DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 1
Seva Mandal Education Society’s
Smt. Kamlaben Gambhirchand Shah Department of Computer Applications
under
Dr. Bhanuben Mahendra Nanavati College of Home Science (Autonomous)
NAAC Re-Accredited ‘A+’ Grade with CGPA 3.69 / 4
UGC Status: College with Potential for Excellence
‘Best College Award 2016-17’ adjudged by S.N.D.T. Women’s University
Smt. Parmeshwari Devi Gordhandas Garodia Educational Complex
338, R.A. Kidwai Road, Matunga, Mumbai - 400019. Tel: 24095792 Email: [email protected]
PROGRAMME: M.Sc. COURSE : COMPUTER SCIENCE
Program Objectives
This program will enable the students to:
1. Gain in-depth knowledge in the key areas of computer science and practice in emerging,
cutting edge Computational Technologies.
2. Develop software solutions to real world problems through Information Technological skills
with international standards and facilitate them to be outstanding professionals.
3. Contribute to scientific research by independently designing, conducting and presenting the
results of small-scale research.
4. Be a part of skilled manpower in the various areas of computer science such as Algorithm
Analysis and Design, Data warehousing and Mining, Software Engineering, Advanced
Computing technologies, Web-based Applications Development, and Data Science.
Program Outcome
The completion of the post-graduation programme:
1. Takes forward the knowledge gained by the students at the undergraduate level and provides
them with an advanced level of learning and understanding of the subject.
2. Provides students with higher educational degree of technical skills in problem solving and
application development.
3. Helps students to acquire an analytical and managerial skills to enhance employment potential.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 2
Program Specific Outcome
1. The main outcome of this programme is enhancement in the Technical and Analytical skills of
computer science enthusiasts and provide them with the perfect amalgamation of theory as well
as practical knowledge in the various thrust areas of the field.
2. The students will acquire broad knowledge in core areas of computer science, current and
emerging computing technologies.
3. The students also acquire a research oriented professional approach to provide sustainable
solution to real life problems which can be solved using computational technologies.
Eligibility
A Science Graduates in
o BSc. (Physics),
o BSc. (Maths.),
o BSc (Elect.),
o BSc. (IT),
o B.Sc. (CS) or
o BCA or
o any engineering graduate in allied subject from the recognized university
with an aggregate mark not less than 50% (Open Category) and 45%
(Reserved category).
Mathematics at 12th Level or 100 marks mathematics studied at graduation level
is minimum requirement.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 3
M.SC. (COMPUTER SCIENCE)
SYLLABUS
M.Sc. (COMPUTER SCIENCE) SEMESTER - I (FIRST YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int. Ext. Total
MCS101 Programming Concepts and Design,
Analysis of Algorithms 4 - 2 2 4 2
MCS102
Data Communication and
Networking
4 - 2 2 4 2
MCS103 Operating Systems 4 - 2 2 4 2
MCS 104 Software Engineering 4 - 2 2 4 2
MCSL105 Programming Concepts Lab
- 2 1 1 2 1
MCSL106 Networking Lab - 2 1 1 2 1
MCSL107 Software Testing Lab - 2 1 1 2 1
MCSL108 Advanced Web Technology Lab - 2 1 1 2 1
Total 16 8 24 -
SEMESTER-I
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 4
M.Sc. (COMPUTER SCIENCE) SEMESTER - II (FIRST YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int.
Ext.
Total
MCS201 Mobile Communication and
Wireless Technology 4 - 2 2 4 2
MCS202 Data Analytics and Mining
4 - 2 2 4 2
MCS203 Research Methods and Statistical
Analysis 4 - 2 2 4 2
MCS204 Elective I 4 - 2 2 4 2
MCSL205 Data Analytics and Mining Lab
- 2 1 1 2 1
MCSL206 Statistics Lab - 2 1 1 2 1
MCSL207 Advanced Java Lab - 2 1 1 2 1
MCSL208 Advanced Python Lab - 2 1 1 2 1
Total 16 8 24 -
SEMESTER-II
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
Elective – I
Course Code Course Nomenclature
MCS204A Distributed Systems
MCS204B Computer Graphics
MCS204C Advanced Python
MCS204D Natural Language Processing
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 5
M.Sc. (COMPUTER SCIENCE) SEMESTER - III (SECOND YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int. Ext. Total
MCS301 Big Data Analytics and Machine
Learning 4 - 2 2 4 2
MCS302 Artificial Intelligence 4 - 2 2 4 2
MCS303 Mobile Application Development 4 - 2 2 4 2
MCS304 Information and Cyber Security 4 - 2 2 4 2
MCSL305 Big Data Analytics Lab -
2 1 1 2 1
MCSL306 Machine Learning Lab - 2 1 1 2 1
MCSL307 Mobile Application Development
Lab - 2 1 1 2 1
MCSL308 Ethical Hacking Lab - 2 1 1 2 1
Total 16 8 24 -
SEMESTER-III
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 6
M.Sc. (COMPUTER SCIENCE) SEMESTER - IV (SECOND YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int.
Ext.
Total
MCS401 Cloud Computing 4 - 2 2 4 2
MCS402 Elective II 4 - 2 2 4 2
MCSL403 Research Paper Writing - 4 2 2 4 -
MCSL404 Software Project - 12 6 6 12 -
Total 8 16 24 -
SEMESTER-IV
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
Elective II
Course Code Course Nomenclature
MCS402A Digital Image Processing
MCS402B Robotics
MCS402C Blockchain Technology
MCS402D Modeling and Simulation
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 7
M.SC. (COMPUTER SCIENCE)
SYLLABUS
M.Sc. (COMPUTER SCIENCE) SEMESTER - I (FIRST YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int. Ext. Total
MCS101 Programming Concepts and Design,
Analysis of Algorithms 4 - 2 2 4 2
MCS 102
Data Communication and
Networking
4 - 2 2 4 2
MCS103 Operating Systems 4 - 2 2 4 2
MCS 104 Software Engineering 4 - 2 2 4 2
MCSL105 Programming Concepts Lab
- 2 1 1 2 1
MCSL106 Networking Lab - 2 1 1 2 1
MCSL107 Software Testing Lab - 2 1 1 2 1
MCSL108 Advanced Web Technology Lab - 2 1 1 2 1
Total 16 8 24 -
SEMESTER-I
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
COURSE: PROGRAMMING CONCEPTS AND DESIGN, ANALYSIS OF ALGORITHMS CREDIT - 04 Objectives:
To introduce students to the programming concepts
To introduce the classic algorithms in various computer domains, and techniques for designing efficient algorithms.
To make the students aware of and well-trained in the use of the tools and Techniques of designing and analyzing algorithms.
Outcomes:
The course will help:
To prove the correctness and analyze the running time of the basic algorithms for those classic problems in various domains;
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 8
To apply the algorithms and design techniques to solve problems
To appreciate the impact of algorithm design in practice
To analyze the complexities of various problems in different domains.
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS101 Programming Concepts and
Design, Analysis of Algorithms 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
students to
programming
concepts
Programming Concepts
Object Oriented Programming, Review of OOP -
Objects and classes, inheritance, polymorphism,
abstraction, Event driven programming, graphics
programming, event handling, generic programming –
generic classes – generic methods – generic code and
virtual machine
Assignment
(Marks–05)
2
To explain and use
various types of
analyses of
algorithms
To study the role of
available tools in
solving a problem;
Design strategies and Analysis of Algorithms Role of Algorithms in Computing: Algorithms as a
technology, Characteristics and building blocks of
Algorithm. Getting Started: Designing algorithms,
Well known Sorting algorithms (Insertion sort,
Bubble Sort, Selection Sort, Shell Sort, Heap Sort).
Divide-and-Conquer Technique: The maximum-
subarray problem, Integer Multiplication, Strassen’s
algorithm for matrix multiplication, the substitution
method for solving recurrences. Probabilistic
Analysis and Randomized Algorithms: The hiring
problem, Indicator random variables, Randomized
algorithms.
Analyzing algorithms, Growth of Functions: Some
Useful Mathematical Functions & Notations,
Asymptotic Functions & Notation.
Unit Test-1
(Marks-25)
3
To study and apply
the dynamic
programming and
greedy algorithms
for solving
problems.
Advanced Design
Dynamic Programming: Rod cutting, Elements of
dynamic programming, longest common
subsequence, The Problem of Making Change, Matrix
Multiplication Using Dynamic Programming. Greedy
Algorithms: An activity-selection problem, Elements
of the greedy strategy, Huffman codes, Minimum
Spanning Trees, Prim’s Algorithm, Kruskal’s
Algorithm, Dijkstra’s Algorithm.
Oral
Presentation
(Marks 10)
4
To study and apply
various graph
search techniques.
Graph Algorithms
Representations of graphs, Traversing Trees, Breadth-
first search, Depth-first search, Best-First Search &
Minimax Principle, Topological Sort. Single-Source
Shortest Paths: The Bellman-Ford algorithm, Single-
source shortest paths in directed acyclic graphs
Class Test
(Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 9
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1. Richard F Gilberg, B. A. (2005). Data Structure A Pseudocode Approach with C. (Second
ed.). USA: Cengage Publisher.
2. Thomas H. Cormen, C. E. (2009). Introduction to Algorithms (Third ed.). New Delhi: PHI
Learning Pvt. Ltd.
REFERENCE BOOKS:
1. Bhargava, A. (n.d.). 1. Grokking Algorithms: An illustrated guide for programmers and
other curious people http://www.manning.com/bhargava. India: MEAP.
2. Lipschutz, S. (2014). Shaum‟s Outlines Data Structure. TMH.
3. Sanjoy Dasgupta, C. H. (2006). Algorithms. India: McGraw-Hill Higher Education.
4. T.Goodrich, M. (2010). Data Structures and Algorithms in C++. Wiley Publications.
_______________________________________________________________________________
COURSE: DATA COMMUNICATION AND NETWORKING
CREDIT - 04
Objectives:
To help students to get a grounding of network components and architecture.
To explore networking models.
To learn the way protocols are used in networks and their design issues.
Outcomes:
The students will be able to:
Comprehend the basic concepts of computer networks and data communication systems.
Analyse basic networking protocols and their use in network design
Explore various advanced networking concepts.
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS102
Data Communication and
Networking
4 - 2 2 4 2
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 10
Module
No.
Objective Content Evaluation
1
To introduce to
basic concepts of
networking
Introduction to Networking
Internet and Intranet, Protocol layer and their services,
Network Applications like Web, HTTP, FTP and
Electronic Mail in the Internet, Domain Name System,
Transport-Layer Services, Multiplexing and
Demultiplexing, UDP, TCP, TCP Congestion Control,
Network Layer, Virtual Circuit and Datagram
Networks, Need of Router, The Internet Protocol (IP),
Routing Algorithms, Routing in the Internet.
Students will
be evaluated
by taking
viva.
(Marks 05)
2
To elaborate
network
virtualization
Network Virtualization
Need for Virtualization, The Virtual Enterprise,
Transport Virtualization-VNs, Central Services Access:
Virtual Network Perimeter, A Virtualization
Technologies primer: theory, Network Device
Virtualization, Data-Path Virtualization, Control-Plane
Virtualization, Routing Protocols.
Written Unit
Test – I
(Marks 25)
3
To elaborate the
concept of Adhoc
networking
Adhoc Networking
Introduction, application of MANET, challenges,
Routing in Ad hoc networks, topology & position-based
approaches, Routing protocols: topology based, position
based, Broadcasting, Multicasting, & Geocasting,
Wireless LAN, Transmission techniques, MAC protocol
issues, Wireless PANs, The Bluetooth technology.
Written Class Test will be conducted. (Marks 10)
4
To elaborate
wireless sensor
networks
Wireless sensor networks
Need and application of sensor networks, sensor
networks design considerations, empirical energy
consumption, sensing and communication range, design
issues, localization scheme, clustering of SNs, Routing
layer, Sensor networks in controlled environment and
actuators, regularly placed sensors, network issues,
RFID as passive sensors.
Assignments
will be given
for the above
topics.
(Marks 10)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1. Carlos de Morais Cordeiro, D. P. (2011). Ad Hoc and Sensor Networks: Theory and
Applications ( 2nd edition ed.). World Scientific Publishing Company.
2. James F. Kurose, K. W. (2012). Computer Networking: A Top-Down Approach (6th edition
ed.). Pearson.
3. Victor Moreno, K. R. (2006). Network Virtualization. Cisco Press.
REFERENCE BOOKS:
1. Carlos de Morais Cordeiro, D. P. (2011). Ad Hoc and Sensor Networks: Theory and
Applications ( 2nd edition ed.). World Scientific Publishing Company.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 11
2. Forouzan, B. (2009). TCP/IP Protocol Suite (4 edition ed.). McGraw-Hill Science.
3. Garg, V. (2002). Wireless network evolution: 2G to 3G. Prentice Hall.
4. James F. Kurose, K. W. (2012). Computer Networking: A Top-Down Approach (6th edition
ed.). Pearson.
5. Jonathan Loo, J. L. (2011). Mobile Ad Hoc Networks: Current Status and Future Trends.
CRC Press .
6. Schiller, S. J. (2012). Mobile Communications (Second Edition ed.). Pearson Education.
7. Stallings, W. (2013). Wireless Communications and Networks. Pearson Education.
8. Stojmenovic, I. (2010). Handbook of Wireless Networks and Mobile Computing (Wiley
India Edition ed.).
9. Victor Moreno, K. R. (2006). Network Virtualization. Cisco Press.
_______________________________________________________________________________
COURSE: OPERATING SYSTEMS
CREDIT - 04
Objectives:
To learn the fundamentals of Operating Systems.
To learn the mechanisms of operating system to handle processes and threads and their
communication
To learn the mechanisms involved in memory management in contemporary operating
systems
Outcomes:
The students will be able to:
Analyze the structure of OS and basic architectural components involved in operating
system design
Conceptualize the components involved in designing a contemporary operating system
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS103 Operating Systems 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce to
basic concepts of
operating systems
Introduction to Operating System
Introduction to Linux kernel, Types of kernel
(monolithic, micro, exo), Operating system booting
process GRUB-I, GRUB-II. Processes, Interprocess
Communication, Scheduling.
Written
Unit Test –
I
(Marks 25)
2
To elaborate
memory
management in
Memory management and virtual memory in Linux
Basic memory management, swapping, virtual memory,
Page replacement algorithms, Design issues for paging
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 12
operating system systems, segmentation. Case Study: Linux memory
management.
3
To elaborate the
concept of Input
and Output
operations
Input/ Output in Linux Principles of I/O Hardware, Principles of I/O Software,
Deadlocks, RAM Disks, Disks, Terminals. File Systems:
Files, Directories, File System Implementation, Security,
Protection mechanisms in different Linux versions
Written Class Test will be conducted. (Marks 10)
4
To elaborate
android operating
system
Android Operating System
The Android Software Stack, The Linux Kernel – its
functions, essential hardware drivers. Libraries - Surface
Manager, Media framework, SQLite, WebKit, OpenGL.
Android Runtime - Dalvik Virtual Machine, Core Java
Libraries. Application Framework - Activity Manager,
Content Providers, Telephony Manager, Location
Manager, Resource Manager. Android Application –
Activities and Activity Lifecycle, applications such as
SMS client app, Dialer, Web browser, Contact manager
Assignment
s will be
given for
the above
topics.
(Marks 15)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1. Avi Silberschatz, P. B. (2009). Operating System Concepts with Java ( Eight Edition ed.).
John Wiley & Sons, Inc.
2. Evi Nemeth, G. S. (2011). UNIX and Linux System Administration Handbook ( Fourth
Edition ed.). Pearson Education, Inc.
3. Meier, R. (2012). PROFESSIONAL Android™ 4 Application Development. John Wiley &
Sons, Inc. .
4. Pramod Chandra, P. B. (2014). An Introduction to Operating Systems: Concepts and
Practice (GNU/Linux) (4th edition ed.).
REFERENCE BOOKS:
1. Andrew S. Tanenbaum, A. S. (2006). Operating Systems: Design and Implementation
(Third Edition ed.). Prentice Hall.
2. Developers, A. (n.d.).
3. Documentation, F. (n.d.).
4. Documentation, O. U. (n.d.).
_______________________________________________________________________________
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 13
COURSE: SOFTWARE ENGINEERING
CREDIT: 4
Objectives:
The basic objective of software engineering is to develop methods and procedures
for software development that can scale up for large systems.
It can be used consistently to produce high-quality software at low cost and with a small
cycle of time.
Outcome:
Students will be able to:
Apply use of knowledge of Software Life Cycle to successfully implement the projects in
the corporate world
Identify the Inputs, Tools and techniques to get the required Project deliverable and Product
deliverable using knowledge areas of Project Management.
Code Course
Teaching Period /
Week Credit
Duration
of
Theory
Exam
(in Hrs.) L Pr./ Tu Int. Ext. Total
MCS104 Software Engineering 4 - 2 2 4 2
Module
No
Objective Content Evaluation
1 The objective of
this module is to
introduce the
student to the basic
foundations of
software
development using
software
engineering
principles.
Introduction to software engineering and project
management
Introduction to Software Engineering, Software
Components, Software Characteristics, Software
Crisis, Software Engineering Processes, Similarity
and Differences from Conventional, Engineering
Processes, Software Quality Attributes. Software
Development Life Cycle (SDLC), Models: Water
Fall Model, Prototype Model, Spiral Model,
Evolutionary Development Models, Iterative
Enhancement Models.
Unit Test-1
(Marks-25)
2
To introduce
students to
Software
Requirement
elicitation
techniques
Software Requirement Analysis and Specification
Requirement Engineering Process: Elicitation,
Analysis, Documentation, Review and Management
of User Needs, Feasibility Study, Information
Modeling, Data Flow Diagrams, Entity Relationship
Diagrams, Data Dictionary Decision Tables, SRS
Document, IEEE Standards for SRS. Requirement
Elicitation: Interviews, Questionnaire,
Brainstorming, Facilitated Application Specification
Technique (FAST), Use Case Approach. SRS Case
study.
Online Test
(Marks-15)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 14
3
This will introduce
the students to the
basic concepts of
software project
scheduling &
design
Software Project Planning and Scheduling
Business Case, Project selection and Approval,
Project charter, Project Scope management: Scope
definition and Project Scope management, Creating
the Work Breakdown Structures, Scope
Verification, Scope Control. Staffing Level
Estimation, Effect of schedule Change on Cost,
Degree of Rigor & Task set selector, Project
Schedule, Schedule Control
Software Design
Basic Concept of Software Design, Architectural
Design, Low Level Design: Modularization, Design
Structure Charts, Pseudo Codes, Flow Charts,
Coupling and Cohesion Measures, Design
Strategies: Function Oriented Design, Object
Oriented Design, Top-Down and Bottom-Up
Design. Software Measurement and Metrics:
Various Size Oriented Measures: Halestead’s
Software Science, Function Point (FP) Based
Measures, Cyclomatic Complexity Measures:
Control Flow Graphs.
4
To understand the
importance of
Software Testing
strategies and
Quality Assurance
during the software
development
process.
Software Testing and Quality Assurance
Testing Objectives, Unit Testing, Integration
Testing, Acceptance Testing, Regression Testing,
Testing for Functionality and Testing for
Performance, Top-Down and Bottom-Up Testing
Strategies: Test Drivers and Test Stubs, Structural
Testing (White Box Testing), Functional Testing
(Black Box Testing), Test Data Suit Preparation,
Alpha and Beta Testing of Products.
Static Testing Strategies: Formal Technical Reviews
(Peer Reviews), Walk Through, Code Inspection,
Compliance with Design and Coding Standards
Software Quality Assurance (SQA): Verification
and Validation, SQA Plans, Software Quality
Frameworks, ISO 9000 Models, SEI-CMM Model.
Assignment
(Marks-5)
5 The objectives of
this module is to
introduce the
fundamentals of
software costing and
maintenance
To describe three
metrics for software
productivity
assessment.
Software Maintenance and Software Project
Management
Software as an Evolutionary Entity, Need for
Maintenance, Categories of Maintenance:
Preventive, Corrective and Perfective Maintenance,
Cost of Maintenance, Software Re- Engineering,
Reverse Engineering. Software Configuration
Management Activities, Change Control Process,
Software Version Control, An Overview of CASE
Tools. Software Estimation: Size Estimation:
Function Point (Numericals). Cost Estimation:
COCOMO (Numericals), COCOMO-II
(Numericals). Software Risk Analysis and
Management.
Assignment
(Marks-5)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 15
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal50 + External 50 = 100
TEXT BOOKS:
1. Pressman, R. S. (2019). Software Engineering (5th and 7th edition ed.). McGraw Hill
publication.
2. Schwalbe, K. (2011). Managing Information Technology Project (6th edition ed.). Cengage
Learning publication.
REFERENCE BOOKS:
1) Bell, D. (2005). Software Engineering for students: A Programming Approach. Pearson
publication.
2) Inc(), K. L. (2012). Software Engineering. Dreamtech Press. .
3) KK Agrawal, Y. S. (2007). Software Engineering (3rd
ed.). New Age International
publication.
4) Marchewka, J. T. (2013). Information Technology Project Management. Wiley India
publication.
_______________________________________________________________________________
COURSE: PROGRAMMING CONCEPTS LAB
CREDIT: 2
Objectives:
Identify the way of implementation algorithms required for sorting searching, sorting array
Identify the method of implementation of graph related algorithms
Outcomes:
The students will be able to:
Understand the concept of implementation of various algorithms
Understand the measuring of performance values of various algorithms
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL105 Programming Concepts Lab
- 2 1 1 2 1
Module
No
Objective Content Evaluation
1 To implement sorting
algorithms
Implementation of Sorting
Algorithms
Insertion sort, Bubble Sort, Selection
Sort, Shell Sort
Students will be
evaluated using Lab
Manual. (Marks 5)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 16
2
To implement divide
and conquer method-
based algorithms
Implementation of Algorithms based
on divide and conquer
Quick sort implementation, Binary
search algorithm
Class Test
(Marks 10)
3
To implement shortest
path and minimum
spanning tree
algorithm
Implementation of MST and Shortest
path algorithm
Find Minimum Cost Spanning Tree of a
given undirected graph using Kristal‟s
algorithm, from a given vertex in a
weighted connected graph, find shortest
paths to other vertices using Dijikstra‟s
algorithm.
4
To implement graph
traversal algorithms
Implementation of Graph Algorithms Traverse a graph using Breadth-first
search, Depth-first search
Practical Exam will be
conducted.
(Marks 10)
Programming Language: C/C++
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOKS:
1) Narasimha Karumanchi, (2016), Data Structures and Algorithms Made Easy: Data
Structures and Algorithmic Puzzles, CareerMonk Plublications
REFERENCE BOOKS:
1. Bhargava, A. (2016). Grokking Algorithms: An illustrated guide for programmers and
other curious people. MEAP.
2. Sanjoy Dasgupta, C. H. (2006). Algorithms. McGraw-Hill Higher Education .
3. Thomas H. Cormen, C. E. (2009). Introduction to Algorithms (Third ed.). New Delhi: PHI
Learning Pvt. Ltd.
____________________________________________________________________________
COURSE: NETWORKING LAB
CREDIT: 2
Objectives:
This practical subject introduces the student actual implementation of various types of
networks using simulating software
The objective of this subject is to give hands on experiment of hardware establishment of
networks using simulating software
Outcomes:
The students will be able to configure various types of networks
Students will implement various networks using simulating software
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 17
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL106 Networking Lab - 2 1 1 2 1
Module
No.
Objective Content Evaluation
1
To introduce
students to IDE of
simulating software
Study of simulating software interface
Basic Configuration of router, assigning
ipv4 and ipv6 addresses to the interfaces of
the routers
Lab manual for 05
marks
2
To elaborate the
configuration of
VLANs and PPP
Configure VLANs on the router, Spanning
tree, Configuration of PPP
Online test of 10
marks
3
To demonstrate the
configuration of
RIPv2, EIGRP and
OSPF
Configure RIPv2, Configure EIGRP,
Configure OSPF
4
To implement
configuration of
switch
Access List Configuration, Configuration of
NAT, Configuration of DCHP,
Configuration of switch
Practical test of 10
marks
Practical’s to be done Packet Tracer (or other simulating software)
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOK:
1. A., F. B. (2004). Computer Networks, PHI Data Communication and Networking (Third
ed.). McGraw Hill.Andrew Tenenbaum.
REFERENCE BOOKS:
1. Keshav, S. (2002). An Engineering Approach to Computer Networking. Addision-Wesley.
2. Kurose, J. ,. (2005). Computer Networking: A Top-Down Approach Featuring the Internet
(Third ed.). Addison-Wesley.
_______________________________________________________________________________
COURSE: SOFTWARE TESTING LAB
CREDIT: 2
Objectives:
Identify the need of software testing in current industry scenario, techniques and tools in
area of software testing
Demonstrate the ability to apply multiple methods to check reliability of a software system
and to identify and apply redundancy and fault tolerance for a medium-sized application,
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 18
Identify the Fault in program logic that fails to validate data and values properly before
they are used
Discuss the distinctions between validation and defect testing,
Understand types of testing and essential characteristics of tool used for test automation
Outcomes:
The students will be able to:
Understand the concept and need of software testing
Understand the need and usage of software tools required for manual and automated testing
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL107 Software Testing Lab - 2 1 1 2 1
Module
No
Objective Content Evaluation
1
To understand
the concepts of
software testing
Introduction to Software Testing
Functional and non-functional Testing, Writing Test cases,
Testing Framework, Test Documents, Static Testing: Data
Flow Analysis, Control Flow Analysis, Cyclomatic
Complexity, White Box Testing: Statement Coverage,
Branch Coverage, Path Coverage, State Transition, Black
Box Testing: Equivalence Class Partitioning, Boundary
Value Analysis, Cause Effect Graphing and Decision table
technique, Use case testing
Students
will be
evaluated
using Lab
Manual.
(Marks 5)
2
To perform
manual testing
Software Testing Strategies and Manual Testing
Characteristics, Integration Testing, Functional Testing,
Object-oriented Testing, Alpha and Beta Testing, overview
of testing tools, Manual Testing on existing Project
Class Test
(Marks 10)
3
To perform
automation
testing using
QTP
Automation Testing using QTP
QTP Introduction, recording and replaying test cases, QTP
Synchronization Point, QTP Parameterization, QTP
Checkpoints (Windows and Web application), Recording
modes in QTP
4
To perform
automation
testing using
Bugzilla
Automation Testing using Bugzilla Bugzilla Introduction and usage, Creating Reporting a new
bug, Viewing Bug reports, Modifying Bug reports,
Performance Testing Concepts: Load Testing, Stress
Testing
Practical
Exam will
be
conducted.
(Marks 10)
Note: Manual Testing (MT), Automation Testing (AT)
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 19
REFERENCE BOOKS:
1. Shende. (2010). Testing in 30 + open source tools, . SPD.
2. Spillner, D. (2014 ). Software testing foundations. SPD.
____________________________________________________________________________
COURSE: ADVANCED WEB TECHNOLOGY LAB
CREDIT: 2
Objectives:
The students will Study the architecture of Dot Net framework
Understand the basic principles of website development using IDE
Learn advanced windows and web development techniques using dot NET
Outcomes:
The students will be able to create user interface-based applications
Design and develop secure web applications using asp.net according to industry standards
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL108 Advanced Web Technology Lab - 2 1 1 2 1
Module
No.
Objective Content Evaluation
1
To introduce
students to
IDE of Asp.net
web
application
Asp.Net Web Application
ASP.net server controls: Button, TextBox,
Labels, CheckBoxex, Radio Buttons, List
Controls. Web config and global.aspx files, data
types, variables, statements, organizing code
Lab manual for 05
marks
2
To elaborate
the use of
validation
controls in
asp.net
Validation Control
Validation techniques, state, management using
view state, using session state, using application
state, using cookies and URL encoding, Master
page, content pages, nesting master pages,
accessing master page controls from a control
page, Site navigation Controls
Online test of 10
marks
3
To
demonstrate
the use of data
base
connectivity
Database Connectivity
Introduction, using SQL data sources, GridView
Control, DetailView and FormView Controls,
ListView and DataPager Controls in ASP.NET
Practical exam of
10 marks
4
To implement
LINQ with
asp.net
LINQ
Operators, implementation, LINQ to objects,
XML and ADO.net, AJAX: Introduction and
working, asp.net Ajax server control, JQuery:
Introduction, UI Library, working
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 20
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25= 50
TEXT BOOKS:
1. Walther, S. (2010). ASP.NET MVC Framework. Unleashed.
2. Walther, S. (2011). ASP.NET 3.5 Unleashed. SAMS Publishing.
REFERENCE BOOKS:
1. Christian Nagel, Bill Evjen, Jay Glynn, Karli Watson, Morgan Skinner(2012) , Professional
C# and .NET 4.5. Wrox Publication.
2. Andrew Stellman, Jennifer Greene (2013), Head First C# ( Second Edition ed.). O’Reilly.
3. Benjamin Perkins, Jacob Vibe Hammer, Jon D. Reid (2017), C#, B.,Wrox Publication.
4. Kamal, R. (2017). Internet and Web Technologie. Tata McGraw Hill.
5. Mukhi, V. (2003). C# with Visual Studio. BPB.
6. Murach’s. (2010). ADO. Net 4 Database Programming with C# (4th Edition ed.).
7. Murach’s. (2010). ASP. Net 4. 0 Web Programming with C#.
8. Patel, C. (2010). Advance .NET Technology ( second edition ed.). DreamTech Press.
9. Ralph Moseley & M. T. Savaliya. (2011). Developing Web Application (Second Editon
ed.). Wiley.
10. Swedberg, J. C. (2013). Learning jQuery (Third Edition ed.). SPD Publication.
11. Trolsen, A. (2012). Pro C# 5.0 and the .NET 4.5 Framework. APress.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 21
M.Sc. (COMPUTER SCIENCE) SEMESTER - II (FIRST YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int.
Ext.
Total
MCS201 Mobile Communication and
Wireless Technology 4 - 2 2 4 2
MCS202 Data Analytics and Mining
4 - 2 2 4 2
MCS203 Research Methods and Statistical
Analysis 4 - 2 2 4 2
MCS204 Elective I 4 - 2 2 4 2
MCSL205 Data Analytics and Mining Lab
- 2 1 1 2 1
MCSL206 Statistics Lab - 2 1 1 2 1
MCSL207 Advanced Java Lab - 2 1 1 2 1
MCSL208 Advanced Python Lab - 2 1 1 2 1
Total 16 8 24 -
SEMESTER-II
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
Elective - I
Course Code Course Nomenclature
MCS204A Distributed Systems
MCS204B Computer Graphics
MCS204C Advanced Python
MCS204D Natural Language Processing
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 22
COURSE: MOBILE COMMUNICATION AND WIRELESS TECHNOLOGY
CREDIT - 04
Objectives:
To learn the concepts of wireless communication and mobile networks
To identify different wireless technologies and its applications
To acquire knowledge on generation of cellular networks and its standards used
Outcomes:
The students will be able to:
Understand the concept of cellular communications, advantages and its limitations
Compare the various wireless technologies and its applications
Apply the appropriate technology in the applications
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS201 Mobile Communication and
Wireless Technology 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
to basic
concepts of
wireless
networking
Fundamentals of Wireless Technology
Introduction to Mobile and wireless communications,
Overview of radio transmission frequencies, Signal
Antennas, Signal Propagation, Multiplexing – SDM,FDM,
TDM,CDM, Modulation – ASK,FSK,PSK, Advanced
FSK, Advanced PSK, OFDM, Spread Spectrum –
DSSS,FHSS, Wireless Transmission Impairments – Free
Space Loss, Fading, Multipath Propagation, Atmospheric
Absorption, Error Correction – Reed Solomon, BCH,
Hamming code, Convolution Code (Encoding and
Decoding),
Students will
be evaluated
by taking
viva.
(Marks 05)
2
To elaborate
wireless and
cellular
wireless
network
Wireless and Cellular wireless Networks
Wireless network, Wireless network Architecture,
Classification of wireless networks – WBAN, WPAN,
WLAN, WMAN, WWAN., IEEE 802.11, IEEE 802.16,
Bluetooth – Standards, Architecture and Services, Cellular
wireless Networks, Principles of cellular networks –
cellular network organization, operation of cellular
systems, Handoff., Generation of cellular networks – 1G,
2G, 2.5G, 3G and 4G.
Written Unit
Test – I
(Marks 25)
3
To elaborate
the concept of
mobile
communicatio
n system
Mobile Communication System
GSM – Architecture, Air Interface, Multiple Access
Scheme, Channel Organization, Call Setup Procedure,
Protocol Signaling, Handover, Security, GPRS –
Architecture, GPRS signaling, Mobility management,
Written Class Test will be conducted. (Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 23
GPRS roaming, network, CDMA2000- Introduction,
Layering Structure, Channels,Logical Channels, Forward
Link and Reverse link physical channels, W-CDMA –
Physical Layers, Channels, UMTS – Network
Architecture, Interfaces, Network Evolution, Release 5,
FDD and TDD, Time Slots, Protocol Architecture, Bearer
Model, Introduction to LTE
4
To elaborate
different layers
of mobile
network
Mobile network, transport and application layers
Mobile IP – Dynamic Host Configuration Protocol, Mobile
Ad Hoc Routing Protocols– Multicast routing, TCP over
Wireless Networks – Indirect TCP – Snooping TCP –
Mobile TCP – Fast Retransmit / Fast Recovery
Transmission/Timeout Freezing-Selective Retransmission
– Transaction Oriented TCP , TCP over 2.5 / 3G wireless
Networks, WAP Model- Mobile Location based services -
WAP Gateway –WAP protocols – WAP user agent profile,
Caching model-wireless bearers for WAP - WML –
WMLScripts – WTA.
Assignments
will be given
for the above
topics.
(Marks 10)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1. Garg, V. K. (2011). Wireless Network Evolution 2G to 3G. Pearson Publications.
2. Misra, S. (2010). Wireless Communications and Networks, 3G and Beyond (Second ed.).
McGraw Hill Education.
REFERENCE BOOKS:
1. Dr. Sunilkumar S. Manvi, M. S. (2010). Wireless and Mobile Networks, Concepts and
Protocols. Wiley India.
2. K. Fazel, S. K. (2010). Multi-Carrier and Spread Spectrum Systems - From OFDM and
MC-CDMA to LTE and WiMAX (Second Edition ed.). Wiley publications.
3. Yi Bang Lin, I. (2008). Wireless and Mobile Network Architectures. Wiley India.
4. Yi-Bing Lin, A.-C. P. (2012). Wireless and Mobile All-IP Networks. Wiley Publications.
COURSE: DATA ANALYTICS AND MINING CREDIT - 4 Objectives:
To acquire the knowledge of various concepts and tools behind mining data for business intelligence
To Study data mining algorithms, methods and tools
To Identify business applications of data mining
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 24
Outcomes:
The students will be able to:
Apply data mining concepts for data analysis and report generation
Develop industry level data mining skills using software tools
Make use of relevant theories, concepts and techniques to solve real-world business
problems
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS202 Data Analytics and Mining
4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
This module introduces students to the concept of data analytics
Data Analytics
Introduction, Data Summarization and
visualization, Linear, Non-linear regression, model
selection
Online Test
(Marks 5)
2
This module provides background on data objects and statistical concepts. It introduces techniques for preprocessing data before mining.
Data Mining and Data Preprocessing What is data mining?, Knowledge discovery- KDD
process, related technologies - Machine Learning,
DBMS, OLAP, Statistics, Data Mining Goals,
stages of the Data Mining Process, Data Mining
Techniques, Knowledge Representation Methods.
Data cleaning, Data transformation, Data reduction,
Discretization and generating concept hierarchies.
introduction to data warehousing, OLAP, and data
generalization. Data Cube Computation and
Multidimensional Data Analysis
Written
Unit Test –
I
(Marks 25)
3
This unit covers supervised learning method as classification and Prediction
Classification and Prediction
Decision tree, Bayesian classification, rule-based
classification, neural networks, support vector
machines, associative classification, k-nearest-
neighbor classifier, case-based reasoning.
Assignmen
ts will be
given for
the above
topics.
(Marks 10)
4
This unit covers unsupervised learning method as clustering and association rule mining To gain detailed insights of outlier detection
Clustering and Association Rule Mining
Partitioning, hierarchical, density-based, grid-
based, and model-based methods data clustering. Mining Frequent Patterns, Associations, and
Correlations
Outlier Detection: Detection of anomalies, such as
the statistical, proximity-based, clustering-based,
and classification-based methods.
Assignmen
ts will be
given for
the above
topics. (Marks 10)
EVALUATION:
1) On Four Modules of 50 marks 2) Final examination of 50 marks 3) Total marks = Internal 50 + External 50 = 100
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 25
TEXT BOOKS:
1. Avi Silberschatz, H. F. (2010). Database System Concepts (5th edition ed.). McGraw-Hill.
2. Chawla, S. S. (2003). Spatial Databases: A Tour. Prentice Hal.
REFERENCE BOOKS:
1. Gehrke, R. R. (2002). Database Management Systems (3rd edition ed.). McGraw-Hill.
2. M. Böhlen, J. G. (2006). Multi-dimensional aggregation for temporal data. . In Proc. of
EDBT.
3. Navathe, E. a. (2003). Fundamentals of Database Systems (6thEdition ed.). Addison.
Wesley.
4. Pelagatti, S. C. (1984). Distributed Database; Principles & Systems. McGraw-Hill
International Editions.
5. Ponniah, P. (2010). Data Warehousing fundamentals. JohnWiley.
6. Schneider, R. G. (2005). Moving objects databases. Morgan Kaufmann Publishers, Inc.
7. Singh, S. K. (2011). Database Systems: Concepts, Design and Applications (2nd edition
ed.). Pearson Publishing.
_______________________________________________________________________________
COURSE: RESEARCH METHODS AND STATISTICAL ANALYSIS
CREDIT: 4
Objectives:
To understand Research and Research Process
To acquaint students with identifying problems for research and develop research strategies
To familiarize students with the techniques of data collection, analysis of data and
interpretation
Outcome:
Students will be able to:
Prepare a preliminary research design for projects in their subject matter areas
Accurately collect, analyse and report data
Present complex data or situations clearly
Review and analyse research findings Get the knowledge of objectives and types of
research
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS203 Research Methods and Statistical
Analysis 4 - 2 2 4 2
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 26
Module
No
Objective Content Evaluation
1
To introduce
students to the
concept of research
Introduction to Research methodology
An Introduction Objectives of Research, Types of
Research, Research Methods and Methodology,
defining a Research Problem, Techniques involved
in Defining a Problem
Unit Test-1
(Marks-25)
2
To elaborate
importance of
literature review
and research design
Review of Literature, Research Design
Need for Research Design, Features of Good
Design, Different Research Designs, Basic
Principles of Experimental Designs, Sampling
Design, Steps in Sampling Design, Types of
Sampling Design, Sampling Fundamentals,
Estimation, Sample size Determination, Random
sampling. Measurement and Scaling Techniques
Measurement in Research
3
To learn data
collection and
processing methods
Data Collection and Processing
Methods of Data Collection and Analysis Collection
of Primary and Secondary Data, Selection of
appropriate method Data Processing Operations,
Elements of Analysis.
Assignment
(Marks-10)
4
To learn data
analysis and
presentation of the
results
Statistical Analysis and Presentation
Statistics in Research, Measures of Dispersion,
Measures of Skewness, Regression Analysis,
Correlation, Quantitative data analysis, Techniques
of Hypotheses, Parametric or Standard Tests Basic
concepts, Tests for Hypotheses I and II, Important
parameters limitations of the tests of Hypotheses,
Chi-square Test, Comparing Variance, As a non-
parametric Test, Conversion of Chi to Phi, Caution
in using Chi-square test, representation of research.
Online Test
(Marks-15)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal50 + External 50 = 100
TEXT BOOKS:
1. Oates, B. J. (2006). Researching Information Systems and Computing. Sage Publications
India Pvt Ltd.
REFERENCE BOOKS:
1. Kahn, J. W. (2010). Research in Education. PHI Publication.
2. Kothari, C. (1985). Research Methodology, Methods and Techniques (third edition ed.).
New Age International.
3. Strauss, J. C. (2008). Basic of Qualitative Research (3rd Edition ed.). Sage Publications.
4. Willkinson K.P, L. B. (2010). Formulation of Hypothesis. Mumbai : Hymalaya Publication.
_______________________________________________________________________________
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 27
COURSE: ELECTIVE I – DISTRIBUTED SYSTEMS CREDIT - 4 Objectives:
To learn the principles, architectures, algorithms and programming models used in distributed systems.
To examine state-of-the-art distributed systems, such as Google File System.
To design and implement sample distributed systems.
To transform students’ computational thinking from designing applications for a single computer system, towards that of distributed systems.
Outcomes: The students will be able to:
Identify the core concepts of distributed systems: the way in which several machines orchestrate to correctly solve problems in an efficient, reliable and scalable way.
Examine how existing systems have applied the concepts of distributed systems in designing large systems, and will additionally apply these concepts to develop sample systems.
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS204A Distributed Systems 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
This module
will enable
students to
introduce
concepts
related to
distributed
computing
systems.
Characterization of Distributed Systems Introduction, Examples of distributed Systems, Resource
sharing and the Web Challenges. Architectural models,
Fundamental Models. Theoretical Foundation for
Distributed System: Limitation of Distributed system,
absence of global clock, shared memory, Logical clocks,
Lamport’s & vectors logical clocks. Concepts in Message
Passing Systems: causal order, total order, total causal
order, Techniques for Message Ordering, Causal ordering
of messages, global state, termination detection.
Written Unit
Test – I
(Marks 25)
2
This module covers solutions to the problem of mutual exclusion, which is important for correctness in distributed systems with shared resources.
Distributed Mutual Exclusion
Classification of distributed mutual exclusion, requirement
of mutual exclusion theorem, Token based and nontoken-
based algorithms, performance metric for distributed
mutual exclusion algorithms. Distributed Deadlock
Detection: system model, resource Vs communication
deadlocks, deadlock prevention, avoidance, detection &
resolution, centralized dead lock detection, distributed
dead lock detection, path pushing algorithms, edge
chasing algorithms.
Assignments
will be given
for the above
topics.
(Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 28
3
To introduce
students to the
concept of
Agreement
protocol and
the abstraction
& use of file
systems
Agreement Protocols
Introduction, System models, classification of Agreement
Problem, Byzantine agreement problem, Consensus
problem, Interactive consistency Problem, Solution to
Byzantine Agreement problem, Application of Agreement
problem, Atomic Commit in Distributed Database system.
Distributed Resource Management: Issues in distributed
File Systems, Mechanism for building distributed file
systems, Design issues in Distributed Shared Memory,
Algorithm for Implementation of Distributed Shared
Memory.
Assignments
will be given
for the above
topics.
(Marks 5)
4
The students
will learn
about the
Failure
Recovery in
Distributed
Systems and
Fault
Tolerance
concepts
Failure Recovery in Distributed Systems
Concepts in Backward and Forward recovery, Recovery in
Concurrent systems, Obtaining consistent Checkpoints,
Recovery in Distributed Database Systems. Fault
Tolerance: Issues in Fault Tolerance, Commit Protocols,
Voting protocols, Dynamic voting protocols.
Online Class
test will be
conducted.
(Marks 5)
5
The students
will
understand the
transactions
and
concurrency
Control
mechanisms in
Distributed
systems
Transactions and Concurrency Control
Transactions, Nested transactions, Locks, Optimistic
Concurrency control, Timestamp ordering, Comparison of
methods for concurrency control. Distributed
Transactions: Flat and nested distributed transactions,
Atomic Commit protocols, Concurrency control in
distributed transactions, Distributed deadlocks,
Transaction recovery. Replication: System model and
group communication, Fault - tolerant services, highly
available services, Transactions with replicated data.
Online Class
test will be
conducted.
(Marks 5)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) Ramakrishna, G. (2007). Database Management Systems. Mc Grawhill.
2) Shivaratri, S. &. (2006). Advanced Concept in Operating Systems. McGraw Hill.
REFERENCE BOOKS:
1. Coulouris, D. K. (2005). Distributed System: Concepts and Design. Pearson Education.
2. Tel, G. (2012). Distributed Algorithms. Cambridge University Press.
3. Tenanuanbaum, S. (2001). Distributed Systems, PHI.
_______________________________________________________________________________
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 29
COURSE: ELECTIVE I – COMPUTER GRAPHICS
CREDIT - 4
Objectives:
To understand the concepts of output primitives of Computer Graphics.
To learn 2D and 3D graphics Techniques.
Outcomes:
The students will be able to:
Demonstrate the algorithms to implement output primitives of Computer Graphics
Apply and analyse 2D and 3D techniques
Code Course
Teaching
Period /
Week
Credit Duration of
Theory
Exam (in
Hrs.) L Pr./
Tu Int. Ext. Total
MCS204B Computer Graphics 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
students to
computer
graphics
Introduction to Computer Graphics
Elements of Computer Graphics, Graphics display systems
Written Unit
Test – I
(Marks 25)
2
To elaborate on primitive algorithms to generate outputs
Output primitives and its algorithms
Points and Lines, Line Drawing algorithms: DDA line
drawing algorithm, Bresenham’s drawing algorithm,
Circle and Ellipse generating algorithms: Mid-point Circle
algorithm, Mid-point Ellipse algorithm, Parametric Cubic
Curves: Bezier curves. Fill area algorithms: Scan line
polygon fill algorithm, Inside-Outside Tests, Boundary fill
algorithms, Flood fill algorithms
3
To introduce
students to
various
transformation
and clipping
2D Geometric Transformations & Clipping
Basic transformations, Matrix representation and
Homogeneous Coordinates, Composite transformation,
shear & reflection. Transformation between coordinated
systems, Window to Viewport coordinate transformation,
Clipping operations – Point clipping Line clipping: Cohen
– Sutherland line clipping, Midpoint subdivision, Polygon
Clipping: Sutherland – Hodgeman polygon clipping
,Weiler – Atherton polygon clipping
Online Class
test will be
conducted.
(Marks 15)
4
To elaborate
on basic 3D
and fractal
concepts
Basic 3D concepts and Fractals
3D object representation methods: B-REP, sweep
representations, CSG, Basic transformations, Reflection,
shear, Projections – Parallel and Perspective Halft one and
Dithering technique. Fractals and self-similarity: Koch
Curves/snowflake, Sirpenski Triangle
Assignments
will be given
for the above
topics.
(Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 30
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) David F. Rogers, J. A. (1990). Mathematical elements for computer graphics. McGraw-Hill.
REFERENCE BOOKS:
1) Donald Hearn, M. P. (2002). Computer Graphics C Version. Pearson Education.
2) Rafael C. Gonzalez, R. E. (2011). Digital Image Processing (3rd ed.). Pearson Education.
_______________________________________________________________________________
COURSE: ELECTIVE I – ADVANCED PYTHON
CREDIT - 4
Objectives:
To introduce students to use of Python programming to solve data analytics problems
To elaborate students to statistical analysis using Python programming
Outcomes:
The students will be able to improve Problem solving and programming capability
The students will be able to perform data analytics using appropriate data mining methods
Code Course
Teaching
Period /
Week
Credit Duration of
Theory
Exam (in
Hrs.) L Pr./
Tu Int. Ext. Total
MCS204C Advanced Python 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
use of python
for data
analytics
Introduction to Data Analytics
Why Analytics, Traditional Data Management, Analytical
tools, Types of Analytics, Hind sight, ore sight and
insight, Dimensions and measures, why learn Python for
data analysis, Using the IPython notebook
Written Unit
Test – I
(Marks 25)
2
To describe various libraries required for data analytics
Libraries for data analytics
Anaconda, Numpy, Scipy, Pandas, Matplotlib, Seaborn,
Scikit-learn, Jupyter Notebook: Create Documentation,
Code mode, Markdown mode
Assignments
will be given
for the above
topics.
(Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 31
3
To elaborate
statistical
analysis using
Python
Statistics using python
Mean, Median, Mode, Z-scores, Bias -variance
dichotomy, Sampling and t-tests, Sample vs Population
statistics, Random Variables, Probability distribution
function, Expected value, Binomial Distributions, Normal
Distributions, Central limit Theorem, Hypothesis testing,
Z-Stats vs T-stats, Type 1 type 2 error, Chi Square test
ANOVA test and F-stats
Assignments
will be given
for the above
topics.
(Marks 5)
4
To study
special
libraries in
Python
Study of Numpy, Scipy, Matplotlib
NUMPY: Creating NumPy arrays, Indexing and slicing in
NumPy, Downloading and parsing data, creating
multidimensional arrays, NumPy Data types, Array
tributes, Indexing and Slicing, creating array, views
copies, Manipulating array shapes I/O,
SCIPY: Introduction to SciPy, Create function, modules of
SciPy
MATPLOTLIB: Scatter plot, Bar charts, histogram, Stack
charts, Legend title Style, Figures and subplots, plotting
function in pandas, Labelling and arranging figures, Save
plots
Online Class
test will be
conducted.
(Marks 10)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOK:
1) Brown, M. C. (n.d.), (2018), Complete Reference: Python. McGraw Hill.
2) Chun, W. J. (2018). Core Python Programming. Prentice Hall.
REFERENCE BOOKS:
1) Allen Downey, J. E. (2017). How To Think Like A Computer Scientist: Learning With Python.
DreamTech.
2) Mark Lutz, D. A. (2016). Learning Python. O’Reilly.
_______________________________________________________________________________
COURSE: ELECTIVE I – NATURAL LANGUAGE PROCESSING
CREDIT - 4
Objectives:
This course introduces the fundamental concepts and techniques of natural language
processing (NLP).
Students will gain an in-depth understanding of the computational properties of natural
languages and the commonly used algorithms for processing linguistic information.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 32
Outcomes:
The students will be able to:
Understand key concepts from NLP those are used to describe and analyze language
Understand POS tagging and context free grammar for English language
Understand semantics and pragmatics of English language for processing
Code Course
Teaching
Period /
Week
Credit Duration of
Theory
Exam (in
Hrs.) L Pr./
Tu Int. Ext. Total
MCS204D Natural Language Processing 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
students to text
representation
in computers
Introduction
Human languages, models, ambiguity, processing
paradigms; Phases in natural language processing,
applications., Text representation in computers, encoding
schemes., Linguistics resources- Introduction to corpus,
elements in balanced corpus, TreeBank, PropBank,
WordNet, VerbNet etc. Resource management with XML,
Management of linguistic data with the help of GATE,
NLTK.
Written Unit
Test – I
(Marks 25)
2 To elaborate on finite state automata
Language Grammar
Regular expressions, Finite State Automata, word
recognition, lexicon, Morphology, acquisition models,
Finite State Transducer, N-grams, smoothing, entropy,
HMM, ME, SVM, CRF. Part of Speech tagging-
Stochastic POS tagging, HMM, Transformation based
tagging (TBL), Handling of unknown words, named
entities, multi word expressions. A survey on natural
language grammars, lexeme, phonemes, phrases and
idioms, word order, agreement, tense, aspect and mood
and agreement, Context Free Grammar, spoken language
syntax.
Assignments
will be given
for the above
topics.
(Marks 10)
3
To introduce
students on
parsing
Parsing
Unification, probabilistic parsing, TreeBank. Semantics-
Meaning representation, semantic analysis, lexical
semantics, WordNet Word Sense Disambiguation-
Selectional restriction, machine learning approaches,
dictionary-based approaches. Discourse- Reference
resolution, constraints on co-reference, algorithm for
pronoun resolution, text coherence, discourse structure
Assignments
will be given
for the above
topics.
(Marks 5)
4
To
demonstrate
uses of NLP
Applications of NLP
Spell-checking, Summarization Information Retrieval-
Vector space model, term weighting, homonymy,
polysemy, synonymy, improving user queries. Machine
Translation– Overview.
Online Class
test will be
conducted.
(Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 33
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXTBOOKS:
1) Daniel Jurafsky and James H Martin. (2009), Speech and Language Processing, 2e,
Pearson Education
REFERENCE BOOKS:
1) James A. (1994), Natural language Understanding 2e, Pearson Education
2) Bharati A., Sangal R., Chaitanya V.. (2000), Natural language processing: a Paninian
perspective, PHI
3) Siddiqui T., Tiwary U. S.. (2008), Natural language processing and Information
retrieval, OUP
COURSE: DATA ANALYTICS AND MINING LAB
CREDIT: 4
Objectives:
To acquire the knowledge of various concepts and tools behind data mining for business intelligence
To Study data mining algorithms, methods and tools
To Identify business applications of data mining
Outcomes:
The students will be able to:
Apply data mining concepts for analysis of data
Develop industry level data mining skills using software tools
Make use of relevant theories, concepts and techniques to solve real-world business
problems
Code Course
Teaching
Period /
Week
Credit Duration
of Theory
Exam (in
Hrs.) L Pr./
Tu Int. Ext. Total
MCSL205 Data Analytics and Mining Lab
- 2 1 1 2 1
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 34
Module
No
Objective Content Evaluation
1
To elaborate the
concept of data
preprocessing
Data Preprocessing
Data cleaning, data transformation, Data
reduction, Discretization and generating
concept hierarchies, Installing Weka 3 Data
Mining System, experiments with Weka -
filters, discretization
Students will be
evaluated using Lab
Manual.
(Marks 05)
3
To implement
classification and
prediction
Data Mining (Supervised Learning) Using
Weka/R Miner
Classification
Prediction
Practical Exam will
be conducted.
(Marks 15)
4
To implement
clustering and
association rule
mining
Data Mining (Unsupervised Learning) using
Weka/R Miner
Clustering
Association Rule Mining
2
To gain detailed
insights of outlier
detection
Outlier Detection
Detection of anomalies, such as the statistical,
proximity-based, clustering-based, and
classification-based methods.
Class Test
(Marks 05)
Softwares used: Advanced Excel, XLMiner,Weka, IBM SPSS Statistics
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOKS:
1. S. C. Gupta, V. K. Kapoor, (2014), Fundamental of Mathematical Statistics
2. Efraim Turban, Ramesh Sharda, Dursun Delen, David King, (2013), Business Intelligence (2nd
Edition), Pearson
REFERENCE BOOKS:
1. Swain Scheps, (2008), Business Intelligence for Dummies, Wiley Publications
2. Inmon, (1993), Building the Data Warehouse, Wiley
3. Dunham, Margaret H, (2006), Data Mining: Introductory and Advanced Topics, Prentice Hall
4. Witten, Ian and Eibe Frank, (2011), Data Mining: Practical Machine Learning Tools and
Techniques, Second Edition, Morgan Kaufmann
5. MacLennan Jamie, Tang ZhaoHui and Crivat Bogdan, (2009), Data Mining with Microsoft
SQL Server 2008, Wiley India Edition
_______________________________________________________________________________
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 35
COURSE: STATISTICS LAB
CREDIT: 2 Objectives:
To equip the students with a working knowledge of probability, statistics, and modelling in the presence of uncertainties.
To understand the concept of hypothesis and significance tests
To help the students to develop an intuition and an interest for random phenomena and to introduce both theoretical issues and applications that may be useful in real life.
Outcomes: The students will be able to:
Distinguish between quantitative and categorical data
Apply different statistical measures on data
Identify, formulate and solve problems
Code Course
Teaching
Period /
Week
Credit Duration
of Theory
Exam (in
Hrs.) L Pr./
Tu Int. Ext. Total
MCSL206 Statistics Lab
- 2 1 1 2 1
Module
No.
Objective Content Evaluation
1 To elaborate software
for data analysis
Introduction to the software used for
data analysis
Environment, entering data and
formatting, handling data files, performing
calculations, handling utilities, formulae
and functions
Lab manual for 05
marks
2
To demonstrate
visualization of data
Visualizing
Handling different types of data variables,
creating tables, frequency distribution
tables and presenting the data in the forms
of Charts, Diagrams, graphs, polygons and
plots
Online test of 10
marks
3
To implement the
methods to find
Measures of Central
Tendency, dispersion,
Skewness
Data Descriptors and Hypothesis
Testing
Measure of Central Tendencies,
Dispersions, skewness, Hypothesis testing
and estimation, Goodness of Fit
Practical test of 10
marks
4
To perform
Correlation and
regression to analyse
data
Correlation and Regression Using SPSS Statistics find correlation and
regression in sample data
Note: Softwares used: Advanced Excel, XLMiner, IBM SPSS Statistics
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 36
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOK:
1. S. C. Gupta, V. K. Kapoor, (2014), Fundamental of Mathematical Statistics
REFERENCE BOOKS:
1. Efraim Turban, Ramesh Sharda, Dursun Delen, David King, (2013), Business Intelligence (2nd
Edition), Pearson
2. Swain Scheps, (2008), Business Intelligence for Dummies, Wiley Publications
_______________________________________________________________________________
COURSE: ADVANCED JAVA LAB
CREDIT: 2
Objectives:
To prepare students to excel and succeed in industry / technical profession through global, rigorous education.
Excellence through application development.
To provide students with a solid foundation on Tools, Technology and Framework Outcomes:
Students will demonstrate a high degree of proficiency in programming enabling them for careers in software engineering with competencies to design, develop, implement and integrate software applications and computer systems.
Students will develop confidence for self-education and ability for life-long learning
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam (in
Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL207 Advanced Java Lab - 2 1 1 2 1
Module
No.
Objective Content Evaluation
1 To implement database connectivity in Java Application
JDBC All data base operation using Access /oracle/MySQL as backend
Lab manual for 05 marks
2
To demonstrate the use of Servlets
Servlets A Simple Servlet Generating Plain text/ HTML, program based on cross page posting and post back posting (client request and server response)
Online test of 15 marks
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 37
3
To demonstrate the use of
Java Server Pages
JSP
Sample program to demonstrate
JSP syntax and semantics, program
based on directive and error object,
program based on cookies and
Sessions
Practical test of 15
marks
4
To implement MVC
architecture
Introduction to Framework:
Struts
Basic Configuration for struts,
Program based on Action validation
and control in struts, Program based
on integration of JSP and Servlets
with struts
Practical test of 15
marks
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) Herbert schildt(2017), The complete reference JAVA2, 5th Ed., Tata McGraw Hill
2) Sharanam Shah and Vaishali Shah(2010), Core Java for beginners, Shroff Publishers and
Distributors
REFERENCE BOOKS:
1) Sharanam Shah and vaishali shah(2014), Struts 2 for beginners, SPD
2) Dreamtech(2007), Advance Java-Savalia,Core, Java 6 Programming Black Book, Wiley
3) Marty Hall and Larry Brown(2003), Core Servlets and Java Server Pages: Vol I: Core
Technologies 2/e , Pearson
4) Sharnam Shah and Vaishali Shah(2011), Java EE 6 for Server Programming for
professionals, SPD
_______________________________________________________________________________
COURSE: ADVANCED PYTHON LAB
CREDIT - 2
Objectives:
To introduce students to use of Python programming to solve data analytics problems
To elaborate students to statistical analysis using Python programming
Outcomes:
The students will be able to improve Problem solving and programming capability
The students will learn data analytics through python programming
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 38
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam (in
Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL208 Advanced Python Lab - 2 1 1 2 1
Module
No.
Objective Content Evaluation
1
To describe various libraries required for data analytics
Operations using Libraries for data analytics Anaconda, Numpy, Scipy, Pandas, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook: Create Documentation, Code mode, Markdown mode
Lab manual for 05 marks
2
To elaborate statistical analysis using Python
Practical on Statistics using python Mean, Median, Mode, Z-scores, Bias -variance dichotomy, Sampling and t-tests, Sample vs Population statistics, Random Variables, Probability distribution function, Expected value, Binomial Distributions, Normal Distributions, Central limit Theorem, Hypothesis testing, Z-Stats vs T-stats, Type 1 type 2 error, Chi Square test ANOVA test and F-stats
Practical test of 5 marks
3
To study special libraries in Python such as Numpy and Scipy
Practical on Numpy, Scipy NUMPY: Creating NumPy arrays, Indexing and slicing in NumPy, Downloading and parsing data, creating multidimensional arrays, NumPy Data types, Array tributes, Indexing and Slicing, creating array, views copies, Manipulating array shapes I/O, SCIPY: Introduction to SciPy, Create function, modules of SciPy
Practical test of 10 marks
4
To study special libraries in Python such as Numpy and Scipy
Practical on Matplotlib MATPLOTLIB: Scatter plot, Bar charts, histogram, Stack charts, Legend title Style, Figures and subplots, plotting function in pandas, Labelling and arranging figures, Save plots
Online Class test of 5 marks
EVALUATION:
1) On Four Modules of 25 marks 2) Final examination of 25 marks 3) Total marks = Internal 25 + External 25 = 50
TEXT BOOK:
1) Martin C. Brown, (2018), Complete Reference: Python., McGraw Hill REFERENCE BOOKS:
1) Allen Downey, Jeff Elkner and Chris Meyers, (2017), How To Think Like A Computer Scientist: Learning With Python,DreamTech
2) Wesley J Chun, (2018), Core Python Programming, Prentice Hall
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 39
M.Sc. (COMPUTER SCIENCE) SEMESTER - III (SECOND YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int. Ext. Total
MCS301 Big Data Analytics and Machine
Learning 4 - 2 2 4 2
MCS302 Artificial Intelligence 4 - 2 2 4 2
MCS303 Mobile Application Development 4 - 2 2 4 2
MCS304 Information and Cyber Security 4 - 2 2 4 2
MCSL305 Big Data Analytics Lab -
2 1 1 2 1
MCSL306 Machine Learning Lab - 2 1 1 2 1
MCSL307 Mobile Application Development
Lab - 2 1 1 2 1
MCSL308 Ethical Hacking Lab - 2 1 1 2 1
Total 16 8 24 -
SEMESTER-III
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
COURSE: BIG DATA ANALYTICS AND MACHINE LEARNING
CREDIT - 04
Objectives:
To study the basic of Hadoop
To study the basic of Map-Reduce
To study the basic of NoSQL, Hive, Pig,
To study the basic of Machine Learning
Outcomes:
The course will help:
To understand and learn Hadoop, Map-Reduce, NoSQL
To understand and learn Hive, Pig, Machine Learning
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 40
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS301 Big Data Analytics and Machine
Learning 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
student to the
concept of big
Data,
Statistical and
Soft
Computing
Analysis of
Big Data.
Introduction to Big Data Big data: Introduction to Big data Platform, Traits of big
data, Challenges of conventional systems, Web data,
Analytic processes and tools, Analysis vs Reporting,
Modern data analytic tools, Statistical concepts:
Sampling distributions, Re-sampling, Statistical
Inference, Prediction error. Data Analysis: Regression
modeling, Analysis of time Series: Linear systems
analysis, Nonlinear dynamics, Rule induction, Neural
networks: Learning and Generalization, Competitive
Learning, Principal Component Analysis and Neural
Networks, Fuzzy Logic: Extracting Fuzzy Models from
Data, Fuzzy Decision Trees, Stochastic Search Methods.
Unit Test-1
(Marks-25)
2
To introduce
students with
Map-Reduce
based
computing
environment
used for Big
Data Analysis
MAP REDUCE
Introduction to Map Reduce: The map tasks, grouping
by key, the reduce tasks, Combiners, Details of
MapReduce Execution, Coping with node failures.
Algorithms Using MapReduce: Matrix-Vector
Multiplication, Computing Selections and Projections,
Union, Intersection, and Difference, Natural Join.
Extensions to MapReduce: Workflow Systems,
Recursive extensions to MapReduce, Common map
reduce algorithms.
Oral
Presentation
(Marks 10)
3
To
demonstrate
standard linear
methods used
in Machine
Learning
Machine Learning- Standard Linear methods
Statistical Learning, Assessing Model Accuracy. Linear
Regression: Simple Linear Regression, Multiple Linear
Regressions, Other Considerations in the Regression
Model, The Marketing Plan, Comparison of Linear
Regression with K-Nearest Neighbors. Classification:
An Overview of Classification, Why Not Linear
Regression, Logistic Regression, Linear Discriminant
Analysis, A Comparison of Classification Methods.
Class Test
(Marks 10)
4
To
demonstrate
standard non-
linear methods
used in
Machine
Learning
Machine Learning- Non-Linear Learning methods
Polynomial Regression, Step Functions, Basis
Functions, Regression Splines, Smoothing Splines,
Local Regression, Generalized Additive Models, Tree-
Based Methods: The Basics of Decision Trees. Bagging,
Random Forests, Boosting., Support Vector machines,
Principle Component Analysis and Clustering
Assignment
(Marks 05)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 41
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1. Anand Rajaraman and Jeffrey David Ullman (2012), Mining of Massive Datasets, Cambridge
University Press.
2. Michael Minelli, (2013), Big Data, Big Analytics: Emerging Business Intelligence and Analytic
Trends for Today's Businesses, Wiley
REFERENCE BOOKS:
1. J. Hurwitz, et al., (2013), Big Data for Dummies, Wiley
2. Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, Thomas Deutsch, George Lapis, (2012),
Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data,
McGraw-Hill
3. James Manyika , Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles
Roxburgh, Angela Hung Byers, (2011), Big data: The next frontier for innovation, competition,
and productivity, McKinsey Global Institute
4. Pete Warden, (2011), Big Data Glossary, O’Reilly
5. David Loshin, (2013), Big Data Analytics: From Strategic Planning to Enterprise Integration
with Tools, Techniques, NoSQL, and Graph, Morgan Kaufmann Publishers
6. Kevin P Murphy, (2012), Machine Learning: A Probabilistic Perspective: The MIT Press
Cambridge
7. Ethem Alpaydın, (2015), Introduction to Machine Learning (Third Edition): The MIT Press
8. Christopher M. Bishop, (2006) Pattern Recognition and Machine Learning: Springer
9. Peter Harrington, (2012), Machine Learning in Action: Manning Publications
10. Brett Lantz, (2013), Machine Learning with R: Packt Publishing
_____________________________________________________________________________
COURSE: ARTIFICIAL INTELLIGENCE
CREDIT - 4
Objectives:
● To Understand various Artificial Intelligence concepts ● To enable the students to identify and describe problems that are open to be solved by AI
methods
Outcomes:
The students will be able to:
Understand various problems which will be solvable by using Artificial Intelligence
concepts
Learn to write programs using Artificial Intelligence programming languages (LISP and
PROLOG)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 42
Code Course
Teaching Period
/ Week Credit Duration of
Theory Exam (in
Hrs.) L Pr./
Tu Int. Ext. Total
MCS302
Artificial Intelligence 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1 To learn the
concepts of AI
Introduction to Artificial Intelligence Introduction: Concepts & definitions of AI, Brief
history of AI, State space search: Generate and test,
Simple search, Depth First Search (DFS), Breadth First
Search (DFS), Comparison and quality of solutions. Best
First Search (BFS), Hill Climbing, A* algorithm.
Written Unit
Test – I
(Marks 25)
2
To study
propositional logic
and first order
predicate logic and
use the technique
to solve logical
reasoning
problems.
To develop and use
fuzzy arithmetic
tools in solving
problems
Knowledge Representation
Propositional and Predicate Logic: Syntax and
semantics for prepositional logic (PL) and first order
propositional logic (FOPL), Properties of well-formed
formula (wff), Inference rules. First Order Predicate
Logic: Syntax of Predicate Logic, Prenex Normal
Form (PNF), (Skolem) Standard Form, Applications of
FOPL. Deductive Inference Rules and Methods: Basic
Inference Rules and Application in PL, Basic Inference
Rules and Application in FOPL, Resolution Method in
PL and FOPL. Fuzzy Logic: Fuzzy Sets, Fuzzy
Operators & Arithmetic, Membership Functions, Fuzzy
Relations.
Assignments
will be given
for the above
topics.
(Marks 5)
3
T To learn to write
programs using the
syntax of AI
programming
languages (LISP
and PROLOG)
AI Programming Languages & Applications of AI
AI Programming Languages: Introduction to LISP,
Syntax and Numeric Functions, Basic List Manipulation
Functions in LISP Functions, Predicates and
Conditionals, Input, Output, and Local Variables,
Iteration and Recursion, Property Lists and Arrays,
PROLOG: List, Operators, Arithmetic, Cut and Fail
operator, Backtracking.
Assignments
will be given
for the above
topics. (Marks
5)
4
To make a detailed
study of Expert
System
Expert Systems: Introduction and Concept of Planning,
Representing and Using Domain Knowledge Expert
System Shells, Knowledge Acquisition. Intelligent
Agents: Agents and environments, Rationality and other
performance measures, Nature of environments, Structure
of agents.
Online Class
test will be
conducted.
(Marks 15)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 43
TEXT BOOK:
1) Deepak Khemani, (2013), A First course in Artificial Intelligence, Tata McGraw Hill
Education (India) private limited
2) Ben Coppin, Jones, (2004), Artificial Intelligence Illuminated, Bartlett Publishers Inc.
REFERENCE BOOKS:
1) Stuart Jonathan Russell, Peter Norvig, (2010), Artificial Intelligence: A Modern
Approach, 3e, Prentice Hall Publications.
2) M Tim Jones (2008), Artificial Intelligence A Systems Approach, Firewall media, New
Delhi
3) George Lugar, (2002), Artificial Intelligence -Structures and Strategies for Complex
Problem Solving., 4/e, Pearson Education
_____________________________________________________________________________
COURSE: MOBILE APPLICATION DEVELOPMENT CREDIT - 04 Objectives:
To Understand the entire Android Apps Development Cycle
To Apply the advanced android development techniques
To Conceptualize the design of user applications using User Experience Design.
Outcomes: The students will be able to:
Demonstrate Android activities life cycle
Apply proficiency in coding on a mobile programming platform.
Design and develop innovative android applications
Create real life application with end-to-end understanding of User experience practices
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS303 Mobile Application Development 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1 To identify android
platform features
Introduction to Android
The android platform, the layers of android, Four
kinds of android components, understanding the
androidManifest.xml file, creating an android
application
Unit Test-1
(Marks-25)
2
To introduce UI
and data operations
User Interface, Storing and Retrieving data
Creating the activity, working with views, using
resources Working with intents and services, Using
the file system, working with shared preferences,
Oral
Presentation
(Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 44
3
To integrate
android platform
with API
Location Sensors and REST API Integration
Using Location Manager and Location Provider,
working with maps, Working with GPS, Bluetooth
and WiFi, Integrating google maps, services for
push notificationGoogleads, UsingAsyncTask to
perform network operations, introduction to
HtttpUrlConnection and JSON, performing
network operations asynchronously, working with
OkHttp, Retrofit and Volley
Class Test
(Marks 10)
4
To learn database
connectivity in
android application
Database connectivity and distributing android
application
SQLite Programming, Android database
connectivity using SQLite, distribution options,
packaging and testing the application, distributing
applications on google play store
Assignment
(Marks 05)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) W. Frank Ableson, Robi Sen, Chris King, C. Enrique Ortiz(2011), Android in action,
Third Edition, Dreamtech Press.
REFERENCE BOOKS
1) Wei-Meng Lee (2012), Beginning Android 4 Application Development, Wrox
Publications
2) Helllo, (2015), Android Introducing Google’s Mobile Development Platform, Fourth Ed,
Burnette, SPD Publications.
_______________________________________________________________________________
COURSE: INFORMATION AND CYBER SECURITY
CREDIT - 04
Objectives:
To develop an understanding of information security as practiced in computer operating
systems, distributed systems, networks and representative applications.
To gain familiarity with prevalent network and distributed system attacks, and defences
against them.
To develop a basic understanding of cryptography, how it has evolved, and some key
encryption techniques used today.
To develop an understanding of security policies (such as authentication, integrity and
confidentiality), as well as protocols to implement such policies in the form of message
exchanges.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 45
Outcomes:
The students will be able to gain:
Knowledge about the technical and legal terms relating to the cybersecurity, cyber offences
and crimes.
Gain an insight to the Indian Act 2000 and the organizational implications of cyber
Security
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS304 Information and Cyber Security 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
student to
different types
of computer
security attack
and ethical
hacking
Computer Security Principles of Security, Different Attacks: malicious and
non-malicious program, Types of Computer Criminals.
Operating System Security: Protected objects and
methods of protection. Memory address protection:
Fence, Relocation, Base/Bound Registers, Tagged
Architecture, Segmentation, Paging, Directory, access
control list. Database Security: Security requirements,
Integrity, Confidentiality, Availability, Reliability of
Database, Sensitive data, Multilevel database, Proposals
for multilevel security. Introduction to Ethical Hacking
Students
will be
evaluated
by taking
viva.
(Marks 05)
2
To elaborate
the concept of
Authentication
, Internet
Security,
network
security and
Kerberos
Network Security Different types of network layer attacks, Firewall (ACL,
Packet Filtering, DMZ, Alerts and Audit Trials) – IDS,
IPS and its types (Signature based, Anomaly based,
Policy based, Honeypot based). Web Server Security:
SSL/TLS Basic Protocol-computing the keys- client
authentication-PKI as deployed by SSL Attacks fixed in
v3- Exportability-Encoding-Secure Electronic
Transaction (SET), Kerberos, Secret Key Cryptography,
public key cryptography, Hash function and message
digest
Written
Unit Test –
I
(Marks 25)
3
To elaborate
cloud data
security
Cloud Security
How concepts of Security apply in the cloud, User
authentication in the cloud; How the cloud provider can
provide this- Virtualization System Security Issues: e.g.
ESX and ESXi Security, ESX file system security-
storage considerations, backup and recovery-
Virtualization System Vulnerabilities, security
management standards- SaaS, PaaS, IaaS availability
management- access control- Data security and storage
in cloud.
Written
Class Test
will be
conducted.
(Marks 10)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 46
4
To
demonstrate
wireless
communicatio
n security
Mobile Security
Mobile system architectures, Overview of mobile
cellular systems, GSM and UMTS Security & Attacks,
Vulnerabilities in Cellular Services, Cellular Jamming
Attacks & Mitigation, Security in Cellular VoIP
Services, Mobile application security. Securing Wireless
Networks: Overview of Wireless Networks, Scanning
and Enumerating 802.11 Networks, Attacking 802.11
Networks, Bluetooth Scanning and Reconnaissance,
Bluetooth Eavesdropping, Attacking & Exploiting
Bluetooth, Zigbee Security & Attacks.
Assignmen
ts will be
given for
the above
topics.
(Marks 10)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) Charles P. Pfleeger, Charles P. Pfleeger, Shari Lawrence Pfleeger, (2006), Security in
Computing 4th edition, Prentice Hall; 4th edition
2) Kia Makki, Peter Reiher, (2007), Mobile and Wireless Security and Privacy, Springer
REFERENCE BOOKS:
1) Tim Mather, Subra Kumaraswamy, Shahed Latif., (2009), Cloud Security and Privacy:
An Enterprise Perspective on Risks and Compliance (Theory and practice), O'Reilly
Media; 1 edition
2) Ronald L. Krutz, Russell Dean Vines, (2010), Cloud Security: A Comprehensive Guide to
Secure Cloud Computing, Wiley
3) Charlie Kaufman, Radia Perlam, Mike Speciner, (2010), Network Security, Prentice Hall,
2nd Edition
4) Atul Kahate, (2013), Cryptography and Network Security 3rd edition, Tata McGraw Hill
Education Private Limited
5) William Stallings, (2013), Cryptography and Network Security: Principles and practice
6th edition, Pearson Education
_______________________________________________________________________________
COURSE: BIG DATA ANALYTICS LAB
CREDIT: 2
Objectives:
To enable the students to gain practical knowledge about Hadoop, Map-Reduce
To enable the students to gain practical knowledge about NoSQL, Hive, Pig
Outcomes:
The students will be able to:
Understand various problem-solving methods using Big Data Analytics techniques
Learn the map-reduce based programming techniques
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 47
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL305 Big Data Analytics Lab
- 2 1 1 2 1
Module
No
Objective Content Evaluation
1
To demonstrate use of
Map-Reduce based
framework to analyse
letters in large text
Occurrences of Letter
Implement Hadoop system, Map-reduce program to
count the number of occurrences of each alphabetic
character in the given dataset. The count for each
letter should be case-insensitive (i.e., include both
upper-case and lower-case versions of the letter;
Ignore non-alphabetic characters).
Students
will be
evaluated
using Lab
Manual.
(Marks 5)
2
To demonstrate use of
Map-Reduce based
framework to analyse
words in large text
Occurrences of Words
Map-reduce program to count the number of
occurrences of each word in the given dataset. (A word
is defined as any string of alphabetic characters
appearing between non-alphabetic characters like
nature's is two words. The count should be case-
insensitive. If a word occurs multiple times in a line,
all should be counted)
Class Test
(Marks 10)
3 To implement Pig
system
Implementation of Pig System
Pig installation, Load Data in Pig from Local
Environment and Query the Data
Practical
Exam will
be
conducted.
(Marks 10) 4
To implement Hive
System Implementation of Hive System
Hive queries, Hive Storage and HDFS
The experiments may be done using software/tools like Hadoop / WEKA / R / Java etc.
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 markss
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOKS:
1. Anand Rajaraman and Jeffrey David Ullman, (2016) Mining of Massive Datasets, Cambridge
University Press.
2. Michael Minelli, (2013), Big Data, Big Analytics: Emerging Business Intelligence and Analytic
Trends for Today's Businesses, Wiley
REFERENCE BOOKS:
1. J. Hurwitz, et al., (2013), Big Data for Dummies, Wiley
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 48
2. Paul C. Zikopoulos, Chris Eaton, Dirk deRoos, Thomas Deutsch, George Lapis, (2012),
Understanding Big Data Analytics for Enterprise Class Hadoop and Streaming Data,
McGraw-Hill
3. James Manyika , Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles
Roxburgh, Angela Hung Byers, (2011), Big data: The next frontier for innovation, competition,
and productivity, McKinsey Global Institute
4. Pete Warden, (2011), Big Data Glossary, O’Reilly
5. David Loshin, (2013), Big Data Analytics: From Strategic Planning to Enterprise Integration
with Tools, Techniques, NoSQL, and Graph, Morgan Kaufmann Publishers
6. Kevin P Murphy, (2012), Machine Learning: A Probabilistic Perspective: The MIT Press
Cambridge
7. Ethem Alpaydın, (2015), Introduction to Machine Learning (Third Edition): The MIT Press
8. Christopher M. Bishop, (2006) Pattern Recognition and Machine Learning: Springer
9. Peter Harrington, (2012), Machine Learning in Action: Manning Publications
10. Brett Lantz,(2013), Machine Learning with R: Packt Publishing
_______________________________________________________________________________
COURSE: MACHINE LEARNING LAB
CREDIT: 2
Objectives:
To enable the students to gain practical knowledge about algorithms of linear methods in
Machine Learning
To enable the students to gain practical knowledge about algorithms of non-linear methods
in Machine Learning
Outcomes:
The students will be able to:
Understand various problem-solving methods machine learning techniques
Learn in depth linear and non-linear methods of machine learning
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL306 Machine Learning Lab -
2 1 1 2 1
Module
No
Objective Content Evaluation
1
To demonstrate standard
linear methods
(regression) used in
Machine Learning
Standard Linear methods - Regression
Practical sessions on Statistical Learning,
Assessing Model Accuracy. Linear Regression:
Simple Linear Regression, Multiple Linear
Regressions, Other Considerations in the
Regression Model, The Marketing Plan,
Comparison of Linear Regression with K-Nearest
Neighbors.
Students
will be
evaluated
using Lab
Manual.
(Marks 5)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 49
2
To demonstrate standard
linear methods
(classification) used in
Machine Learning
Standard Linear methods - Classification
Practical Sessions on Classification: Logistic
Regression, Linear Discriminant Analysis, A
Comparison of Classification Methods
performance.
Class Test
(Marks 10)
3
To demonstrate standard
non-linear tree-based
methods used in
Machine Learning
Non-Linear Learning methods - Tree-Based
Methods
Practical sessions on Polynomial Regression, Step
Functions, Basis Functions, Regression Splines,
Smoothing Splines, Local Regression, Generalized
Additive Models, Tree-Based Methods: The
Basics of Decision Trees. Bagging, Random
Forests, Boosting
Practical
Exam will
be
conducted.
(Marks 10)
4
To demonstrate standard
non-linear SVM, PCA
methods used in
Machine Learning
Non-Linear Learning methods - SVM
Practical sessions on Support Vector machines,
Principle Component Analysis and Clustering
The experiments may be done using software/tools like Hadoop / WEKA / R / Java etc.
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOKS:
1. David Loshin, (2013), Big Data Analytics: From Strategic Planning to Enterprise Integration
with Tools, Techniques, NoSQL, and Graph, Morgan Kaufmann Publishers
2. Kevin P Murphy, (2012), Machine Learning: A Probabilistic Perspective: The MIT Press
Cambridge
REFERENCE BOOKS:
1. Pete Warden, (2011), Big Data Glossary, O’Reilly
2. Ethem Alpaydın, (2015), Introduction to Machine Learning (Third Edition): The MIT Press
3. Christopher M. Bishop, (2006) Pattern Recognition and Machine Learning: Springer
4. Peter Harrington, (2012), Machine Learning in Action: Manning Publications
5. Brett Lantz,(2013), Machine Learning with R: Packt Publishing
COURSE: MOBILE APPLICATION DEVELOPMENT LAB
CREDIT: 2 Objectives:
To Understand the entire Android Apps Development Cycle
To Apply the advanced android development techniques
To Conceptualize the design of user applications using User Experience Design.
Outcomes: The students will be able to:
Demonstrate Android activities life cycle
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 50
Apply proficiency in coding on a mobile programming platform.
Design and develop innovative android applications
Create real life application with end-to-end understanding of User experience practices
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL307 Mobile Application Development
Lab -
2 1 1 2 1
Module
No
Objective Content Evaluation
1
To demonstrate the basic components and event handling of an Android application.
Android Platform Introduction to the Android platform and the Android Studio IDE, Android components, Activities, User Interface Design, Intents, Activity lifecycle, UI Design: Widgets and Layouts, UI Events, Event Listeners
Students
will be
evaluated
using Lab
Manual.
(Marks 5)
2
To describe the basics of graphics and multimedia support in Android.
To demonstrate basic skills of using an Android SDK for implementing Android applications.
Graphics Support in Android Drawables, Basics of Material Design, 2D graphics: Canvas/Drawing using a view, multimedia in Android: Audio playback and MediaPlayer, SoundPool
Class Test
(Marks 10)
3
To demonstrate skills of using networking concepts in Android
Networking support Basics of networking in Android, AsyncTask, HttpURL Connection
Practical
Exam will
be
conducted.
(Marks 10)
4
To demonstrate use of database connectivity in Android
Database connectivity and distributing and android application SQLite Programming, Android database connectivity using SQLite, distribution options, packaging and testing the application, distributing applications on google play store
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOKS:
1) W. Frank Ableson, Robi Sen, Chris King, C. Enrique Ortiz(2011), Android in action,
Third Edition, Dreamtech Press.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 51
REFERENCE BOOKS:
1) Wei-Meng Lee(2012), Beginning Android 4 Application Development, Wrox
Publications
2) Helllo, Android Introducing Google’s Mobile Development Platform, Fourth Edition, Ed
Burnette(2015), SPD Publications
_____________________________________________________________________________
COURSE: ETHICAL HACKING LAB
CREDIT: 2
Objectives:
To acquire hands-on working skill set which includes Vulnerability Assessment, Network
Infrastructure, Network Securities, Network Exploitation, Red Hat Linux Security.
Outcomes:
The students will be able to:
Demonstrate ethical hacking techniques
Learn security of sensitive data and websites
Code Course
Teaching
Period /
Week
Credit Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCSL308 Ethical Hacking Lab -
2 1 1 2 1
Module
No
Objective Content Evaluation
1
To learn
Footprinting
concept
Introduction
Introduction to Ethical Hacking, Foot printing,
Surveying & Gathering Data, Understanding IP
& MAC addresses., concepts of TCP/IP, Basic
networking concepts, Understanding domain
registrations & Webhosting concepts
Students will be
evaluated using Lab
Manual.
(Marks 5)
2
To learn scanning
of network
Scanning Network
Overview of Network Scanning, CEH Scanning
Methodology, Check for Live Systems, ICMP
Scanning, Ping Sweep Tools, Check for Open
Ports, Network scanning, Network Pentesting,
Viruses, worms & Trojans, Ethical hacking
Methods (Key loggers, phishing, RAT)
Class Test
(Marks 10)
3
To learn methods
of password
security
Password Security
Passwords Cracking, Hacking through Social
Engineering, Cryptography, Steganography
Practical Exam will
be conducted.
(Marks 10)
4
To learn the
concept of Denial
of Service attack
Denial of Service attack
SQL Injections, Denial of Service, Cross-site
scripting (XSS), Firewalls configurations &
Bypassing
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 52
EVALUATION:
1) On Four Modules of 25 marks
2) Final examination of 25 marks
3) Total marks = Internal 25 + External 25 = 50
TEXT BOOKS:
1) Shekhar Mishra, (2017), Ethical Hacking for Beginners 2019: Complete step by step
Guide Beginner to Advance, PHI
REFERENCE BOOKS:
1) Patrick Engebretson, (2015), The Basics of Hacking and Penetration Testing: Ethical
Hacking and Penetration Testing Made Easy, Syngress Basics Series
2) James Clark (2017), Geek Collection 7 in 1 Box Set: Computer Hacking Guide for
Beginners, SQL, Google Drive, Project Management, Amazon FBA, LINUX, Excel, TMH
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 53
M.Sc. (COMPUTER SCIENCE) SEMESTER - IV (SECOND YEAR)
Code Subject Title
Teaching
Period /
Week
Credit
Duration
of
Theory
Exam
(in Hrs.)
L Pr./
Tu Int.
Ext.
Total
MCS401 Cloud Computing 4 - 2 2 4 2
MCS402 Elective II 4 - 2 2 4 2
MCSL403 Research Paper Writing - 4 2 2 4 -
MCSL404 Software Project - 12 6 6 12 -
Total 8 16 24 -
SEMESTER-IV
1 Credit=25 Marks
Total Credits = 24
Total Marks = 24*25=600
Elective II
Course Code Course Nomenclature
MCS402A Digital Image Processing
MCS402B Robotics
MCS402C Blockchain Technology
MCS402D Modeling and Simulation
_______________________________________________________________________________
COURSE: CLOUD COMPUTING
CREDIT - 04
Objectives:
To learn the concept of parallel and distributed computing
To enable the students to gain knowledge of cloud-based computing technologies
To learn to deploy cloud-based computing environment
Outcomes:
The students will be able to:
Design and implement software application in a cloud environment.
Manipulate large data sets in a parallel computing environment.
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 54
Code Course
Teaching Period
/ Week Credit
Duration
of
Theory
Exam
(in Hrs.) L
Pr./
Tu Int. Ext. Total
MCS401 Cloud Computing 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To elaborate the
concept of
parallel and
distributed
computing and
virtualization
Parallel, Distributed Computing and
Virtualization
Elements of parallel computing, elements of
distributed computing, Technologies for distributed
computing: RPC, Distributed object frameworks,
Service oriented computing, Virtualization –
Characteristics, taxonomy, virtualization and cloud
computing.
Unit Test-1
(Marks-25)
2
To introduce
students with
cloud computing
services
Computing Platforms and Cloud technologies
Cloud Computing definition and characteristics,
Enterprise Computing, The internet as a platform,
Cloud computing services: SaaS, PaaS, IaaS,
Enterprise architecture, Types of clouds, Cloud
computing platforms, Web services, AJAX, mashups,
multi-tenant software, Concurrent computing: Thread
programming, High-throughput computing: Task
programming, Data intensive computing: Map-
Reduce programming.
Oral
Presentation
(Marks 10)
3
To demonstrate
the use of cloud-
based software
architecture
Software Architecture Dev 2.0 platforms, Enterprise software: ERP, SCM,
CRM, Custom enterprise applications and Dev 2.0,
Cloud applications.
Class Test
(Marks 10)
4
To demonstrate
the use of cloud-
based services
provider
Amazon Web Services (AWS) Essentials Architecting on AWS, building complex solutions
with Amazon Virtual Private Cloud (Amazon VPC),
Leverage bootstrapping and auto configuration in
designs, Architect solutions with multiple regions,
Employ Auto Scaling design patterns, Amazon
CloudFront for caching, Big data services including
AWS Data Pipeline, Amazon Redshift and Amazon
Elastic MapReduce. AWS OpsWorks.
Assignment
(Marks 05)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) Gautam Shroff, (2010), Enterprise Cloud Computing Technology, Architecture,
Applications, Cambridge University Press
2) Mastering In Cloud Computing (2013), Tata Mcgraw-Hill Education
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 55
REFERENCE BOOKS:
1) Rajkumar Buyya, Christian Vecchiola And Thamari Selvi S, (2009), Cloud Computing: A
Practical Approach, Anthony T Velte, Tata Mcgraw Hill
2) Michael J. Kavis, (2014), Architecting the Cloud: Design Decisions for Cloud Computing
Service Models (SaaS, PaaS, and IaaS), Wiley CIO
3) Kris Jamsa, Jones (2013), Cloud Computing: SaaS, PaaS, IaaS, Virtualization, Business
Models, Mobile, Security and More, Bartlett Learning
4) AWS Training, http://aws.amazon.com/training.
_______________________________________________________________________________
COURSE: ELECTIVE II – DIGITAL IMAGE PROCESSING
CREDIT - 4
Objectives:
To enable the understand the concepts of output primitives of Computer Graphics.
To Learn 2 D and 3 D graphics Techniques.
To Study various Image Processing techniques
Outcomes:
The students will be able to:
Understand various 2D Geometric Transformations & Clipping,
Understand the basic 3D Concepts & Fractals, Introduction of Animation, Image
Enhancement Techniques
Code Course
Teaching Period
/ Week Credit
Duration
of
Theory
Exam
(in Hrs.) L Pr./ Tu Int. Ext. Total
MCS402A Digital Image Processing 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce
students to image
processing
concepts
Introduction
Fundamental Steps in Digital Image Processing:
Components of an Image Processing System, Basic
Concepts in Sampling and Quantization, Representing
Digital Images, Spatial and Gray-Level Resolution.
Written Unit
Test – I
(Marks 25)
2
To demonstrate
techniques of
image
enhancement
Image Enhancement in the Spatial Domain
Some Basic Intensity Transformation Functions: Image
Negatives, Log Transformations, and PowerLaw
Transformations. Piecewise-Linear
Assignments
will be given
for the above
topics.
(Marks 5)
3
To demonstrate
the concept of
transformation of
image
Transformation Functions
Contrast stretching, Gray-level slicing, Bit plane
slicing. Histogram Processing: Image Histogram and
Histogram Equalization, Image Subtraction, and Image
Averaging.
Assignments
will be given
for the above
topics.
(Marks 5)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 56
4 To elaborate
filtering concept
Spatial Filtering
Basics of Spatial Filtering, Smoothing Spatial Filters
Smoothing Linear Filters, Order-Statistics Filters.
Sharpening Spatial Filters: Use of Second Derivatives
for Enhancement–The Laplacian, Unsharp masking and
High-Boost Filtering: Use of First Derivatives for
(Nonlinear) image sharpening - The Gradient– Robert,
Prewitt and Sobel Masks. Combining Spatial
Enhancement Methods.
Online Class
test will be
conducted.
(Marks 15)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) Amrendra Sinha, ArunUdai, (2007), Computer Graphics –Tata McGraw-Hill Education
2) Rajesh K. Maurya (2011), Computer Graphics -- Wiley India Pvt. Limited
REFERENCE BOOKS:
1) Donald Hearn and M Pauline Baker, (2007), Computer Graphics C Version -- Computer
Graphics, C Version, 2/E, Pearson Education.
2) Rafael C. Gonzalez and Richard E. Woods, (2010), Digital Image Processing (3rd
Edition), Pearson Education.
3) Roy A. Plastock, Roy A. Plastock- (2009), Schaum's Outline of Computer Graphics 2/E
4) James D. Foley, Andries van Dam, Steven K. Feiner, John F. Hughes,(2000), Computer
Graphics: Principles and Practice in C, Pearson Education.
5) David F. Rogers, James Alan Adams, (1990), Mathematical elements for computer
graphics, McGraw-Hill
6) Peter Shirley, Stephen Robert Marschner (2009) Fundamentals of Computer Graphics A
K Peters, Limited, 3rd ed.
7) Anil K. Jain, (1989), Fundamentals of digital image processing, Prentice Hall
_______________________________________________________________________________
COURSE: ELECTIVE II – ROBOTICS
CREDIT - 4
Objectives:
To enable students to design an agent that is Robot
To enhance understanding in implementation of Robot
Outcomes:
The students will be able to:
Understand Robots design and implementation in detail
Understand detailed working of Robot
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 57
Code Course
Teaching Period
/ Week Credit
Duration
of
Theory
Exam
(in Hrs.) L Pr./ Tu Int. Ext. Total
MCS402B Robotics 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To study the basics of
the robot and the
theory behind it.
Introduction to Robotics What is a Robot? Definition, History of Robots:
Control Theory, Cybernetics, Grey Walter Tortoise,
Analog Electronic Circuit, Reactive Theory,
Braitenberg’s Vehicle, Artificial Intelligence, Vision
Based Navigation, Types of Robot Control.
Written Unit
Test – I
(Marks 25)
2
To study the different
components of the
Robot and the actions
the robot would
perform
Robot Components
Embodiment, Sensors, States, Action, Brains and
Brawn, Autonomy, Arms, Legs, Wheels, Tracks, and
What really drives them effectors and actuators:
Effector, Actuator, Passive and Active Actuation,
Types of Actuator, Motors, Degree of freedom
Locomotion: Stability, Moving and Gaits, Wheels
and Steering, Staying on the path. Manipulators: End
effectors, Teleoperation, why is manipulation hard?
Sensors: Types of Sensors, Levels of Processing,
Passive and Active sensors, Switches, Light sensors,
Resistive position sensor.
Assignments
will be given
for the above
topics.
(Marks 5)
3
To elaborate on
sensing through Sonar,
Lasers and Cameras
Sonar, Lasers and Cameras
Ultrasonic and Sonar sensing, Specular Reflection,
Laser Sensing, Visual Sensing, Cameras, Edge
Detection, Motion Vision, Stereo Vision,
Biological Vision, Vision for Robots, Feedback or
Closed Loop Control: Example of Feedback
Control Robot, Types of feedback control, Feed
forward or Open loop control.
Assignments
will be given
for the above
topics.
(Marks 5)
4 To study languages to
program Robot
Languages for Programming Robot
Algorithm, Architecture, many ways to make a
map, what is planning, Cost of planning, Reactive
systems, Action selection, Subsumption
architecture, How to sequence behavior through
world, hybrid control, Behavior based control and
Behavior Coordination, Behavior Arbitration,
Distributed mapping, Navigation and Path
planning.
Online Class
test will be
conducted.
(Marks 15)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 58
TEXT BOOK:
1) Deepak Khemani, (2013), A First course in Artificial Intelligence, Tata McGraw Hill
Education (India) private limited
REFERENCE BOOKS:
1) Maja J Matarić, (2007), The Robotics Primer, MIT press Cambridge, Massachusetts,
London, England
2) Milan Sonka,Vaclav Hlavac, Roger Boyle (2007), Image Processing, Analysis, and
Machine Vision, Thomson Learning
3) Robert Haralick and Linda Shapiro (1993), Computer and Robot Vision, Vol I, II,
Addison-Wesley.
_______________________________________________________________________________
COURSE: ELECTIVE – BLOCKCHAIN TECHNOLOGY
CREDIT - 4
Objectives:
To elaborate the functional/operational aspects of cryptocurrency ECOSYSTEM.
To Understand emerging abstract models for Blockchain Technology.
To Identify major research challenges and technical gaps existing between theory and
practice in cryptocurrency domain
Outcomes:
The students will be able to:
Understand various Blockchain, Ethereum Blockchain, and Algorithms and Techniques
Understand the concept of Trust Essentials, Hyperledger, Smart Contracts, Fabric
Composition
Code Course
Teaching Period
/ Week Credit
Duration
of
Theory
Exam
(in Hrs.) L Pr./ Tu Int. Ext. Total
MCS402C Blockchain Technology 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
To introduce students
to Blockchain
technology and its
fundamentals
Introduction to centralized/decentralized
currency
Intent of centralized/decentralized currency, the
consensus problem - Asynchronous Byzantine
Agreement - AAP protocol and its analysis -
Nakamoto Consensus on permission-less, nameless,
peer-to-peer network - Abstract Models for
BLOCKCHAIN - GARAY model - RLA Model -
Proof of Work ( PoW) as random oracle - formal
Written Unit
Test – I
(Marks 25)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 59
treatment of consistency, liveness and fairness -
Proof of Stake ( PoS) based Chains - Hybrid
models ( PoW + PoS)
2
To introduce students
to the basics of
cryptography and
cryptocurrency
Cryptographic basics for cryptocurrency
Short overview of Hashing, signature schemes,
encryption schemes and elliptic curve
cryptography, Bitcoin - Wallet - Blocks - Merkley
Tree - hardness of mining - transaction verifiability
- anonymity - forks - double spending -
mathematical analysis of properties of Bitcoin
Assignments
will be given
for the above
topics.
(Marks 5)
3 To elaborate the
concept of EVM
Ethereum
Ethereum Virtual Machine (EVM) - Wallets for
Ethereum - Solidity - Smart Contracts - some
attacks on smart contracts.
Assignments
will be given
for the above
topics.
(Marks 5)
4
To demonstrate new
trends in Blockchain
technology
Trends and Topics Zero Knowledge proofs and protocols in
Blockchain - Succinct non interactive argument for
Knowledge (SNARK) - pairing on Elliptic curves -
Zcash.
Online Class
test will be
conducted.
(Marks 15)
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1) Arvind Narayanan, Joseph Bonneau, Edward Felten, Andrew Miller, and Steven
Goldfeder.(2016), Bitcoin and cryptocurrency technologies: a comprehensive
introduction. Princeton University Press.
REFERENCE BOOKS:
1) Joseph Bonneau et al, SoK: Research perspectives and challenges for Bitcoin and
cryptocurrency, IEEE Symposium on security and Privacy, 2015
2) J.A.Garay et al, The bitcoin backbone protocol - analysis and applications EUROCRYPT
2015 LNCS VOl 9057, ( VOLII ), pp 281-310. (eprint.iacr.org/2016/1048)
3) R.Pass et al, Analysis of Blockchain protocol in Asynchronous networks , EUROCRYPT
2017, ( eprint.iacr.org/2016/454) . A significant progress and consolidation of several
principles). 4. R.Pass et al, Fruitchain, a fair blockchain, PODC 2017 (
eprint.iacr.org/2016/916).
_______________________________________________________________________________
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 60
COURSE: ELECTIVE – MODELING AND SIMULATION
CREDIT - 4
Objectives:
To provide basic understanding of Modeling and Simulation
Students will find it easy to use this knowledge in profession for applying to various
systems and design
Outcomes:
The students will be able to:
Understand the techniques of modeling in the context of hierarchy of knowledge about a
system and develop the capability to apply the same to study systems
Students will learn different types of simulation techniques.
Code Course
Teaching Period
/ Week Credit
Duration
of
Theory
Exam
(in Hrs.) L Pr./ Tu Int. Ext. Total
MCS402D Modeling and Simulation 4 - 2 2 4 2
Module
No.
Objective Content Evaluation
1
Students will study the
basics of modeling
paradigms appropriate
for conducting
simulations.
Simulation Concepts
Systems, modeling, general system theory,
concept of simulation, simulation as a decision-
making tool, types of simulation.
Written Unit
Test – I
(Marks 25)
2
Students will learn
various distributions and
testing of random
numbers
Random Numbers
Pseudo random numbers, methods of generating
random varieties, discrete and continuous
distributions, testing of random numbers.
Assignments
will be given
for the above
topics.
(Marks 5)
3
Students will understand
the concept of designing
simulation experiments
Design and simulation experiments
Problem formulation, data collection and
reduction, time flow mechanism, key variables,
logic flow chart, starting condition, run size,
experimental design consideration, output
analysis and interpretation validation.
Assignments
will be given
for the above
topics.
(Marks 5)
4
Students will learn
various simulation-based
case studies
Simulation Languages and Case Studies
Comparison, and selection of simulation
languages, study of any one simulation language,
development simulation models using the
simulation language studied for systems like
queuing systems, production systems, inventory
systems.
Online Class
test will be
conducted.
(Marks 15)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 61
EVALUATION: 1) On Four Modules of 50 marks 2) Final examination of 50 marks 3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS:
1. Ross, (2010), Simulation, 4e, Elsevier, ISBN-9788131214626 REFERENCE BOOKS:
1. Zeigler (2018), Theory of Modeling and Simulation, 2e, Elsevier, ISBN-9788131207406 2. Birta (2013), Modeling and Simulation: Exploring Dynamic System Behaviour, Springer,
IBSN978-81-8489-365-6 3. Jerry Banks and John, S. Carson (2009), Discrete Event System Simulation, PHI 4. Shannon, R.E (1975)., Systems Simulation, The Art and Science, PHI
_________________________________________________________________
COURSE: RESEARCH PAPER WRITING
CREDIT - 4
Objectives:
To enable the students to gain experience of identification of research problem
To perform research to solve real time problem using new technologies
Outcomes:
The students will be able to:
Understand various processes involved in writing research paper
Write research paper in specified format
Code Course
Teaching Period
/ Week Credit
Duration
of
Theory
Exam
(in Hrs.) L Pr./ Tu Int. Ext. Total
MCSL403 Research Paper Writing - 4 2 2 4 -
Module
No.
Objective Content Evaluation
1
To help students to identify research problem
Research Problem Identification and Literature
Review
Presentation
(Marks 10)
2
To design the experiment to be conducted
Research Design Presentation
(Marks 10)
3
To conduct experiment after data collection
Data Collection, Experiment Conducted Presentation
(Marks 15)
4
To perform analysis and presentation of results
Data Analysis and Result Presentation Presentation
(Marks 15)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 62
EVALUATION:
1) On Four Modules of 50 marks
2) Final examination of 50 marks
3) Total marks = Internal 50 + External 50 = 100
TEXT BOOKS: 1) Brinoy J Oates, (2006), Researching Information Systems and Computing, Sage Publications
India Pvt Ltd REFERENCE BOOKS: 1) Kothari, C.R., (1985), Research Methodology, Methods and Techniques, third edition, New
Age International 2) Juliet Corbin & Anselm Strauss, (2008), Basic of Qualitative Research (3rd Edition), Sage
Publications 3) Willkinson K.P, L Bhandarkar, (2010), Formulation of Hypothesis, Hymalaya Publication,
Mumbai 4) John W Best and V. Kahn, (2010), Research in Education, PHI Publication. _______________________________________________________________________________
COURSE: SOFTWARE PROJECT CREDIT: 12 Objectives:
Achieve hands on experience in an organization
Relate classroom and textbook learning to the real world.
Learn the professional skills and interpersonal relationship in professional environment
Outcomes: The students will be able to
Attain an exposure to real life organizational and environmental situations
Attain technical skills as per the requirements of the domain
Adapt professional and interpersonal ethics.
Articulate SDLC phases in developing software project and in writing the project document.
Code Course
Teaching Period /
Week Credit
Duration of
Theory
Exam (in
Hrs.) L Pr./ Tu Int. Ext. Total
MCSL404 Software Project - 12 6 6 12 -
Module
No.
Objective Content Evaluation
1
To help students to identify
problem, check its
feasibility, gather
requirements and analyse
them
Problem Identification, Feasibility
study, Requirement Gathering,
Requirement Analysis
Presentation 1
(50 marks)
DR.B.M.N. COLLEGE OF HOME SCIENCE (AUTONOMOUS) M.Sc. Computer Science
PAGE NO: 63
2 To help students to plan
project activities
Project planning, design Presentation 2
(30 marks)
3 To perform software coding
and testing
Project Coding and Testing Presentation 3
(20 marks)
4 To present the developed
software
Final Presentation of the Project Presentation 4
(50 marks)
EVALUATION:
1) On four Modules of 150 marks
2) Final examination of 150 marks
3) Total marks = Internal 150 + External 150 = 300
TEXT BOOKS:
1) Roger S Pressman (2019), Software Engineering, 8th edition, McGraw Hill publication.
2) Kathy Schwalbe (2014), Managing Information Technology Project, 6th edition, Cengage
Learning publication.
REFERENCE BOOKS:
1) Jack T Marchewka (2013) , Information Technology Project Management , Wiley India
publication.
2) KK Agrawal, Yogesh Singh (2008), Software Engineering 3rd edition by New Age
International publication.
3) Kogent Learning Solutions Inc(2012), Software Engineering, Dreamtech Press.
4) Douglas Bell (2005), Software Engineering for students: A Programming Approach,
Pearson publication.
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