solutions by undertaking research work.

PO 05: Create efficient, reliable, readable and maintainable code.

PO 06: Demonstrate a deeper understanding of the chosen domain.

PO 07: Select appropriate method/algorithm to solve the given problem

PO 08: Explain complex technical concepts clearly and effectively, both in written and oral
forms.

PO 09: Demonstrate ability to collaborate effectively with team members, understand

different perspectives, and contribute productively to become successful
professional.

PO 10: Demonstrate ability to work with integrity and a sense of social responsibility.

PO 11: Demonstrate self and life-long learning skills

PO 12: Solve computational problems innovatively

PO 13: Apply knowledge gained and critical thinking to develop real-world applications.

3
 Table of Contents

Sr. Description Page

No. Number
1. Structure of S. Y. Bachelor of Computer Applications 5-6
(BCA)
2. Course Drafts for Courses at SEM III 7 - 18

3. Course Drafts for Courses at SEM IV 19 – 30

4. List of Minors Courses and Open Electives Courses

offered by the BOS in Computer Applications to other BOS 31
under FoS&T / Faculties
5. Detailed drafts of Minor Courses (For SEM III & IV only) 32 – 40
offered by the BOS in Computer Applications to other BOS
under FoS&T / Faculties
6. Detailed drafts of Open Elective Courses (For SEM III & IV 41 – 45
only) offered by the BOS in Computer Applications to other
Faculties
7. Abbreviations 46

8. Details of Task Force 47 - 49

4
 Structure of SY Bachelor of Computer Applications
Level 5.0 Semester - III

Course Course Course Name Teaching Examination Credits

Code Type Scheme Scheme and
Hrs/Week Marks
TH TU PR CE EE Total TH TU PR Total
CA-201-
Data Structures 04 -- -- 30 70 100 04 -- -- 04
MJ
MJ
CA-202- Lab course on CA-
-- -- 04 15 35 50 -- -- 02 02
MJP 201 -MJ
CA-221 -
VSC C++ Programming -- -- 04 15 35 50 -- -- 02 02
VSC
CA-231-
FP Field Work -- -- 04 15 35 50 -- -- 02 02
FP

ELS- Data
02 -- -- 15 35 50 02 -- -- 02
241-MN Communications
ELS- MN
Lab Course on CA -
242- -- -- 04 15 35 50 -- -- 02 02
241 –MN
MNP
Course from
GE/ OE 02 -- -- 15 35 50 02 -- -- 02
University Basket
CA-200 Indian Knowledge
IKS 02 -- -- 15 35 50 02 -- -- 02
-IKS System for Computing
Course from
AEC 02 -- -- 15 35 50 02 -- -- 02
University Basket
Course from
CC 02 -- -- 15 35 50 02 -- -- 02
University Basket
Total 14 00 16 165 385 550 14 00 08 22

5
 Structure of SY Bachelor of Computer Applications
Level 5.0 (SY) Semester – IV

Course Course Course Name Teaching Examination Credits

Code Type Scheme Scheme and
Hrs/Week Marks
TH TU PR CE EE Total TH TU PR Total

CA- Database Management

04 -- -- 30 70 100 04 -- -- 04
251-MJ Systems
MJ
CA-
Lab course on CA-251
252- -- -- 04 15 35 50 -- -- 02 02
–MJ
MJP
CA-
271- VSC Python Programming -- -- 04 15 35 50 -- -- 02 02
VSC
CA-281
CEP Community Services -- -- 04 15 35 50 -- -- 02 02
CEP

ELS-
Communication
291 - 02 -- -- 15 35 50 02 -- -- 02
Networks
MN
MN
ELS-
Lab course on CA -291
292 - -- -- 04 15 35 50 -- -- 02 02
–MN
MNP
Course from University
GE/ OE -- -- 04 15 35 50 -- -- 02 02
Basket
SEC- Spreadsheet
SEC -- -- 04 15 35 50 -- -- 02 02
251-CA Applications
Course from University
AEC 02 -- -- 15 35 50 02 -- -- 02
Basket
Course from University
CC 02 -- -- 15 35 50 02 -- -- 02
Basket
Total 10 00 24 165 385 550 10 00 12 22

Exit option: Award of UG Diploma in Bachelor of Computer Applications (BCA) with 88 credits and
an additional 4 credits (for either a course by Microsoft/CCNA/Salesforce/Google/AWS/Oracle/
RedHat etc. or Swayam/ NPTEL/MKCL MOOC course equivalent to core NSQF course or an
internship) or else Continue with Major and Minor

6
Detailed Drafts
For
Level 5.0 (SY)

SEMESTER III

7
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA – 201 - MJ: Data Structures
Teaching Scheme: Credits Examination Scheme:
Theory: 04 Hrs./Week 04 Continuous Evaluation: 30 Marks
End-Semester: 70 Marks
Course Objectives:
1. To study various data structures
2. To learn analysis of algorithms
3. To understand real-world applications of data structures.
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Define various data structures and notations for algorithm analysis
CO2: Design algorithms using suitable data structure(s)
CO3: Compare various representations of a stack, queue, tree and graph
CO4: List real world applications of stacks, queues, trees and graphs
CO5: Apply appropriate data structure(s) to solve a given problem
CO6: Evaluate the time and space complexity of the given algorithm/program

Course Contents
Unit I Introduction to Data Structure 10 Hrs.
1.1 Introduction, Basic concepts, Data types and data objects.
1.2 Abstract Data Types (ADT)
1.3 Types of Data Structures: Linear and non -linear
1.4 Algorithm analysis: Frequency counts, Space and Time complexity, Asymptotic
notation: Big O, Omega (Ω) (With examples)
Unit II Arrays 10 Hrs.
2.1 Introduction
2.2 Matrix representation using arrays: Row and column major, operations on matrices,
Sparse Matrix
2.3 Sorting techniques with time complexity: Bubble sort, Insertion sort, Merge sort, Quick
sort
2.4 Searching techniques with time Complexity: Linear search and Binary search
Unit III Linked Lists 10 Hrs.
3.1 Introduction
3.2 Representation
3.3 Types of linked lists: Singly, Doubly, Circular (Singly, Doubly)
3.4 Operations on link list: Create, Display, Insert, Delete, Reverse, Search, Sort,

8
 Concatenation, Merge
3.5 Real world applications of Link list : Polynomial Representation, Addition of two
polynomials
Unit IV Stacks and Queues 10 Hrs.
4.1 Introduction
4.2 Representation of Stack: Using arrays and Linked Lists
4.3 Operations on stack: push, pop
4.4 Applications of Stack: Recursion, Expressions: Infix to postfix, postfix to infix
4.5 Representation of Queues: Static (Array) and Dynamic (Linked List)
4.6 Operations on queue: insert, delete
4.7 Types of queues: Circular queue and Priority queue
4.8 Real world Applications of queue (Implementation not expected)

Unit V Trees 10 Hrs.

5.1 Introduction and terminologies
5.2 Types of Binary Trees -Rooted Binary Tree, Full Binary Tree, Complete Binary Tree and
Skewed Binary Tree, Expression Tree
5.3 Representation of Trees using arrays and linked lists, traversals (In-order, pre-order,
post-order) and operations (Create, Insert, delete, modify, counting nodes etc.)
5.5 Applications of Binary trees – Expression conversions and evaluation
5.6 Binary Search Tree (BST): Introduction and Definition, operations on BST (Create, insert
node, delete node, search node) and applications
5.5.2 AVL Tree: Concept, Rotation (LL, LR, RL, RR) with Examples
5.5.3 Heap Sort Technique with Examples (Implementation not expected)

Unit VI Graphs 10 Hrs.

6.1 Introduction and Graph terminologies
6.2 Representation of a Graph -
6.2.1 Adjacency matrix
6.2.2 Adjacency list
6.2.3 Adjacency multi-list
6.3 Graph Traversals -
6.3.1 DFS (Depth First Search)
6.3.2 BFS (Breadth First Search)
6.4 Applications of graphs -
6.4.1 Topological sort
6.5 Minimal Spanning Trees -
6.5.1 Prim’s Algorithm
6.5.2 Kruskal’s Algorithm

9
Books
1. Horowitz, Ellis and Sahani Sartaj, “Fundamentals of Data Structures”,1st Edition,
Galgotia,1984
2. Kamthane, Ashok N., “Introduction to Data Structures using C”,1st Edition, Pearson,2004
3. Bandopadhya, S. K. and Dey, K. S. “Data Structures using C”, 1st Edition, Pearson,
2004
4. Srivastava, S. K. and Srivastava, D., “Data Structures using C”,1st Edition, BPB
Publication, 2004
5. Gilberg, Richard F. and Forouzan, Behrouz A., “Data Structures: A Pseudocode
approach with C”, 2nd Edition, Cengage Learning, 2007
6. Steven S. S, “The Algorithm Design Manual”, 2nd Edition, Springer, 2008

10
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA - 202 - MJP: Lab course on CA – 201 - MJ
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./Week/ Batch 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To understand algorithms and analysis of algorithms
2. To learn static and dynamic data structures.
Course Outcomes: After successful completion of this course, learner will be able to
CO1: Apply appropriate data structures to solve the given problem
CO2: Design an efficient algorithm for the given problem and implement
CO3: Determine the time and space complexity of a given algorithm
Guidelines for Instructor's Manual
The instructor shall prepare instructor’s manual consisting of university syllabus, conduction
and Assessment guidelines.
Guidelines for Student Journal
The student shall perform each laboratory assignment and submit the same in the form of a
journal. Journal shall have a Certificate, table of contents, and handwritten write-up of each
assignment (Title, Objectives, Problem Statement, Program Outputs, software and
Hardware requirements, Date of Completion, Assessment grade/marks and signature of the
instructor).
Guidelines for Assessment
The instructor shall carry out internal evaluation of laboratory assignments of 15 marks on a
continuous basis throughout the semester. For each lab assignment, the instructor shall
assign grade/marks based on parameters with appropriate weightage. Suggested
parameters include-timely completion, performance, innovation, efficient codes, code
documentation, punctuality and neatness of the write-up etc.

A pair of examiners shall conduct end semester examination of 35 marks in the form of
practical examination based on journal assignments. Examiners shall ask questions about
journal assignments and / or problem statement provided during practical examination to
judge understanding of concepts by the students.

11
Assignment List of Assignments Number of
Nos Hrs.
1 Non-Recursive Sorting Techniques 4
• Bubble Sort
• Insertion Sort
2 Recursive Sorting Techniques 6
• Quick Sort
• Merge Sort
3 Searching Techniques 2
• Linear search
• Binary search
4 Linked List 12
• Implementation of Linked List, Singly Circular Linked
List, Doubly Linked List, Doubly Circular Linked List,
operations
5 Stacks and Queues 12
• Static Stack Implementation and operations
• Dynamic Stack Implementation
• Applications of Stack -Expression Conversions
• Static Queue Implementation and operations
• Dynamic Queue Implementation
6 Binary Trees and Binary Search Tree (Dynamic) 12
• Operations on Binary trees – Traversing, level wise
printing of nodes, counting total nodes, compute depth,
Insert, Delete and search node
• BST-create, traverse, count total nodes, Insert,
Delete and search node
7 Graphs 12
• Adjacency Matrix Representation
• Adjacency List Representation
• In-degree and Out-degree calculation
• BFS, DFS Implementation

12
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA - 221 - VSC: C++ Programing
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./ Week / 02 Continuous Evaluation: 15 Marks
Batch End-Semester: 35 Marks
Course Objectives:
1. To understand Object Oriented Programming concepts using the C++.
2. To study principles of data abstraction, inheritance and polymorphism.
3. To learn Virtual functions and polymorphism.
4. To know Formatted I/O and unformatted I/O.
Course Outcomes: After successful completion of this course, the learners will be able to,
CO1: Compare the procedural and object-oriented paradigms
CO2: Use Classes, Objects, constructors, destructors etc.
CO3: Illustrate the concept of function overloading, operator overloading, inheritance, virtual
functions and polymorphism.
CO4: Apply exception handling
CO5: Demonstrate use of various OOPs concepts with the help of programs
Guidelines for Instructor's Manual
The instructor shall prepare instructor’s manual consisting of university syllabus, conduction
and Assessment guidelines.
Guidelines for Student Journal
The student shall perform each laboratory assignment and submit the same in the form of a
journal. Journal shall have a Certificate, table of contents, and handwritten write-up of each
assignment (Title, Objectives, Problem Statement, Program Outputs, software and Hardware
requirements, Date of Completion, Assessment grade/marks and signature of the instructor).
Guidelines for Assessment
The instructor shall carry out internal evaluation of laboratory assignments of 15 marks
throughout the semester. For each lab assignment, the instructor shall assign grade/marks
based on parameters with appropriate weightage. Suggested parameters include-timely
completion, performance, innovation, efficient codes, code documentation, punctuality and
neatness of the write-up etc.

A pair of examiners shall conduct end semester examination of 35 marks in the form of
practical examination based on journal assignments. Examiners shall ask questions about
journal assignments and / or problem statement provided during practical examination to
judge understanding of concepts by the students.
List of Assignments
The instructor shall cover necessary theoretical concepts in object-oriented programming
such as objects, classes, data abstraction, encapsulation, data members, methods, access
specifiers, inheritance, polymorphism, operator and function overloading, abstract classes,
virtual function, file and exception handling etc.

13
Topic Name Object Oriented Programming and functions in C++ 20 Hrs.
Assignment No 1: Creation of classes, objects, methods, access specifiers, input-output
Assignment No 2: Scope resolution operator, static members, call by reference
Assignment No 3: Inline function, friend class and function.
Topic Name Inheritance and Polymorphism 20 Hrs.
Assignment No 4: Constructor and destructor
Assignment No 5: Single inheritance and multiple inheritance
Assignment No 6: Multilevel inheritance and Hierarchical Inheritance, Hybrid inheritance
Assignment No 7: Polymorphism (Function overloading)
Assignment No 8: Polymorphism (Operator overloading)
Topic Name File Handing and Exception Handling 20 Hrs.
Assignment No. 9: Operations on files (Read, Write, Open, Close), Random Access file
functions
Assignment No 10: Exception handling
Assignment No.11: Hash tables and Dictionaries
Books
1. B. Stroutstrup, “The C++ Programming Language”, 3rd Edition, Pearson Education,
2000.
2. T. Gaddis, J. Walters and G. Muganda, “OOP in C++”, 7th Edition, Pearson Education,
2010.
3. R. Lafore, “Object Oriented Programming in C++”, 3rd Edition, Galgotia Publications
Pvt. Ltd, 2004.
4. Herbert Schildt, “The Complete Reference C++”, 4th Edition, Tata McGraw Hill, 2014.
5. Walter Savitch, “Problem solving with C++: The Object of Programming, 4 th Edition,
Pearson Education, 2002.

14
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA - 231 - FP: Field work
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./ Week 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To provide exposure to the students and sensitize them to field issues/problems
2. To understand methodology used to perform field work
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Apply methodology to perform field work
CO2: Identify and define real-world issues or problems
CO3: Analyze the data collected and propose solution to solve real-world problem
Guidelines for the faculty
A faculty shall be assigned as a guide for each group of 3 / 4 students.
The guide assigned for each group shall assist the assigned student group(s) for identifying topic/area
(topic list is provided below for reference) for the field work, objectives and outcomes, preparation of
questionnaire, resources/tools needed and guide the students for possible solutions and report
preparation
The guide assigned for each group shall monitor, track and assess the progress of work carried out
by students throughout the semester
Guidelines for Students
The student shall work in a group of 3 or 4 students. Each group shall select topic/area for the fieldwork
to be undertaken by them in consultation with their assigned guide.
The group shall discuss and decide objectives, outcomes, overall plan for fieldwork, methodology to
be adopted, such as preparation of a questionnaire for conduction of survey or methods for data
gathering, tools to be used for analysis etc. and get the plan approved from their guide.
Each group shall carry out fieldwork during their free slots, or before/after college hours or on Sundays
or holidays. The students shall maintain a diary giving details of tasks performed by them,
observations/study notes etc.
The suggested timelines for the field work are
• Formation of group – 1 week
• Selection of topic for field study – 2 Week
• Discussions and finalization of objectives, outcomes and methodology to be used – 3
Weeks
• Field work and visits, SWOT/SWOC analysis, group discussions and meeting with guide
– Conduction of survey / gathering data etc. – 4 Weeks
• Preparation of report and presentation – 2 weeks
Each group shall submit a report at the end of the semester consisting of Title, Abstract, Rational of
the study, problem definition, objectives, outcomes, methodology used, details of field work
performed (Field Visits, Interviews, discussions etc.), analysis, SWOT/SWOC, findings, details
of proposed solution (Paper design/prototype/mobile App etc.) and conclusions. Students
should also submit geo-tagged photographs, audio-video clips etc.

15
 Guidelines for Assessment
The instructor shall carry out internal evaluation of fieldwork for 15 marks throughout the
semester based on timely completion of the work, analysis, findings and neatness of the report
etc.

The end semester examination of 35 marks shall be based on Group presentation and the
reports of fieldwork submitted in the journal.
List of suggested topics/areas for Field work (but not limited to)
1. Healthcare (Civil and private hospitals) – HIMS, Telemedicine etc.
2. Schools, colleges, Universities - e-Learning Platforms, MOOCs, ERP, IT Infrastructure
and Security systems etc.
3. Agriculture - Use of IoT Devices, drones in Agriculture, Management of Water
Distribution, etc.
4. Old age homes and organizations working of differently abled people - Assistive
Technologies for Divyanga Personnel, Support for Senior Citizens etc.
5. Organizations/NGOs working on food habits, nutrition, adulterations
6. Urban Region - Smart Cities, Traffic Management, Renewable energy and Solar
Systems, Waste collection and disposal, studying water quality and water supply
system of the city etc.
7. Rural Region - Smart Villages, Agriculture Product Distribution Systems etc.
8. Government offices and offices of Local Bodies (Corporation/Municipal Corporation/
Grampanchayat - ERP, IT Infrastructure and Security etc.
9. Pollution control boards – study / develop a system to monitor City environmental
parameters - Air/Sound/Water pollutions
10. Department of disaster Management – Study /develop response system for
allocating resources during natural disasters.
11. Governance - e-Governance Portals, Online Payment Systems etc.
12. Industries (IT/Manufacturing/Telecomm) involved in development of solutions to solve
social issues
BOOKS
1. Waterman, A. Service-Learning: A Guide to Planning, Implementing, and
Assessing Student Projects. Routledge, 1997.
2. Beckman, M., and Long, J. F. Community-Based Research: Teaching for
Community Impact. Stylus Publishing, 2016.
3. Design Thinking for Social Innovation. IDEO Press, 2015.
4. Dostilio, L. D., et al. The Community Engagement Professional’s Guidebook: A
Companion to The Community Engagement Professional in Higher Education. Stylus
Publishing, 2017

16
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA – 200 – IKS: Indian Knowledge System for Computing
Teaching Scheme: Credits Examination Scheme:
Theory: 02 Hrs./Week 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To study contributions of Indian scholars to computation and logic.
2. To understand Indian methods for Number representations
3. To know use of Sanskrit in Natural language processing
4. To learn ancient cryptography techniques
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: List India’s contributions to Computing
CO2: Apply Ancient Indian Mathematical concepts in Computing
CO3: Utilize Linguistic and Computational aspects of Sanskrit from IKS in Modern Computing
CO4: Describe Cryptographic techniques from IKS
CO5: Make use of Cybersecurity techniques from IKS
CO6: Illustrate the Role of IKS in Emerging Technologies
Course Contents
Unit I Introduction to Indian Knowledge Systems (IKS) 05Hrs.
1.1 Introduction IKS
1.2 Defining Indian Knowledge System (IKS) and its components,
1.3 Contribution of Aryabhata and Brahmagupta, Buddhist logico-epistemology
1.4 The knowledge triangle
1.5 Prameya -A vaiśeṣikan approach to physical reality
1.6 Dravyas -the constituents of the physical reality
1.7 Attributes -the properties of substances and Action -the driver of conjunction and
disjunction
1.8 sāmānya, viśēṣa, samavāya
1.9 Pramāṇa -the means of valid knowledge
1.10 Samsaya-ambiguities in existing knowledge.
Unit II Number Systems and Units of Measurement 12 Hrs.
2.1 Number systems in India -Historical evidence
2.2 Salient Features of the Indian Numeral System
2.2.1 Concept of zero and its importance,
2.2.2 Large numbers and their representation
2.2.3 Place Value of Numerals
2.2.4 Decimal System
2.3 Unique approaches to represent Numbers
2.3.1 Bhūta-Saṃkhyā system

17
 2.3.2 Śūnyabindu System
2.3.3. Piṅgala and the Binary system
2.4. Measurements for time, distance, and weight in ancient India
Unit III Linguistics 08 Hrs.
3.1 Introduction to Linguistics
3.2 Aṣṭādhyāyī
3.3 Phonetics
3.4 Word generation
3.5 Computational aspects
3.6 Mnemonics
3.7 Recursive operations -Introduction to use of Kaprekar Constant 6174 in recursion
3.8 Rule based operations
3.9 Sentence formation
3.10 Verbs and prefixes
3.11 Role of Sanskrit in natural language processing
Unit IV Ancient Cryptography and Security Systems 05 Hrs.
4.1 The Evolution of India’s Intelligence Culture-Kautilya’s Discourse on Secret
Intelligence in the Arthashastra
4.2 Kaṭapayādi system
4.3 Steganography in Kautilya’s Arthashastra
4.4 Cryptographic methods in ancient Indian texts
4.5 Relevance to modern-day cybersecurity and encryption
4.6 Introduction to use of Kaprekar Constant (6174) in cryptography
Books
1. B. Mahadevan, Vinayak Rajat Bhat, and R.N. Nagendra Pavana, “Introduction to Indian
Knowledge System: Concepts and Applications”, PHI Learning, 2022.
2. Dee Hetvik, “Ancient Indian encryption: KaTaPaYadi system”, Kindle Edition
3. https://www.geeksforgeeks.org/kaprekar-constant/

18
Detailed Drafts
For
Level 5.0 (SY)

SEMESTER IV

19
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA - 251- MJ: Database Management Systems
Teaching Scheme: Credits Examination Scheme:
Theory: 04 Hrs./Week 04 Continuous Evaluation: 30 Marks
End-Semester: 70 Marks
Course Objectives:
1. To understand the fundamental concepts of Relational database management systems
2. To study and understand systematic approaches for design of database systems
3. To learn SQL - the database Query language
4. To know about transaction management and data security
Course Outcomes: After successful completion of this course, learner will be able to
CO1: Solve real world problems using appropriate relational data model.
CO2: Construct E-R Model for given requirements and convert it into database tables.
CO3: Write efficient SQL queries and use PL/SQL
CO4: Apply database management operations
CO5: Describe mechanisms for transaction management
CO6: Demonstrate understanding of database security
Unit I Introduction 06 Hrs.
1.1 Introduction to DBMS
1.2 File system Vs. DBMS
1.3 Data models -relational, hierarchical, network
1.4 Levels of abstraction
1.5 Data independence
1.6 Structure of DBMS
1.7 Users of DBMS
1.8 Advantages and disadvantages of DBMS
Unit II Conceptual and Relational Database Design 12 Hrs.
2.1 Overview of DB design process.
2.2 Introduction to data models (E-R model, Relational model, Network model, Hierarchical
model)
2.3 Conceptual design using ER data model (entities, attributes, entity sets, relations,
relationship sets) and symbols. Extended ER Features, ER to Relational Mapping
2.4 Constraints (Key constraints, Integrity constraints, referential integrity, unique
constraint, Null/Not Null Constraint, Domain Constraint, Check constraint, Mapping
constraints, Column level and Table Level Constraint)
2.5 Keys in Database (primary key, foreign key, Candidate key, super key)
2.6 Extended features - Specialization, Aggregation, Generalization (Pictorial
representation).
2.7 Structure of Relational Databases (concepts of a table)
2.8 Concept of Normalization -Normal forms (only definitions) with example (1NF,2NF,3N,

20
 BCNF, 4NF)
2.9 Functional dependency - Concept, Closure of Attribute set, Armstrong axioms, Closure
of relation(F+)
2.10 Decomposition - Concept, Properties of Decomposition (Lossless joins and
Dependency preservation)
Unit III Structured Query Language (SQL) 10 Hrs.
3.1 Introduction to SQL.
3.2 DDL commands with examples (Create, Drop, Alter)
3.3 DML commands with examples (Insert, Update, Delete)
3.4 Basic structure of SQL Select query
3.5 SQL Operations (Aggregate functions, Set operations, Date, Time, String functions and
Null values, Nested Sub queries)
3.6 Join Queries (Cartesian Product, Inner joins, Outer - Left, Right, Full)
3.7 Views (Create, Alter, Drop)
3.8 Examples on SQL (case studies)
Unit III Structured Query Language (SQL) 10 Hrs.
3.1 Introduction to SQL.
3.2 DDL commands with examples (Create, Drop, Alter)
3.3 DML commands with examples (Insert, Update, Delete)
3.4 Basic structure of SQL Select query
3.5 SQL Operations (Aggregate functions, Set operations, Date, Time, String functions and
Null values, Nested Sub queries)
3.6 Join Queries (Cartesian Product, Inner joins, Outer - Left, Right, Full)
3.7 Views (Create, Alter, Drop)
3.8 Examples on SQL (case studies)
Unit IV Introduction to PL/Postgres SQL 12 Hrs.
4.1 PL/Postgres SQL: Language structure
4.2 Control structures (Conditional Statements and loops)
4.3 Stored Procedures.
4.4 Functions
4.5 Handling errors and exceptions
4.6 Cursors
4.7 Triggers
Unit V Transaction Management 12 Hrs.
5.1. Transaction
5.1.1.1.1 Properties of transaction
5.1.1.1.2 States of transactions
5.1.1.1.3 Concurrent execution of transactions
5.1.1.1.4 Conflicting operations
5.2 Schedules
5.2.1.1.1 Types of schedules

21
5.3 Concept of serializability
5.3.1 Precedence graph for serializability
5.4 Basic timestamp protocol for concurrency, Thomas Write Rule.
5.5 Two-phase Locking protocol, Timestamps vs. Locking.
5.6 Deadlock and Deadlock Handling - Deadlock Avoidance, Deadlock Detection and
Deadlock Recovery
5.7 Log Base Recovery Techniques - Deferred and Immediate Updates
Unit VI Database Security 8 Hrs.
6.1 Introduction to database security concepts
6.2 Methods for database security
6.3 Access Control Method
6.3.1 Discretionary access control method
6.3.2 Mandatory access control
6.3.3 Role based access control for multilevel security
6.4 Use of views in security enforcement
6.5 Overview of encryption technique for security
6.6 Statistical database security.
Books
1. Silberschatz, Korth, and Sudarshan, “Database System Concepts”, 6th Edition, McGraw-
Hill, 2011
2. Elmasri and Navathe, “Fundamentals of Database Systems”, 7th Edition, Pearson, 2017
3. Ramakrishnan and Gerkhe,“Database Management Systems”, 3rd Edition, Tata
McGraw Hill, 2002
4. Desai Bipin, “Introduction to Database Management System”, 1st Edition, Galgotia
Publication, 2008
5. Date, C. J., Kannan and Swamynathan,“An Introduction to Database Systems”, 8th
Edition, Pearson, 2006
6. Drake and Worsley, “Practical PostgreSQL”, O’Reilly Publications, 2002
7. Kahate, “Introduction to Database Management Systems”, 1st Edition, Pearson Education,
2004
8. Singh, S. K., “Database Systems: Concepts, Design and Application”, 2 nd Edition,
Pearson, 2011

22
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA – 252 - MJP: Lab course on CA - 251 - MJ
Teaching Scheme Credits Examination Scheme:
Lab: 04 Hrs./ Week/ Batch 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To study DDL and DML Queries
2. To understand SQL and PL/SQL
Course Outcomes: After successful completion of this course, learner will be able to
CO1: Design E-R Model for given requirements and convert the same into database tables.
CO2: Design and create relational database systems.
CO3: Use SQL DDL and DML commands
CO4: Apply constructs in PL/PGSQL
Guidelines for Instructor's Manual
The instructor shall prepare instructor’s manual consisting of University syllabus,
conduction and Assessment guidelines.
Guidelines for Student Journal
The student shall perform each laboratory assignment and submit the same in the form of a
journal. Journal shall have a Certificate, table of contents, and handwritten write-up of each
assignment (Title, Objectives, Problem Statement, Program Outputs, software and Hardware
requirements, Date of Completion, Assessment grade/marks and signature of the instructor).
Guidelines for Assessment
The instructor shall carry out internal evaluation of laboratory assignments of 15 marks
throughout the semester. For each lab assignment, the instructor shall assign grade/marks
based on parameters with appropriate weightage. Suggested parameters include-timely
completion, performance, innovation, efficient codes, code documentation, punctuality and
neatness of the write-up etc.

A pair of examiners shall conduct end semester examination of 35 marks in the form of
practical examination based on journal assignments. Examiners shall ask questions about
journal assignments and / or problem statement provided during the practical examination to
judge understanding of concepts by the students.
List of Assignments
Assignment No 1 Simple table design (DDL) Commands 4 Hrs.
Create simple tables including all data types.
• Primary key constraint (as a table level constraint and as a column level constraint)
• Check constraint (All types)
• Unique constraint, Null/Not null constraint

23
 Assignment No 2 Simple tables using referential constraint (DDL) 4 Hrs.
commands
• Create more than one table and access them using referential integrity constraint.
Assignment No 3 DDL commands 4 Hrs.
• Drop a table, Alter schema of a table.
• Insert / Update / Delete records using tables created in previous Assignments
Assignment No 4 DML commands 8 Hrs.
• Write queries on the tables using SQL select query
➢ Select <field-list> from table [where <condition> order by <field list>], Select
<field-list, aggregate functions> from table [where <condition> group by <>
having <> order by <>]
• To create views and retrieve data using the views
Assignment No 5 DML commands 4 Hrs.
• Write queries using set operations (minus operation, union, union all, intersect, intersect
all)
Assignment No 6 Nested Queries 4 Hrs.

• Write nested queries using Except, Except all, Exists, Not exists etc.
Assignment No 7 Stored Procedure 6 Hrs.
• Create a Simple Stored Procedure
• Create a Stored Procedure with IN, OUT and IN/OUT parameter

Assignment No 8 Function 6 Hrs.

• Create and use a Simple Stored Function
• Create and use a simple Stored Function that returns
• Create and use a simple Stored recursive Function
Assignment No 9 Cursor 4 Hrs.
• Create and use a Simple Cursor
• Create and use a Parameterized Cursor
Assignment No 11 Exception Handling 4 Hrs.
• Create and use a Simple Exception-Raise Debug Level Messages
• Create and use a Simple Exception-Raise Notice Level Messages
• Create and use a Simple Exception-Raise Exception Level Messages
Assignment No 12 Triggers 10 Hrs.
• Create and perform insert, update, delete using a Before Trigger & an After Trigger
• Creating EER Diagram and schemas using MySQL- Workbench or any such tools
• Store data in simple DBMS and retrieve it in spreadsheets using SQL query

24
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA – 271 - VSC: Python Programming
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./ Week/ Batch 02 Continuous Evaluation:15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To introduce programming concepts using Python
2. To understand various constructs in Python
3. To test and execute Python programs.
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Write Python programs to solve a given problem
CO2: Choose appropriate data structures such as lists, dictionaries, tuples, and sets.
CO3: Develop Python programs to implement the given small applications.
Guidelines for Instructor's Manual
The instructor shall prepare instructor’s manual consisting of University syllabus, conduction
and Assessment guidelines.
Guidelines for Student Journal
The student shall perform each laboratory assignment and submit the same in the form of a journal.
Journal shall have a Certificate, table of contents, and handwritten write-up of each assignment
(Title, Objectives, Problem Statement, Program Outputs, software and Hardware requirements,
Date of Completion, Assessment grade/marks and signature of the instructor).
Guidelines for Assessment
The instructor shall carry out internal evaluation of laboratory assignments of 15 marks
throughout the semester. For each lab assignment, the instructor shall assign grade/marks
based on parameters with appropriate weightage. Suggested parameters include-timely
completion, performance, innovation, efficient codes, code documentation, punctuality and
neatness of the write-up etc.

A pair of examiners shall conduct end semester examination of 35 marks in the form of practical
examination based on journal assignments. Examiners shall ask questions about journal
assignments and / or problem statement provided during practical examination to judge
understanding of concepts by the students.

25
 List of assignments
The instructor shall cover theoretical aspects such as Data types, declarations, input /
output, control flow, Strings and Functions List, Tuples, Dictionary and Sets etc.
Assignment No. Topics for the Assignments Number of Hrs.
1 Basic Python 06
2 Control structures and operators 08
3 Python Strings 08
4 Python Functions 08
5 Python Lists 08
6 Python Tuples 08
7 Python Dictionary 08
8 Python Sets 06
Total 60
BOOKS
1. Montojo, Jason, Campbell, Jennifer and Gries Paul, “Practical Programming: An
Introduction to Computer Science using Python 3”, 2nd Edition, O’Reilly, 2013
2. Payne James, “Beginning Python: Using Python and Python 3.1”, 1st Edition, Wrox
Publication, 2010
3. Dierbach Charles, “Introduction to Computer Science Using Python”, 1st Edition, Wiley
Publication, 2015
4. Balagurusamy E., “Introduction to Computing and Problem-Solving using Python, 1st
Edition, Tata McGraw Hill publication, 2017
5. Mueller John P., “Beginning Programming with Python for Dummies”, 1st Edition, Dummies,
2014

26
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA – 271 - CEP: Community services
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./ Week 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To provide exposure to the students and sensitize them for community issues/problems
2. To know levels of community engagements (Informative, participative and decision-
making participations)
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Identify and define community engagement service to address community problem
CO2: Choose appropriate community engagement level to solve the problem
CO3: Analyze and propose possible solution to solve community problem
Guidelines for the faculty
A faculty shall be assigned as a guide for each group of 3 / 4 students.
The guide assigned for each group shall assist the assigned student group(s) for identifying topic/area
(topic list is provided below for reference) for the community engagements, objectives and outcomes,
preparation of questionnaire, resources/tools needed and guide the students for possible solutions
and report preparation. The guide assigned for each group shall monitor, track and assess the
progress of work carried out by students throughout the semester
Guidelines for Students
The student shall work in a group of 3 or 4 students. Each group shall select topic/area for the
community engagement to be undertaken in consultation with their assigned guide.
The group shall discuss and decide objectives, outcomes, overall plan for possible activities during
community engagement, methodology to be adopted, such as preparation of a questionnaire for
conduction of survey or methods for data gathering, tools to be used for analysis etc. and get the plan
approved from their guide.
Each group shall carry out activities during their free slots, or before/after college hours or on Sundays
or holidays. The students shall maintain a diary giving details of tasks performed by them,
observations/study notes etc.
The suggested timelines for the field work are
• Formation of group – 1 week
• Selection of topic for community engagement – 2 Week
• Discussions and finalization of objectives, outcomes and methodology to be used – 3
Weeks
• Activities for community engagement - Conduction of survey / gathering data, Awareness
programs, interviews, group discussions and meeting with guide –– 4 Weeks
• Preparation of report and presentation – 2 weeks
Each group shall submit a report at the end of the semester consisting of Title, Abstract, Rational of
the study, problem definition, objectives, outcomes, methodology used, details of activities
undertaken, analysis, findings, details of proposed solution (paper design/prototype/mobile
app etc.) and conclusions. Students should also submit photographs, audio-video clips etc.

27
 Guidelines for Assessment
The instructor shall carry out internal evaluation of work for 15 marks throughout the semester
based on timely completion of the work, analysis, findings and neatness of the report etc.
The end semester examination of 35 marks shall be based on group presentation and the
reports of activities participated.
List of suggested topics/areas for Community Services (but not limited to)
1. Schools and colleges – Awareness about environment issues, cyber security, health and
nutrition, new policies by government, Training programs for students and teachers, etc.
2. Agriculture - Awareness programs for farmers, in association with agriculture officers on
Plantation and Soil protection, Bio-diversity, Organic farming, promotion of local crops,
marketing, sales and logistics for agro products etc.
3. Old age homes and organizations working of differently abled people – Awareness
programs for Senior Citizens and differently abled people and their interviews etc.
4. Organizations/NGOs working on food habits, nutrition, adulterations – Awareness
programs for students staying in hostels
5. Urban Region - Smart Cities, Traffic Management, Renewable energy and Solar Systems
- Interviews with officers and citizens, social and community leaders, Drives for waste
collection and disposal, testing water quality Drives for River and garden Cleaning, etc.
6. Government offices and offices of Local Bodies (Corporation/Municipal Corporation/
Grampanchayat – Interviews with officers and devise mechanism for promotion of
Schemes and services for citizens through websites, street plays etc.
7. Pollution control boards – Interviews with officers and arranging drives/awareness
programs for Air/Sound/Water pollutions
8. Department of disaster Management – Arranging mock drills
9. Office of Local city bus transportation – Interviews with officers, employees and
passengers and suggest solutions with optimised bus routes, frequency, stoppages
and fairs
10. Prominent Local social events such as “Sinhasta Kumbhamela”, “Pundharpur Vari” etc. –
Crowd and traffic management, surveillance, security, Environmental issues etc.
11. Women education and empowerment – Training programs for house wives and Mahila
Udyog and Bachat Gat
12. Community engagement platforms – Study / develop platform for community members
to report issues, share ideas and collaborate on local issues.
➢ Colleges to try adopting a village or a nearby community through conduction of
workshops or awareness drives on topics such as digital literacy, environmental
sustainability, mental health, career guidance and planning for local stakeholders
BOOKS
1. Waterman, A. Service-Learning: A Guide to Planning, Implementing, and Assessing Student
Projects. Routledge, 1997.
2. Beckman, M., and Long, J. F. Community-Based Research: Teaching for Community Impact.
Stylus Publishing, 2016.
3. Design Thinking for Social Innovation. IDEO Press, 2015.
4. Dostilio, L. D., et al. The Community Engagement Professional’s Guidebook: A Companion to The
Community Engagement Professional in Higher Education. Stylus Publishing, 2017

28
 Savitribai Phule Pune University
Second Year Bachelor of Computer Applications
CA – 251 - SEC: Spreadsheet Applications
Teaching Scheme Credits Examination Scheme:
Practical: 04 Hrs./ Week / 02 Continuous Evaluation: 15 Marks
Batch End-Semester: 35 Marks
Course Objectives:
1. To know Excel interface, basic and advanced Data Entry and Formatting
2. To understand Excel Formulas and Functions, Charts
3. To learn to automate tasks with Macros and VBA
Course Outcomes:
After successful completion of this course, the learners will be able to -
CO1: Navigate and utilize spreadsheet applications effectively for data organization and
management
CO2: Apply formulas, functions and logical operations to automate tasks.
CO3: Analyze and visualize data using charts, pivot tables and conditional formatting
CO4: Implement data validation, sorting and filtering for efficient data handling
CO5: Develop practical spreadsheet solutions for business scenarios like financial planning,
inventory management and project management.
Guidelines for Instructor's Manual
The instructor shall prepare instructor’s manual consisting of University syllabus, conduction
and Assessment guidelines.
Guidelines for Student Journal
The student shall perform each laboratory assignment and submit the same in the form of a
journal. Journal shall have a Certificate, table of contents, and handwritten write-up of each
assignment (Title, Objectives, Problem Statement, Program Outputs, software and Hardware
requirements, Date of Completion, Assessment grade/marks and signature of the instructor).
Guidelines for Assessment
The instructor shall carry out internal evaluation of laboratory assignments of 15 marks
throughout the semester. For each lab assignment, the instructor shall assign grade/marks
based on parameters with appropriate weightage. Suggested parameters include-timely
completion, performance, innovation, efficient codes, code documentation, punctuality and
neatness of the write-up etc.
A pair of examiners shall conduct end semester examination of 35 marks in the form of practical
examination based on journal assignments. Examiners shall ask questions about journal
assignments and / or problem statement provided during practical examination to judge
understanding of concepts by the students.
List of Assignments
1. Create, Open, Save Spreadsheet, Basic Data Entry and Formatting and conditional
formatting, Formula and function, Sorting, importing data from various formats (csv/text)

29
2. Lookup and Reference Functions - VLOOKUP, HLOOKUP, XLOOKUP
3. INDEX and MATCH (for dynamic lookups) - INDIRECT, OFFSET, CHOOSE
4. Logical Functions - IF, AND, OR, XOR, IFERROR, IFS
5. Text Functions - CONCAT, TEXTJOIN, PROPER, LEFT, RIGHT, MID
6. Date and Time Functions - TODAY, NOW, EOMONTH, NETWORKDAYS
7. Math and Statistical Functions - SUMIF, COUNTIF, AVERAGEIF RANK, LARGE, SMALL
8. Array Formulas and Dynamic Arrays
a. Basic example of Arrays using ctrl + shift + enter
b. Array with if, len function and mid function formula
c. Advanced use of formula with Array.
9. Power Query for Data Cleaning
a. Automates data cleaning and transformation.
b. Can merge, split, remove duplicates, and reshape data.
10. Histogram, Waterfall, Gantt and Combo Charts
11. Pivot Tables
a. Creating simple Pivot Tables
b. Basic and Advanced value field
c. Classic Pivot Tables
d. Filtering Pivot Tables
e. Modifying Pivot Tables
f. Grouping data in pivot table based on numbers, category and Dates
12. VBA
a. Creating a Macro, Procedures and Functions in VBA, Variables in VBA
b. If statement and Select statement - if and Else if, Select case Statement
c. Loops in VBA - For and Do loop, Exit Loop, Advanced Loop
d. Mail Functions in VBA - Send automated mail, Merge multiple excel files into one
sheet, Split worksheets using VBA filters

13. Micro Projects

a. Financial Calculation and Budget Planning or
b. Sales and Inventory Management or
c. Project and Time Management
Books
1. Alexander, Michael and Kusleika, Dick, “Excel 365 Bible”, 2nd Edition, John Wiley & Sons,
2022
2. Mc, Fedries and Greg Harvey, “Excel All-in-One for Dummies”, 1st Edition, Dummies, 2021
3. Holler, James., “Office 365 Bible”, James Publication, 2024
4. Global, Emenwa., “Excel in 7 Days”, Independently, 2022
5. Hong, Bryan., “101 Excel Formulas Guide”, Independently, 2019

30
 List of MINOR Courses offered
by BOS in Computer Applications (FoS&T)
to any other BOS under FoS&T or any Faculty except FoS&T

Sr. Course Credits

Semester Course Name
No. Code TH PR Total
1 III CA-241- Programming with Python 02 00 02
MN
2 III CA-242- Lab course on Programming with 00 02 02
MN Python
3 IV CA-291- Introduction to Artificial Intelligence and 02 00 02
MN Machine Learning
4 IV CA-292- Lab course on Artificial Intelligence and 00 02 02
MN Machine Learning

List of Open Elective (OE) Courses offered

by BOS in Computer Applications
to any Faculty except FoS&T

Sr. Course Credits

Semester Course Name
No. Code TH PR Total
1. III OE-201- Introduction to Artificial Intelligence 02 00 02
CA
2. IV OE-251- Software Tools for Office Administration 00 02 02
CA

31
 Detailed Drafts Of
Minor Courses offered
by BOS (Computer Applications)
to
any other BOS under FoS&T or any
faculty except FoS&T
for
SEMESTER III and IV only

32
 Savitribai Phule Pune University
Minor Course offered by BOS (Computer Applications) to any other BOS
under FoS&T or any faculty except FoS&T for
SEMESTER III only
CA – 241 – MN: Programming with Python
Teaching Scheme: Credits Examination Scheme:
Theory: 02 Hrs./ Week 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To introduce programming concepts using Python
2. To understand various constructs in Python
3. To test and execute Python programs.
Course Outcomes: After successful completion of this course, the learners will be able to:
CO1: Write Python programs to solve the given problem
CO2: Utilize the data structures such as lists, dictionaries, tuples and sets.
CO3: Use built-in and user defined modules and packages.
CO4: Apply operations involving file systems and data handling.
Course Contents
Unit I Introduction to Python 5 Hrs.
1.1 Introduction
1.1.1. Python identifiers and reserved words
1.1.2. Lines and indentation, multi-line statements and Comments
1.1.3. Input/output with print and input functions
1.1.4. Command line arguments and processing command linear augments
1.2 Data Types
1.2.1 Standard data types -basic, none, Boolean, numbers
1.2.2. Data type conversion
1.3 Operators
1.3.1: Basic operators (Arithmetic, comparison, assignment, bitwise, logical)
1.3.2 Membership operators (in, not in)
1.3.3. Identity operators (is, is not)
1.4 Control Statement
1.4.1 Conditional/decision statements (if, if—else, elif,
1.4.2. Loop Control Structure (while, Do--while, for)
1.4.3 Selection Control Statement (Switch case, Pass, Continue, Break)
1.5 Basic Object-Oriented Programming Concepts in Python
1.5.1 Creating classes, instance, objects, accessing members
1.5.2 Data hiding (the double underscore prefix)
1.5.3 Built-in class attributes

33
 1.5.4 Garbage collection
1.5.5 Constructor
1.6 Applications of Python

Unit II Functions and Strings 7 Hrs.

2.1 Introduction to function
2.1.1 Defining a function, calling a function
2.1.2 Types of function (Built-in, function, user-defined function, lambda function
/anonymous function, recursive function)
2.1.3 Function arguments
2.1.4 Global and Local variable, Examples
2.1.5 Math Functions
2.1.6 Functional programming tools -filter( ), map( ), and reduce( )
2.2 Introduction to string
2.2.1 Declaration and String manipulation -Accessing String, String Slices.
2.2.2 Documentation Strings-Single quotes, Double quotes, Triple quotes, Raw String
2.2.3 Python string operators, escape character
2.2.4 String formatting operator
2.2.5 Built-in String functions / Methods
Unit III Tuple, Set and Dictionary 8 Hrs.
3.1 Introduction to tuple
3.1.1 Tuple definition, accessing tuple values, update and delete tuple elements
3.1.2 Basic Tuple operations
3.1.3 Tuple -Indexing and slicing
3.1.4 Built in tuple functions
3.1.5 Applications of tuple
3.2 Introduction to set
3.2.1 Create, update and remove elements from set
3.2.2 Set operations
3.2.3 Set built-in functions
3.2.4 Applications of set
3.3 Introduction to Dictionary
3.3.1 Creating and accessing values in a dictionary
3.3.2 Updating dictionary, delete dictionary elements
3.3.3 Properties of dictionary keys
3.3.4 Built-in dictionary functions and methods
Unit IV Modules and Packages 4 Hrs.

4.1 Introduction to Module

4.2 Types of Module and Examples
4.2.1 Built_in Module (Math module, Random module, Time module, regular
expression)

34
 4.2.2 User Defined Module (creation and import)
4.2.3 External Module (Python libraries-NumPy, Pandas, Matplotlib, Seaborn)
4.3 Introduction to Package
4.3.1 Importing and creating package
4.3.2 Example of packages
Unit V File Handling, Data Handling using Data Frames 6 Hrs.
5.1 Introduction to file
5.1.1 Definition
5.1.2 Types of files (Text, Binary and CSV file)
5.1.3 File Opening Modes (r, r+, w, w+, a, a+)
5.1.4 Creating files and Operations on files (open, close, read, write)
5.2 Data Manipulation
5.2.1 Creating Data Frame -User define, using csv file
5.2.2 View Data Frame
5.2.3 Preprocessing on Data Frame -Null Values, Duplicate values
5.2.4 Modify Data in Data Frame
5.2.5 Grouping and Aggregating Data
5.3 Data Visualization (Histogram, Line chart, Bar chart, Scatter plot )
Books
1. Lubanovic Bill, “Introducing Python-Modern Computing in Simple Packages”, 1st Edition,
O’Reilly Publication, 2014
2. Montojo, Jason., Campbell, Jennifer and Gries, Paul, “Practical Programming: An
Introduction to Computer Science using Python 3”, 2nd Edition, O’Reilly, 2013
3. Dierbach Charles., “Introduction to Computer Science Using Python”, 1st Edition, Wiley
Publication, 2015
4. Mueller, John P., “Beginning Programming with Python for Dummies”, 1st Edition,
Dummies, 2014
5. A Beginner’s Python Tutorial: http://en.wikibooks.org/wiki/ABeginner%27s

35
 Savitribai Phule Pune University
Minor Course offered by BOS (Computer Applications) to any other BOS
under FoS&T or any faculty except FoS&T for
SEMESTER III only
CA – 242 - MNP: Lab Course on CA – 241 - MN
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./ Week 02 Continuous Evaluation: 15 Marks
/ Batch End-Semester: 35 Marks
Course Objectives:
1. To introduce programming concepts using Python
2. To understand various constructs in Python
3. To test and execute Python programs.
Course Outcomes: After successful completion of this course, the learners will be able to:
CO1: Write Python programs to solve the given problem
CO2: Utilize the data structures such as lists, dictionaries, tuples and sets.
CO3: Use built-in and user defined modules and packages.
CO4: Apply operations involving file systems and data handling.
List of Assignments
Unit 1 Introduction to Python 12 Hrs.
Assignment on various operator in Python
Assignment on Loop and decision control statement
Assignment on classes and built in functions
Unit 2 Strings and Functions 12 Hrs.
Assignment on string operators and built-in string functions
Assignment on user defined functions and math functions
Unit 3 Tuple, Set and Dictionary 12 Hrs.
Assignment on Tuple
Assignment on Sets
Assignment on create dictionary
Assignment on access and manipulates the elements from dictionary.
Unit 4 Modules and Packages 12 Hrs.
Assignment on importing, Creating and exploring modules
Assignment on Math module, Random module, Time module, Regular expression module.
Assignment on importing package and creating package
Unit 5 File Handling, Data Handling using (3) Data Frames (3) 12 Hrs.
Assignment on Creating files and Operations on file
Assignment on Data Frame creation and preprocessing on data
Assignment on Data Visualization

36
 Savitribai Phule Pune University
Minor Course offered by BOS (Computer Applications) to any other BOS under
FoS&T or any faculty except FoS&T for
SEMESTER IV only
CA - 291- MN: Introduction of Artificial Intelligence and Machine Learning
Teaching Scheme: Credits Examination Scheme:
Theory: 02 Hrs./ Week 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To learn the core concepts of AI, evolution and different paradigms of AI
2. To understand expert systems and how they utilize knowledge bases and inference
engines to solve problems.
3. To study the concepts in machine learning, including supervised, unsupervised, and
reinforcement learning.
4. To know the basics of deep learning frameworks.
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Describe basic concepts in AI
CO2: Compare different search algorithms used in AI
CO3: Demonstrate understanding of knowledge representation and logic
CO4: apply key machine learning concepts such as supervised, unsupervised, and
reinforcement learning.
CO5: Develop the ability to use machine learning algorithms such as linear regression,
logistic regression, decision trees.
Course Contents
Unit I Introduction to Artificial Intelligence and Problem Space 07 Hrs.
1.1 Introduction
1.2 Comparison of AI, Machine Learning, Deep Learning
1.3 AI Techniques and Application of AI
1.4 Agents
1.4.1 definition and types of agents
1.4.2 Agent and Environments
1.4.3 Structure of Agents.
1.5 Defining problem as a State Space Search
1.6 Production System, Problem Characteristics
1.7 Problem Space
1.7.1 Water Jug Problem
1.7.2 Missionary Cannibal Problem
1.7.3 Block Words Problem
1.7.4 Monkey and Banana Problem
Unit II Search Algorithms 08 Hrs.

37
2.1 Search Algorithms
2.2 Uninformed Search Algorithm / Blind Search Techniques
2.2.1 Breadth-First Search
2.2.2 Depth-First Search
2.3 Informed Search Techniques
2.3.1 Generate and Test
2.3.2 Simple Hill Climbing
2.3.3 Best First Search
2.3.4 Constraint Satisfaction
2.3.5 Mean End Analysis
2.3.6 A* and AO*
Unit III Knowledge Representation and Reasoning 08 Hrs.
3.1 Definition of Knowledge
3.2 Types of Knowledge
3.2.1 Procedural Knowledge
3.2.2 Declarative Knowledge
3.3 Approaches to Knowledge Representations
3.4 Propositional and Predicate Logic
Unit IV Introduction to Machine Learning 07 Hrs.
4.1 Introduction to Machine Learning
4.2 Key concept of Machine Learning (Data, Model, Training, Labels, Features)
4.3 Types of Machine Learning (Supervised, Unsupervised and Reinforcement Learning)
4.4 Deep Learning: Natural Language Processing, Computer Vision, Speech Recognition,
Robotics, Generative AI.
4.5 Applications
Books
1. Norvig, Peter., and Russell, Stuart., “Artificial Intelligence: A Modern Approach", 3rd
Edition, Pearson, 2009
2. Knight, Kelvin. and Rich, Elaine., “Artificial Intelligence”, 3rd Edition, McGrawhill
Publication, 2017
3. Geron, Aurelien., “Hands-On Machine Learning with Scikit-Learn, Keras, and
TensorFlow”, 3rd Edition, 2022
4. Goodfellow, Ian., Bengio, Yoshua and Courville, Aaron.,“Deep Learning”, MIT press,
2016
5. Muller, Andreas., “Introduction to Machine Learning with Python: A Guide for Data
Scientists”, 1st Edition, Shroff Publisher, 2016
6. Howard, Jeremy and Gugger, Sylvain, “Deep Learning for Coders with Fastai and
PyTorch: AI Applications Without a PhD”, O’Reilly, 2020
7. Raschka, Sebastian., Liu, Yuxi and Mirjalili, Vahid, “Machine Learning with PyTorch
and Scikit-Learn: Develop machine learning and deep learning models with Python”,
Packt Publication, 2022

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 Savitribai Phule Pune University
Minor Course offered by BOS (Computer Applications) to any other BOS
under FoS&T or any faculty except FoS&T for
SEMESTER IV only
CA – 292 – MNP: Lab Course on CA - 291 - MN
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./ Week/ 02 Continuous Evaluation: 15 Marks
Batch End-Semester: 35 Marks
Course Objectives:
1. To learn to use algorithms in AI and machine learning
2. To understand various machine learning techniques, libraries and tools
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Apply the suitable AI algorithms to solve a given problem
CO2: preprocess real-world data, including handling missing values, outliers, and scaling
CO3: Use appropriate machine-learning libraries and tools
CO4: solve problems using machine learning techniques.
List of Assignments
Assignment 1 Artificial Intelligence and Problem Space
• Water Jug Problem
• Missionary Cannibal Problem
Assignment 2 Problem Space
• Block Words Problem
• Monkey and Banana Problem
Assignment 3 Search Algorithms
• Breadth-First Search
• Depth-First Search
Assignment 4 Search Algorithms
• Constraint Satisfaction
Assignment 5 Generate and Test
• Simple Hill Climbing
• Best First Search

Assignment 6 Testing and Analysis

• Mean End Analysis
• A* and AO*
Assignment 7 Knowledge Representation
• Procedural Knowledge
• Declarative Knowledge

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Assignment 8 Reasoning
• Propositional Logic
• Predicate Logic
Assignment 9 Machine Learning Libraries
• Scikit-learn, pandas, NumPy
• Jupiter Notebook basics
• Introduction to Google Collab
Assignment 10 Data Cleaning
• User defined data frame creation
• Missing data, noise removal
Assignment 11 Data Visualization Techniques
• Data visualization techniques using Matplotlib and Seaborn
Assignment 12 GenAI
• Use GenAI to acquire the knowledge in structured format like if then else rule.

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 Detailed Drafts Of
Open Elective Courses offered
by BOS (Computer Applications)
to
any faculty except FoS&T
for
SEMESTER III and IV only

41
 Savitribai Phule Pune University
Open Elective course offered by BOS (Computer Applications) to any faculty
except FoS&T for SEMESTER III only
OE – 201 – CA: Introduction to Artificial Intelligence
Teaching Scheme: Credits Examination Scheme:
Theory: 02 Hrs./ Week 02 Continuous Evaluation: 15 Marks
End-Semester: 35 Marks
Course Objectives:
1. To learn the core concepts of AI, evolution and different paradigms of AI
2. To understand expert systems and how they utilize knowledge bases and inference engines
to solve problems.
3. To study the concepts in machine learning, including supervised, unsupervised, and
reinforcement learning.
4. To know the basics of deep learning frameworks
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Describe basic concepts in AI
CO2: Compare different search algorithms used in AI
CO3: Demonstrate understanding of knowledge representation and logic
CO4: Compare supervised, unsupervised, and reinforcement learning.
Course Contents
Unit I Introduction to Artificial Intelligence 04 Hrs.
1.1 Introduction
1.2 Comparison of AI, Machine Learning, Deep Learning
1.3 Applications of AI
1.4 AI Techniques
1.5 Agents and Types of Agents, Agents and Environments, Structure of Agents
Unit II Problems, Problem Spaces and search 04 Hrs.
2.1 Defining problem as a State Space Search
2.2 Production System
2.3 Problem Characteristics
2.4 Search and Control Strategies
2.5 Problems- Water Jug problem, Missionary Cannibal Problem, Block words Problem,
Monkey and Banana problem

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Unit III Knowledge Representation and Introduction to Searching 12 Hrs.
Algorithms
3.1 Knowledge Representation
3.1.1 Introduction
3.1.2 Types of knowledge
3.1.3 Approaches to Knowledge Representation
3.1.4 Applications of Knowledge Representation
3.2 Search Algorithm
3.2.1 Elements of AI search algorithms
3.2.2 Importance of Search Algorithm
3.2.3 Types of AI search algorithms (BFS, DFS, A* and AO*)
3.2.4 Applications
Unit IV Machine Learning 10 Hrs.
4.1 Introduction to Machine Learning
4.2 Key concept of Machine Learning (Data, Model, Training, Labels, Features)
4.3 Types of Machine Learning (Supervised, Unsupervised and Reinforcement Learning)
4.4 Deep Learning: Natural Language Processing, Computer Vision, Speech Recognition,
Robotics, Generative AI.
4.5 Applications
Books
1. Knight, Kelvin. and Rich, Elaine., “Artificial Intelligence”, 3rd Edition, Mc-Graw Hill
Publication, 2017
2. Ertel, Wolfgang and Black Nathanael T., “Introduction to Artificial Intelligence”,
Springer,2011
3. Mitchell, Tom M., “Machine Learning”, McGraw Hill, 1997
4. Nilsson Nils J., “Artificial Intelligence: A New Synthesis”, Morgan Kaufman, 1998
5. Ethem, Alpaydin., “Introduction to Machine Learning”,3rd Edition, PHI Publication, 2015

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 Savitribai Phule Pune University
Open Elective course offered by BOS (Computer Applications) to any faculty
except FoS&T for SEMESTER IV only
OE – 251 – CA: Software Tools for Office Administration
Teaching Scheme: Credits Examination Scheme:
Practical: 04 Hrs./Week/ 02 Continuous Evaluation:15Marks
Batch End-Semester: 35 Marks
Course Objectives:
1. To be familiarize with office automation tools for efficient document management, data
processing, and communication.
2. To understand tools for word processing, spreadsheets, presentations, and data collection
to enhance office productivity.
3. To study tools for collaboration and management of files using cloud-based platforms like
Google Drive and OneDrive securely.
4. To learn email etiquette, calendar scheduling, and cyber security for professional office
administration.
Course Outcomes: After successful completion of this course, the learners will be able to
CO1: Apply word processing techniques to create, format, and manage professional
documents
CO2: Use spreadsheet tools for data entry, analysis, visualization, and decision-making.
CO3: Design and deliver interactive professional presentations using animations and
multimedia integration.
CO4: Create and analyze Google Forms for data collection, surveys, and automated feedback
management.
CO5: Implement email and cloud-based collaboration tools to enhance office communication,
scheduling, and document security.
List of Assignments
Document Creation and Communication Tools 15 Hrs.
Assignment 1: Understanding CV Formatting and Design: Create a Curriculum Vitae
(CV) using Google Docs or MS Word. Apply proper formatting with headings, bold text, and
bullet points. Upload the document to Google Drive and share it with your friends as viewers.

Assignment 2: Automating Personalized Communication with Mail Merge: Use Mail

Merge in MS Word to send personalized invitation letters. Prepare an Excel sheet with at
least 5 names and email addresses. Merge the data into a formal letter template. Save the
final document as PDF, upload it to Google Drive

Assignment 3: Writing Formal Emails for Professional Communication: Compose a

formal email to your professor requesting a meeting using Gmail or Outlook. Attach a Word
file as an agenda.

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 Spreadsheets for Data Management and Analysis 15 Hrs.
Assignment 4: Data Visualization Using Charts and Conditional Formatting
Analyze sales data using charts in MS Excel or Google Sheets. Enter sample sales data
(Product, Sales, Revenue, etc.). Create a Bar Chart and Pie Chart to visualize the data.
Apply conditional formatting to highlight low sales.
Assignment 5: Financial Tracking with Google Sheets
Create a monthly expense tracker in Google Sheets. Include columns: Date, Category,
Amount, and Total. Use the SUM formula to calculate total expenses. Format the sheet
properly.
Presentations and Multimedia Integration 15 Hrs.
Assignment 6: Enhancing Presentations with Multimedia and Effects
Design a 5-slide presentation on "Future of Office Automation" using Google Slides or MS
PowerPoint. Include images, animations, and transitions. Add a video or audio clip to enhance
the content.
Online Collaboration and Cloud-Based Tools 15 Hrs.
Assignment 7: Creating and Analyzing Surveys Using Google Forms
Create a Google Form to collect event feedback. Include multiple-choice, rating scale, and
short-answer questions. Collect at least 10 responses and analyze them in Google Sheets.
Assignment 8: Efficient Meeting Scheduling with Google Calendar
Schedule a team meeting using Google Calendar. Add title, date, time, and agenda. Invite at
least 3 participants and set a reminder.
Assignment 9: File Management and Collaboration in Google Drive
Organize and share files in Google Drive. Create a folder named "Office Automation Project"
and upload at least 3 different files (Doc, Sheet, Slide)
Books
1. Randy, Nordell, “Microsoft Office 365: In Practice”,1st Edition, McGraw-Hill Publication,
2023
2. Steve Tudor, "Excel 2023: The Most Updated Guide to Master Microsoft Excel"
3. Richard Wilson, "Google Forms and Google Sheets for Beginners"
4. Poatsy, Mary Anne., and Davidson, Jason, “Microsoft Word 2021 and 365 for Beginners”,
1st Edition, Pearson Publication, 2022

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 Abbreviations

AEC Ability Enhancement Course

CEP Community Engagement Project

FoS&T Faculty of Science and Technology

FP Field Project

GE / OE General / Open Elective Course

IKS Indian Knowledge System

MJ Major Core Theory Course

MJP Major Core Laboratory Course

MN Multidisciplinary Minor Theory Course

MNP Multidisciplinary Minor Laboratory Course

MOOC Massive Open Online Course

NEP National Educational Policy - 2020

NPTEL National Programme on Technology Enhanced Learning

SEC Skill enhancement Course

SPPU Savitribai Phule Pune University

SWAYAM Study Webs of Active-Learning for Young Aspiring Minds VEC Value Education Course

VEC Value Education Course

VSC Vocational Skill Enhancement Course

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 Savitribai Phule Pune University, Pune
Maharashtra, India

Task Force for Curriculum Design and Development

Of
SY Bachelor of Computer Applications

Programme Coordinator

Dr. A B Nimbalkar - Member, Ad-hoc Board of Studies -

Computer Applications
Team Members for Course Design

Data Structures
Name of the Faculty Name of the College
Dr. Patil Rahul KRT Arts, BH Commerce and AM Science College (KTHM), Nashik
Mrs. Borase S P KRT Arts, BH Commerce and AM Science College (KTHM), Nashik
Mrs. Ghorpade S J KRT Arts, BH Commerce and AM Science College (KTHM), Nashik
Mrs. Jyoti P Malusare Haribhai V. Desai College of Arts, Science and Commerce, Pune
Mrs. Shivarkar Sonali S. M. Joshi College, Hadapsar, Pune

C++ Programming
Name of the Faculty Name of the College
Mrs. Kadam S. A. Baburaoji Gholap College, Sangvi, Pune
Mrs Suvarna S Patil BJS ASC College, Wagholi, Pune
Dr. Preeti Bharambe MAEERs MIT Arts Commerce and Science College Alandi, Pune
Mrs. Sarita Somnath Raut Pravara medical trust’s Arts commerce and science college, Shevgaon

Programming with Python

Name of the Faculty Name of the College
Mrs. Dipali Meher PES Modern College of Arts Science and commerce, Ganeshkhind, Pune
Mrs. Chandgude Vidya MIT Arts Commerce & Science College, Alandi, Pune
Mrs. Saykar Sunita J. Annasaheb Magar College, Pune
Mrs. Kamble Jayshree Pratibha college of commerce and computer studies, Pune
Mr. Derle D R KRT Arts, BH Commerce and AM Science College (KTHM), Nashik

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 Introduction to Artificial Intelligence
Name of the Faculty Name of the College
Mrs. Rohini Subhash Kapse KRT Arts, BH Commerce and AM Science College (KTHM), Nashik
Mrs. Sonali Sagar Gholve Sarhad College of ACS, Katraj, Pune
Mrs. Suvarna Sachin Pardeshi Ahmednagar College, Ahilyanagar

Indian Knowledge System for Computing

Name of the Faculty Name of the College
Dr. Vikas Nana Mahandule MAEERs MIT Arts Commerce and Science College Alandi, Pune
Mrs. Chavan Rutuja Pratibha College of Commerce and Computer Studies, Pune

Database Management Systems

Name of the Faculty Name of the College
Dr. Reena Shinde Sinhgad College of Science, Pune
Mrs. Kadlag Vinita Annasaheb Magar College, Pune
Mrs. Vrunda P Chouthkanthiwar JSPM'S JSIMR, Pune
Mrs. Gogte Suvarna Pratibha College of Commerce and Computer Studies, Pune

Python Programming
Name of the Faculty Name of the College
Dr. Sanjay T Wani Women's College of Home Science and BCA, Loni
Mrs. Dipali Deepak Mali Annasaheb Magar Mahavidyalaya, Pune
Mrs. Dhadawe Priya Amit Sarhad college of Arts, Commerce and Science, Katraj Pune
Mrs. Alka Baban Mhetre RJSP Mandal’s Arts commerce and science college, Bhosari, Pune

Introduction to Artificial Intelligence and Machine Learning

Name of the Faculty Name of the College
Dr Harshita Vachhani Pratibha college of Commerce and Computer Studies, Pune
Mr. Sanjay S Manvatkar BJS ASC College, Wagholi, Pune
Ms. Gadekar Manisha J Annasaheb Magar College, Pune
Mrs. More K D KRT Arts, BH Commerce and AM Science College (KTHM), Nashik

Software Tools for Office Administration

Name of the Faculty Name of the College
Dr. Vikas Nana Mahandule MAEERs MIT Arts Commerce and Science College Alandi, Pune
Prof. More R.N. Annasaheb Magar College, Pune
Mr. Amit Vilasrao Tale MAEERs MIT Arts Commerce and Science College Alandi, Pune

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 Spreadsheet Applications
Name of the Faculty Name of the College
Mrs. Savita Bhujbal Annasaheb Magar Mahavidyalaya Hadapsar, Pune
Mrs. Vijayshri Bava (Gosavi) K. K. Wagh Arts, Commerce & Science College, Nashik

Members of Ad-hoc Board of Studies - Computer Applications

Name Affiliation
Dr. S S Sane R H Sapat COE, Management Studies and Research Nashik
Dr. A B Nimbalkar Annasaheb Magar Mahavidyalaya Hadapsar, Pune
Dr. Razzak Sayyad Ahmednagar College, AhilyaNagar
Dr. R M Sonar IIT, Powai, Mumbai
Dr. S A Kadam Bharati Vidyapeeth, Pune

***********

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