Mount Carmel College Autonomous

Mount Carmel College Autonomous Affiliated to Bengaluru Central University

58, Palace Road, Bengaluru- 560 052

Department of Computer Science

Regulations, Scheme & Syllabus

for

MSc Programme

Choice Based Credit System (CBCS)

w.e.f. 2019 Batch

The Syllabus for the MSc Programme in Computer Science for the I–II Semesters from the academic

year 2019-2020.

Abstract

Mount Carmel College offers various Under Graduate and Post graduate programmes in the Science

discipline and Application areas. The under graduate programmes span through three academic years

with six semesters of four months duration each and post graduate programmes span through two

academic years with four semesters of four months duration each.MSc programme in Computer

Science is designed to provide an insight into computing through advanced concepts, principles,

strategies and skills supplemented with practical knowledge to effectively develop and work with a

range of technologies to build systems and applications that help apply in real-time computing

environments. The program combines strong fundamentals, projects, team oriented activities, and soft

skills, leading to a holistic education.

The first two semesters have four core courses of which three courses are coupled with lab modules

and two allied courses. Community Development Programme is mandatory in the Second semester to

enable the students to be aware of socio-economic impacts. The third semester opens avenues for

specialization by offering four core courses of which three courses are associated with lab modules,

one allied course and one open Elective. This open elective course encourages students to interact with

other disciplines. The fourth semester offers three elective courses and a project.

All the papers are assessed through Continuous Internal Assessment and End Semester Examination.

Regulations and Scheme of M.Sc (CSc)

1. Eligibility :

a) B.Sc. (Computer Science) or BCA/B.E.(CS/IS) with Mathematics as one of the subject and atleast 50%

aggregate marks of all optional subjects (throughout 3 years B.Sc. / BCA course)

b) The minimum requirement for SC / ST candidates are relaxed in accordance with University

regulations. 2. Duration of the Course: Two academic years consisting of four semesters.

3. Medium of Instruction: The medium of Instruction and Examination shall be in English.

4. Intake: Total number of students not to exceed – 30

5. Evaluation Procedure for core/elective courses with practicals / tutorials :

a) Continuous Internal Assessment for theory ( CIA ) : 30 Marks

b) End Semester Examination for theory( ESE ) : 70 Marks

c) Continuous Internal Assessment for Practicals/ Tutorials ( CIA ) : 15 Marks

d) End Semester Examination forPracticals/ Tutorials ( ESE ) : 35 Marks

e) Students should secure a paper minimum of 40% each in end semester theory and in theory

total (CIA + ESE), end semester practical examination and in practical total (CIA + ESE) and an aggregate

of 50%.

6. Evaluation Procedure for core/elective courses without practicals / tutorials :



7. Evaluation Procedure for Allied courses:



c) Students should secure a paper minimum of 40% each in end semester theory and in theory

total (CIA + ESE).

8. Evaluation Procedure for Project

IV Semester Project :

a) Continuous Internal Assessment ( CIA ) : 50 Marks

Two tests 20

Assignments / Projects / Presentations 10

Total 30

Pre-final test 10

Assignments / Projects / Presentations 5

Total 15

Review - I 20

Review - II 20

Guide Evaluation of work progress 10

Total 50

b) End Semester Examination ( ESE ) : 150 Marks

c) Students should secure a paper minimum of 40% in end semester examination and in total

(CIA+ ESE).

Programme Outcomes :

PO1 : Able to demonstrate a broad knowledge of Computer Science which includes file structures,

computer programming skills, computing skills, algorithm design, Theory of computation, Data mining,

Artificial Intelligence, information security

PO2 : Demonstrate the ability to recognize, design and implement efficient software solutions to problems,

communicate effectively and to work as a team

PO3 : Demonstrate the ability to conduct a research or applied Computer Science projects, requiring

writing and presentation skills which exemplify their skills in Computer Science

PO4 :Write programs utilizing modern software tools, Apply programming principles effectively and

write procedural code to solve complex problems

PO5 : Able to learn and adapt to new technologies and use it effectively for analyzing complex real-world

problems and devise computer-based solutions

PO6 : Retrieve, use and evaluate relevant professional information, apply research methods, techniques,

and problem solving approaches in the specialization areas

MSc- Framework

I SEMESTER

Sl. No. Course Code Name of the Course LTP Cre

dits

CIA

Marks

Tuto

rial

ESE Total

Marks

1 MCS1FSCCC-01 File Structures 4:0:2 5 30+15 70+35 150

2 MCS1TCCC-02 Theory of Computation 4:2:0 5 30 50 70 150

3 MCS1ADBMSCC-

03

Advanced DBMS 4:0:2 5 30+15 70+35 150

4 MCS1AJPCC-04 Advanced Java

Programming

3:0:4 5 30+15 70+35 150

5 MCS1PPAC-01 Python Programming 0:0:4 2 15 35 50

6 MCS1TCSAC-02 Technical and

Communication Skills

2:0:0 2 15 35 50

Total

24 700

II SEMESTER

Sl. No. Course Code Name of the Course LTP Cre

dits

CIA

Marks

Tuto

rial

ESE Total

Marks

1 MCS2DMTCC-05 Data Mining

Techniques

4:0:2 5 30+15 70+35 150

2 MCS2AICC-06 Artificial Intelligence 4:2:0 5 30 50 70 150

3 MCS2AACC-07 Advanced Algorithms 4:0:2 5 30+15 70+35 150

4 MCS2WTCC-08 Web Technology 3:0:4 5 30+15 70+35 150

5 MCS2MADAC-03 Mobile Application

Development

0:0:4 2 15 35 50

6 MCS2RMAC-04 Research

Methodology

2:0:0 2 15 35 50

7 MCS2CDP Community

Development

Programme

2 50 50

Total

26 750

IV SEMESTER

Sl.

No. Course Code

Name of the

Course LTP

Credi

ts

CIA

Marks

Tutorial ESE

Total

Marks

1 MCS4AMLEC-05 Advanced Machine

Learning 3:0:4 5 30+15

70+35 150

2 MCS4OTEC-06 Optimization

Techniques 4:2:0 5 30

50 70 150

3 MCS4IOTEC-07 Internet of Things 4:0:0 4 30

70 100

4 PR-01 Project / Viva Voce 12 8 50

150 200

22

600

Total

98

2800

III SEMESTER

Sl.

No. Course Code Name of the Course LTP Cre

dits

CIA

Marks

Tutori

al

ESE Total

Marks

1 MCS3SDSEC-01 Statistics for Data

Science

4:2:0 5 30 50 70 150

2 MCS3MLEC-02 Machine Learning 3:0:4 5 30+15 70+35 150

3 MCS3CCDEC-03 Cloud Computing

for Data Science

4:0:2 5 30+15 70+35 150

4 MCS3BDAEC-04 Big Data Analytics 4:0:2 5 30+15 70+35 150

5 MCS3DVTAC-

05

Data Visualization

Techniques

2:0:0 2 15 35 50

6 OE Open elective 2:0:0 2 15 35 50

7 Internship Report 2 50 50

8 Total 26 750

MCS1FSCCC-01: FILE STRUCTURES

Total No. of Hours: 52 L:T:P : 4:0:2

CO1: Understand the need for Data Structures when building application CO2: Analyze the need for optimized algorithm

CO3: Ability to understand insertion and deletion of data for different data structures CO4: Understand the efficient implementation of sorting and searching techniques

Module

I

Introduction and overview

Introduction, Basic Terminology, Data Structures, Operations, Algorithms: Time

& Space Complexity, Algorithmic Notation, Abstract Data Types. Programming

standards and ethics.

Linked Lists

Introduction, Linked lists and Memory Representation, Traversing, Searching,

Memory Allocation, Garbage Collection, Insertion, Deletion, Circular Linked

list, Two-way Lists(Doubly). Linked List Implementation of Stack and Queue

12 hrs

Module

II

Stacks and Queues

Stacks, Array Representation, Arithmetic Expressions, Polish Notation,

Application of Stacks, Recursion, Towers of Hanoi, Implementation of

Recursive procedures by Stack, Queues, Queue Array Representation.

10 hrs

Module

III

Sorting

Introduction, Sorting, Insertion Sort, Selection Sort, Shell Sort, Merging, Merge-

Sort, Quick Sort, Radix Sort, External Sorting.

Searching

Hashed List Searches: Hashing Methods - Direct method, Subtraction Method,

Modulo-division Method, Digit-extraction Method, Midsquare Method, Folding

Method, Rotation Method, Pseudorandom Hashing. Collision Resolution – Open addressing, Linear Probe, Quadratic Probe,

Pseudorandom Collison Resolution, Linked List Collision Resolution, Bucket

Hashing, Combination Approaches. Text Searching using Knuth-Morris-Pratt

algorithm.

10 hrs

Module

IV

Trees

Introduction, Binary Trees, Representing Binary Trees in memory, Traversing

Binary Trees, Traversal Algorithms, Binary Search Trees, Searching, Inserting

and deleting in Binary Search Trees, Heap, Heap sort, Huffman’s Algorithm.

Balanced Tree

AVL Trees: AVL Balance Factor, Balancing Trees, AVL node structure, AVL

Tree Rotate Algorithms.

10 hrs

Module

V

Multiway Search Trees, B-Trees

B-Trees: B-Tree insertion, Deletion, Traversal and Search algorithm, Simplified

B Trees, 2-3 Tree, 2-3-4 Tree, Variations of B Tree - B+ Tree, B* Tree.

Graphs Graph Theory Terminology, Sequential representation of Graphs, Adjacency

matrix, Path matrix, Linked representation of a Graph, Operations on Graphs,

Depth First and Breadth First Traversing a Graph, Minimum Spanning Tree

Algorithm.

10 hrs

REFERENCE BOOKS [1] Gilberg, F Richard &Forouzan, A Behrouz, Data StructuresAPseudocode approach with C,2nd Edition,

Cengage, 2008.

[2] Horowitz Sahni Anderson-Freed, Fundamental of Data Structures in C, Universities

Press, Reprint 2008.

[3] Richard Johnsonbaugh, Algorithims,Pearson Education, 2nd Edition, 2008 [4] Robert Sedgwick, Algorithim in C++, Addison-Wesley Publishing Company.

[5] Knuth, Donald E, Art of Computer Programming, Sorting & Searching, Addison-Wesley, 2005.

FILE STRUCTURES LAB

1

Program to implement :

a) Singly Linked List

b) Doubly Linked List

c) Circular Linked List

2

Program to implement:

a) Stack using pointers

b) Infix to Postfix

c) Evaluation of Postfix Expression

d) Tower of Hanoi

3


a) Queue

b) Circular Queue

4

Programs to implement:

a) Tree Traversals on Binary Trees

b) Graphs Search Methods

5

Program to implement operations on:

a) AVL Trees

b) Splay Trees

6

There are flight paths between cities. If there is a flight between city A and city B

then there is an edge between the cities. The cost of the edge can be the time that

flight takes to reach city B from A, or the amount of fuel used for the journey.

Represent this as a graph. The node can be represented by airport name or name of

the city. Use adjacency list representation of the graph or use adjacency matrix

representation of the graph.

7


a) Sorting Techniques (Insertion, Merge, Quick, Selection)

b) Searching Techniques

Practical Examination Question Paper Pattern for 35 marks Scheme of Evaluation:

Part – A : Three Questions from the lab list of the subject to be given by the examiner and two questions will

be answered and executed by the students of their choice.

Part A

Writing two Programs 10 Marks

Execution of Two programs 20 Marks

Viva-Voce 05 Marks

Total 35 Marks

MCS1TCCC-02: THEORY OF COMPUTATION

Total No. of Hours: 52 L :T :P : 4 :2 :0

Course Outcomes:

CO1:Understand the importance of automata as a modelling tool of computational problems

CO2: Understand the role of regular languages and context-free languages and their

limitations

CO3: Understand the role of key problems in defining classes of equivalent problems from a

computational perspective- Push down automata and Turing machines

CO4: Be familiar with thinking analytically for problem-solving situations in related areas

of computer science

CO5: Understand the limitations of computational procedures

REFERENCE BOOKS:

[1] John E. Hopcroft, Rajeev Motwani, Jeffrey D.Ullman, “Introduction to Automata Theory,

Languages and Computation”, 3rd Edition, Pearson Education, 2011.

[2] John C Martin, “Introduction to Languages and Automata Theory”, 3rd Edition,

Tata McGraw-Hill, 2007.

[3] Daniel I.A. Cohen, “Introduction to Computer Theory”, 2nd Edition, John Wiley and Sons,

2009.

[4] Harry R Lewis, Christos H. Papadimitriou, “Elements of the Theory of Computation”,

Second Edition, 2015

[5] Thomas A. Sudkamp, “An Introduction to the Theory of Computer Science, Languages

and Machines”, 3rd Edition, Pearson Education, 2006.

Module

I

Basic mathematical terms and concepts: Sets, Relations, Languages,

Fundamental proof techniques, Closures and algorithms, Finite representations of

Languages, Finite Automata – Deterministic finite automata, Design procedure

for constructing DFA, Nondeterministic finite automata, Construction of NFA,

DFA from NFA

12hrs

Module

II

Regular Expressions and Context-free Languages: Regular expressions,

Regular languages, Languages that are not Regular, Normal forms, Context-free

grammars, Languages that are not context-free, Algorithms for context-free

grammars

10hrs

Module

III

PDA and Turing Machine: Parse trees, Decision trees, Pushdown automata,

Construction of PDA, Turing Machines – Definition, Computing with TM,

Nondeterministic Turing machines, Grammars, Extensions of TM. 10 hrs

Module

IV

Undecidability: Church-Turing thesis, Universal Turing machines, Halting

problems, Unsolvable problems about Turing machines, Properties of Recursive

Languages

10 hrs

Module

V

Computational Complexity and NP-completeness: The Class P, Problems,

Boolean satisfiability, Class NP, Polynomial-time reductions, Cook’s Theorem,

NP complete problems

10hrs

Theory of Computation – Tutorial Session

Exercise - 1.

a.Construct the powerset for the following sets.

(i) {a, b}

(ii) {0, 1} ∪ {1, 2}

(iii) {z}

(iv) {0, 1, 2, 3, 4} ∩ {1, 3, 5, a}

(v) {0, 1, 2, 3} − {1, 3, 5, a}

(vi) ∅(the empty set)

b. Determine the cardinality of the following languages over the alphabet Σ = {0, 1}(That is, are they finite,

infinite and countable, or infinite and uncountable). Prove your answers.

(i) Σ0

(ii) Σ4

(iii) Σ∗= Σ0 ∪Σ1 ∪Σ2 ∪Σ3 ∪・・・

Exercise - 2.

a. Find a possible alphabet Σ for the following languages. A word foobar should be interpreted as a string of

characters f, o, o, b , a and r.

(i) The language L = {oh, ouch, ugh}

(ii) The language L = {apple, pear, 4711}

(iii) The language of all binary strings

b. Describe what the Kleene star operation ∗over the following alphabets produces.

(i) Σ = {0, 1}

(ii) Σ = {a}

(iii) Σ = ∅(the empty alphabet)

Exercise - 3.

a. Describe what the + operation over the following alphabets produces.

(i) Σ = {0, 1}

(ii) Σ = {a}

(iii) Σ = ∅(the empty alphabet)

b. State the alphabet Σ for the following languages:

(i) L = Σ∗= {ε, 0, 1, 00, 01, 10, 11, 000, 001, 010, 011, . . .}

(ii) L = Σ+ = {a, aa, aaa, . . .}

(iii) L = Σ+ = {ε}

c. Assuming that Σ = {0, 1}, construct complement languages for the following.

(i) {010, 101, 11}

(ii) Σ∗− {110}

(iii) Σ+− ε

d. Let Σ be an alphabet, and let ε be the empty string over Σ.

(i) Is ε in Σ?

(ii) Does it hold that εεε= ε?

(iii) Let x and y be two strings over Σ.

Is the concatenation of x and y always the same as the concatenation of y and x?

e. A string is infinite when its length is infinite. Let Σ be an arbitrary alphabet.

(i) Does Σ∗contain any infinite string?

(ii) If Σ would be an infinite alphabet (which it actually may not be), would the answer still be

the same?

Exercise - 4.

a. For each of the following languages on ∑ = {a, b} draw a DFA that accepts the following

(i) All strings that have no a's.

(ii) All strings with three a's and any number of b's.

(iii) All strings of lengths two, three, and four ba

b. For the alphabet ∑ = {a, b}, draw a DFA that is equivalent to the following NFA.

Exercise - 5.

a. Write regular expressions for the following languages over the alphabet ∑ = {a, b}

(i) All strings that do not end with aa.

(ii) All strings that contain an even number of b’s.

(iii) All strings which do not contain the substring ba.

b. Draw DFA’s for each of the languages described above so that no DFA must contain more than 4 states.

Exercise - 6.

a. Consider the following non-deterministic finite automaton (NFA) over the alphabet

∑ = {0, 1}.

(i) What does NFA recognizes?

(ii) Write the regular expression for this language

(iii) Minimize the NFA to DFA using Minimization algorithm

b. Give context-free grammars that generate the following languages.

(i) { w ∈ {0, 1} ∗ | w contains at least three 1’s }

(ii) {w ∈ {0, 1} ∗ | w = wR and |w| is even}

(iii) { w ∈ {0, 1} ∗ | the length of w is odd and the middle symbol is 0 }

(iv) { a i b j c k | i, j, k ≥ 0, and i = j or i = k }

(v) { a i b j c k | i, j, k ≥ 0 and i + j = k }

(vi) (f) ∅

Exercise – 7.

a. Convert the following CFG into an equivalent CFG in Chomsky normal form

S → BSB | B | ε

B → 00 | ε

b. Construct pushdown automata that recognize the following languages.

A = { w∈ {0, 1} ∗ | w contains at least three 1s }

B = { w∈ {0, 1} ∗ | w = wR and the length of w is odd }

Exercise – 8.

a. Design a Turing Machine that decides the language L = {0 n1 n | n ≥ 1}.

Explain your procedure

b. Discuss any one NP-Complete problem with PPT presentation

Exercise – 9.

a. Dissertation in any one area of application of automata theory

Note: The students are continually evaluated during every tutorial session for a total of 50 marks.

MCS1ADBMSCC-03: ADVANCED DATABASE MANAGEMENT SYSTEMS


Course Objectives: To provide strong foundation of database concepts and develop skills for the design,

storage and retrieval in relational databases, XML and No SQL databases.

CO1: Understand the underlying principles of Relational Database Management System.

CO2: Analyze and understand Database storage

CO3: Understand Query processing on XML Data model

CO4: To implement and maintain an efficient database system using emerging tools

REFERENCE BOOKS

[1] Ramez Elmasri, Shamkant B Navathe, “Fundamentals of Database Systems”, Addison Wesley, Pearson

Education, Seventh Edition.

[2] Abraham Silberschatz, Henry F. Korth and S. Sudarshan, “Database System Concepts”, Tata McGraw

Hill, Sixth Edition.

[3] Jeffry A Hoffer, Mary B Prescott, HeikkiTopi, “Modern Database management System”, Pearson

Education, Ninth Edition

[4] Kristina Chodorow, MongoDB, “The definitive Guide”, O’Reilly, 2nd Edition, 2013

Module

1

Introduction to Relational Databases:

Database system applications, Purpose of database systems, Database Systems

versus File Systems, Database Languages, Database Users and Administrators,

History of Database Systems.

Data Models: Entity-Relationship Model, Relational Model.

Database System Architecture: Database System Architectures, Distributed

Databases, Parallel Databases.

10 hrs

Module

II

Relational Databases Language: Data definition in SQL, Queries in SQL,

Insert, Delete and Update Statements in SQL, Views in SQL, Specifying General

Constraints as Assertions, specifying indexes, Embedded SQL, TSQL.

Relational Database Design: First Normal Form, Functional Dependencies,

Decomposition, Desirable Properties of Decomposition, Third Normal Form,

Fourth Normal Form, Boyce-Codd Normal Form, Fifth Normal Form.

10 hrs

Module

III

Database Storage: File organization, Organization of records in files, Data

Dictionary storage.

Indexing and Hashing: Basic Concepts, Ordered Indices, B+-Tree Index Files,

Static Hashing, Dynamic Hashing.

Transaction Processing And Concurrency Control : Definition of Transaction

and ACID properties; Concurrency Control Techniques: Lock based Concurrency

control -Optimistic Concurrency Control – Timestamp based Concurrency

Control, Deadlock Handling.

12 hrs

Module

IV

Object-Based Databases: Object-Oriented Databases – Need for complex Data

Types, Object-Oriented Data Model, Object-Oriented Languages. Difference

between Object-Oriented and Object-Relational Databases.

XML Data Model: Structured, Semistructured, and Unstructured Data, XML

Hierarchical Tree Data Model, XML Documents, DTD, and XML Schema,

Storing and Extracting XML Documents from Databases - XML Languages,

Extracting XML Documents from Relational Databases.

10 hrs

Module

V

NoSQL: Definition and introduction, Document databases – MongoDB, Storing

data and accessing data from MongoDB, Querying MongoDB, Document store

internals, MongoDB reliability and durability, Horizontal scaling, CRUD

operations in MongoDB, Creating and using indexes in MongoDB.

10 hrs

ADVANCED DATABASE MANAGEMENT SYSTEMS LAB

Sl.No Exercises

1

Create a database and implement the following:

a) Data Definition Language Commands, Data Manipulation Language

Commands, Data Control Language and Transfer Control Language

Commands

b) Integrity Constraints

c) Demonstrate SQL Built-in functions(Date, Time, Numeric, String &

Conversion)

d) Retrieving data from multiple tables using joins.

e) Sub-Queries.

2

a) Creation of Views, Synonyms, Sequence and Indexes.

b) Implement variables and type declarations using TSQL block.

c) Demonstrate Exception Handling

3 a) Illustration of procedures and functions.

b) Creation of database triggers and cursors.

4

a) Create database using XML attributes and elements.

b) Implement queries based on FLOWER expressions and joins using XQuery.

c) Implement queries based on Nested queries and sorting of results using

XQuery.

d) Implement queries based on functions and types using XQuery.

5

Implement JSON Datatypes

6

a) Perform CRUD Operations to design Schemas

b) Stack, merge, Strsplit functions and implementation

c) Learn about Data Management using MongoDB

7

a) MongoDB integration with Java.

b) Implementation of Unstructured data like images and videos in MongoDB

using Java.




Part A



Viva-Voce 05 Marks

Total 35 Marks

MCS1AJPCC-04: ADVANCED JAVA PROGRAMMING


Course Outcomes:

CO1: Understand the concept of client/server applications on the Internet and write TCP/UDP socket

programs.

CO2: Implement Core Java concepts and develop sophisticated, interactive user interfaces using Java Swing

class.

CO3: Develop reusable software components using Java Beans.

CO4: Implement JDBC concepts to communicate with database.

CO5: Develop distributed application using RMI and web application using Servlets and JSP.

REFERENCE BOOKS

[1]Kogent Solution Inc , “Java 6 Programming Black Book, New Ed”, Dreamtech Press, New

Edition, 2012.

[2]Schildt Herbert, “Java 2: The Complete Reference”, Tata McGraw-Hill Professional, Ninth

Edition 2014.

[3]Kogent Solution Inc , “Java Server Programming Java Ee5 Black Book, Platinum Ed”,

DreamtechPress, New Edition, 2008

[4]Andrew Lee Rubinger, Bill Burke , “Enterprise JavaBeans 3.1: Developing Enterprise Java

Components”, OReilly Media, Inc. , 2010.

Module

I

Network Programming :

Basics of Networking, Sockets in Java, Client-Server in Networking,Internet

Addressing, Domain Naming Service, Inet4Addresses and Inet6Address,The URL

Class:URI Syntax and Components,TCP/IP and Datagram, Java Net API,

InetAddresses, Creating and Using Sockets,Creating TCP Clients and Servers,

Handling URL, Using URLConnection Objects, Working with Datagrams.

Remote Method Invocation: Introduction, Client/Server Architecture,

Implementing RMI.

10hrs

Module

II

Swing Programming: Event Handling, Text Fields, Buttons, Toggle Buttons,

Checkboxes, and Radio Buttons,Viewports, Scrolling, Sliders, Lists, Tables and

Trees, Combo boxes, Tabbed Panes, and layouts, Menus, , and Dialog Boxes,

Images.

5 hrs

Module

III

Java Database Programming: Introduction, Architecture, Driver Types, JDBC

Components - Driver, Driver Manager, Connection, ResultSet, Statement, JDBC

Data Types. 8 hrs

Module

IV

Servlet Programming:

Introduction to Servlets. The Servlets Life Cycle, Servlet API, Handling HTTP

GET and POST Request, Servlet Context, Servlet Config, Request Dispatcher,

Send Redirect. Cookies, Session Tracking, Filter API. Single Thread Model,

Multi-tier Applications Using Database Connectivity

10 hrs

Module

V

Java Server Pages: Introduction to Java Server Pages(JSP), Advantages of JSP,

Components of a JSP: Expressions, Scriptlets, Comments, Declaratives,

Directives, (Page, Include, Taglib) Implicit Objects, JSTL. JSP Standard Actions

(usebean, setproperty, getproperty, param, plugin,and fallback). Introduction to

JSP Custom Tag( SimpleTagSupport Interface), Introduction to Java Beans, The

Java Beans API – Introspector, property Descriptor, Event Descriptor, Method

Descriptor, A Bean Example, JSP with Java Beans

12 hrs

ADVANCED JAVA PROGRAMMING LAB 4 HRS/WEEK

SECTION A

SECTION B:

Project:

Students are expected to develop a mini project.




Part – B : The mini project done by the student has to be demonstrated. An add-on module has to be given.

Sl.

No

Exercise

1. Create UDP Client and Server Socket

2 Create TCP Client and Server Socket

3 Demonstrate the usage of different Layouts in Java Swing

4. Demonstrate various GUI components in Java Swing with appropriate Event

Handling

5. Implementation of Java Bean by making use of Introspector,PropertyDescriptor,

EventDescriptor classes

6 Servlet program to read Form data using GET and POST methods

7. Servlet program to demonstrate cookies

8. Servlet program to demonstrate HttpSession

9 Servlet program to demonstrate HttpServletResponse

10 Servlet program to demonstrate database access

11 Demonstrate RMI

12 Create and manage a session in JSP

13 Create custom JSP tag

14 Program to implement all the attributes of page directive tag in JSP

15 Program to demonstrate JSP actions

16 Program to perform database operations in JSP.

Part A



Viva-Voce 05 Marks

Part B

Demo 10 Marks

Add- on 05 Marks

Total 50 Marks

MCS1PPAC-01: TECHNICAL WRITING AND COMMUNICATION SKILLS

L:T:P 2:0:0

Objective

To introduce the learners to the nuances of various genres of technical communication, both oral

and written

To guide students through self-study and assignments, in performing their communicative

tasks in real-life work environment.

To enable them strengthen their oral and written communication skills so that they can achieve

their professional goals more effectively.

Module

I

Process of Communication: Process, Characteristics, Levels, Flow, Networks

and Importance.

Barriers to Communication : Interpersonal, Intrapersonal, and Organizational 4 hrs

Module

II

Communication: Creating messages, writing Documents, and presenting

Documents

Active Listening: Types, Traits of a good Listener, Active versus Passive

Listening, and Implications of Effective Listening

Effective Presentation: Analysing Audience, Organising Contents, Preparing

an outline, Visual aids, Types of Delivery, Kinesics, Proxemics, Paralinguistic,

Chronemics

6 hrs

Module

III

Fundamentals of Writing: Words and Phrases, Sentences and Paragraphs

Fundamentals of Writing: Art of Condensation, Reading Comprehension

Letters: Structure, Principles, Types

Group Communication: Group Discussions, Meetings and Conference

6 hrs

Module

IV

Group Communication: Mock GD followed by comments

Job Interviews: Types, Preparation, Success and Failure Factors

Reports: Importance, Preparatory Steps and Structure 4 hrs

Module

V

Reports: Written Practice

Proposals: Definition, Types, Structure and Style

Research Paper, Dissertation and Thesis

Instruction Manuals and Technical Description

Case Study, Work Schedule

6 hrs

REFERENCE BOOKS

[1] Raman Meenakshi and Sangeeta Sharma, Technical Communication: Principles and Practice. Oxford

University Press, 2004

[2] Stevenson, Susan and Steve Whitmore, Strategies for Engineering Communication, Wiley Singapore

Edition .2002

[3] Gerson, Sheron J and Steven M. Gerson, Technical Writing, 3rd Edition, 2000 Delhi Addison Wesley

Longman Pte.Ltd.

MCS1TCSAC-02 :PYTHON PROGRAMMING L:T:P 0:0:4

Total No. of Hours: 52 Practical Hours per Week: 04

PART A

1

a) Working with programs using Constant, Variables And Data Types Of Primitives And Non-

Primitives Variables

b) Working with programs using Decision And Branching O If, Else, Switch, Break, Continue

c) Working with programs using Looping , For, While, Do-While

2 a) Working with programs using Python Modules, The math module, The random module

b) Working with programs using Functions , Functions , Unit Testing, Local Variables and

Parameters, The Accumulator Pattern, Nesting Functions, Flow of Execution, Using the main

function, Program Development

3 a) Working with programs using Strings, A Collection Data Type, Indexing, String Methods

and Slicing, Traversal Patterns , The in and not in Operators

b) Working with programs using Lists, Concatenation, Repetition, and Element Deletion,

Objects and References, Lists and for loops, Lists as Parameters and Return values from

functions , List Comprehensions , Nested Lists

4 a) Working with programs using Tuples

b) Working with programs using Arrays

5 a) Working with programs using Files, Working with Data Files, Reading and Writing Text

Files, with Statements

b) Working with programs using Dictionaries, Dictionaries and their Operations Aliasing and

Copying

6 a) Working with programs using Exceptions , Exception Handling and Flow-of-control,

Principals of using Exceptions, Catching Multiple Specific Exceptions, Clean-up after

Exceptions

b) Working with programs using GUI Programming, simple graphics programming

7 a) Working with Database, MySQLdb

b) Working with CGI Programming

8 a) Working with programs using Networking, socket programming

b) Working with programs using Sending and receiving mail

PART B

Mini Project





Part A



Viva-Voce 05 Marks

Part B

Demo 10 Marks

Add- on 05 Marks

Total 50 Marks

MCS2DMTCC-05: DATA MINING TECHNIQUES

Total No. of Hours: 52 L: T: P: 4:0:2

Course Outcomes

CO 1: Use data preprocessing techniques to build data warehouse

CO 2: Analyze mining pattern associations rules on transaction databases

CO 3: Evaluate and examine classification methods

CO 4: Understand various clustering techniques for categorizing data

REFERENCE BOOKS [1] Jaiwei Han and MichelineKamber, “Data Mining: Concepts and Techniques”, Morgan

Kaufman Publishers, Third Edition, San Francisco, USA 2002.

[2] Pang-Ning Tan, Michael Steinbach, Vipin Kumar, “Introduction to Data Mining”, Addison-

Wesley, 2006.

[3] Arun K Pujari, “Data Mining Techniques”, University Press 2nd Edition, 2009

[4] Alex Berson and Stephen J. Smith, “Data Warehousing, Data Mining & OLAP”, Tata

McGraw – Hill Edition, Tenth Reprint 2007.

[5] Claudia Imhoff, Nicholas & et al “Mastering Data Warehouse Design”, J. Wiley.

Module

I

Data Warehousing and Online Analytical Processing: Basic concepts, Data

warehouse Modeling: Data cube and OLAP, Date Warehouse Design and Usage,

Data warehouse implementation.

Data Preprocessing: Data Cleaning, Data Integration, Data Reduction, Data

Transformation and Data Discretization.

12 hrs

Module

II

Data Mining: Introduction, Kinds of Data, Patterns and Technologies, Architecture

of Data Mining Systems, Applications, Primitives and Issues in Data Mining.

Exploring the Data: Data Objects and Attributes, Data Quality, Statistical

Descriptions of Data, Measuring Data Similarity and Dissimilarity, Data

Visualization.

08 hrs

Module

III

Mining Frequent Patterns Associations and correlations: Basic concepts,

Frequent Item set Mining Methods, Patterns evaluation Methods.

Advanced Pattern Mining: Pattern Mining: A Road Map, Pattern Mining in

Multilevel, Multidimensional Space, Constraint Based Frequent Pattern Mining,

Mining High-Dimensional Data and Colossal Patterns, Mining Compressed or

Approximate Patterns, Pattern Exploration and Application.

12 hrs

Module

IV

Classification: Basic Concepts, Decision tree induction, Bayes classification

Methods, Bayesian Belief Networks, Rule Based Classification, Lazy Learners,

Model Evaluation and Selection.

Clustering: Clustering Analysis, Partitioning Methods, Hierarchical Methods,

Density-Based Methods, Grid Based Methods, Evaluation of Clustering.

12hrs

Module

V

Data Mining Trends and Research Frontiers- Mining complex Data types, Other

Methodologies of Data Mining, Data Mining Applications, Data Mining and

Society, Data Mining Trends.

Application –Implementation using Data Mining tool.

08 hrs

DATA MINING LAB Tool to be used: R

1

a) Expressions and Functions

b) Arrays, Vectors, Matrices & Lists

2 a) Data Frames& Factors

b) Data Interfaces – CSV, Excel, Binary Files

3 a) Data Exploration – Descriptive and Dispersion measures

b) Data Visualization & Correlation – Simple, Polychoric and Partial

4 a) Regression: - Linear, Multiple, Nonlinear and Logistic

b) t-tests: - One Sample, Independent and Paired

5 a) Association Analysis and Rule Speedup

b) Classification: Naïve Bayes Classifier

6 a) Classification: Decision trees and Random Forest

b) Classification: K-Nearest Neighbor

7 a) Clustering: Partitioning clustering

b) Clustering: Hierarchical Clustering

8 a) Clustering: Density-based, Model-based and Fuzzy

b) Cluster Validation and Evaluation




Part A



Viva-Voce 05 Marks

Total 35 Marks

MCS2AICC-06: ARTIFICIAL INTELLIGENCE Total No. of Hours: 52 L:T:P : 4:2:0

Course Outcomes:

CO1 :Understand the basics of AI , AI technique and Production characteristics , analyze the 8 puzzle

problem and heuristic search techniques.

CO2 :Ability to apply knowledge representation, reasoning, game playing and planning.

CO3 : Familiarize with natural language processing, grammars, parsing techniques, Semantic

analysis and representation.

CO4 :Understand Expert systems, Rule-Based system architecture, knowledge acquisition and

knowledge system.

CO5 :Familiarize with pattern recognition, classification and understanding speech.

REFERENCE BOOKS

[1] E. Rich and K. Knight, “Artificial Intelligence”, Second Edition

[2] Dan. W. Patterson,“Introduction to Artificial Intelligence and expert system”. PHI

[3] S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Second Edition

Pearson Education

[4] Eugene Charniak and Drew McDermott, “Introduction to Artificial Intelligence”, Second

Edition

[5] Nils J. Nilson,“Principles of Artificial Intelligence”, Narosa Publication

Module

I

Introduction: Introduction to AI, Foundation of AI, AI Technique Production

characteristics, Production system characteristics, Production systems, Heuristic

Search Techniques (Best First Search, AO*, Hill Climbing, Constraint

Satisfaction), Game Playing.

8hrs

Module

II

Knowledge Representation: Knowledge Representation Issues, Approaches to

knowledge Representation, Representing simple facts in logic, computable

functions and predicates, Procedural vs declarative knowledge, forward vs

Backward Reasoning matching, control knowledge. Planning.

12 hrs

Module

III

Natural language Processing: Natural language Processing, Introduction,

overview of linguistics, Grammars and language, Basic Parsing techniques,

Semantic analysis and representation, structure, Natural Language generation,

Natural Language systems

10 hrs

Module

IV

Expert systems: Expert systems, Rule-Based system architecture Non-production

system Architecture, dealing with uncertainty, knowledge acquisition and

validation, knowledge system Building tools. 10hrs

Module

V

Pattern Recognition: Pattern Recognition, Recognition and classification process,

learning classification Patterns, Recognizing and understanding speech. 12 hrs

ARTIFICIAL INTELLIGENCE TUTORIALS


1 Water Jug problem

2 8 Queens problem

3 Implementation of Depth First Search, Breadth First Search and Best First Search

4 Implementation of A* and AO* algorithm

5 Robot(traversal) problem using means end analysis

6 Block world problem

7 Constraint satisfaction

8 The missionaries and Cannibals problem

MCS2AACC-07: ADVANCED ALGORITHMS


Course Outcomes :

CO1 :Understand the problem type, pick an appropriate algorithm design, analyze the worst-case

running time of the algorithm using asymptotic analysis.

CO2 :Be familiar with some approximation algorithms, including algorithms that are PTAS or FPTAS.

Analyze the approximation factor of an algorithm.

CO3 :Explain major string matching algorithms and their analyses. Employ it in various

applications.

CO4 : Explain the different ways to analyze randomized algorithms and demonstrate the difference

between a randomized algorithm and an algorithm with probabilistic inputs.

CO5 :Understand the need for parallel algorithm design, choose the necessary parameters for

implementing parallel algorithms and deploy it in correct scenarios.

Module

I

Algorithm Analysis and Design Techniques : An Overview

Algorithm Analysis :Growth functions, Asymptotic notations, Recurrence relation,

Substitution method, Master Theorem.

Design Techniques: Divide and Conquer: Design Principles and Strategy, Analyzing Divide and

Conquer Algorithms, Merge Sort Algorithm, Binary Search. Greedy approach:

Design Principles and Strategy, Fractional Knapsack Problem, Task Scheduling

Problem. Dynamic programming strategies: Design Principles and Strategy,

Fibonacci Series, 0/1 Knapsack Problem. Backtracking: Design Principles and

Strategy, Sum of Subset, n queens problem. Branch-and-bound techniques:

Design Principles and Strategy, Traveling Salesman Problem.

12 hrs

Module

II

Parallel Algorithms :

Design approach to parallel algorithms, constraints, performance measures of parallel

algorithms, parallel sorting (merge sort), matrix operations in parallel (matrix

multiplication), minimum spanning tree in parallel.

10 hrs

Module

III

String Matching and Document Processing :

The naïve string matching algorithm, The Knuth-Morris-Pratt Algorithm, The Boyer-

Moore String matching algorithm, Karp-Rabin String matching algorithm,

Approximate String matching.

8 hrs

Module

IV

Probabilistic and Randomized Algorithms :

Probabilistic algorithms, Randomized Deterministic algorithms, Monte Carlo and Las

Vegas algorithms, Probabilistic Numerical algorithms, Probabilistic Parallel

algorithms

12 hrs

Module

V

Approximation Algorithms :

Combinatorial optimization, approximation factor, PTAS, FPTAS. vertex - cover

problem, The travelling salesman problem, Bin packing, The Steiner Tree problem,

The facility location problem.

10 hrs

REFERENCE BOOKS:

[1] Thomas H Corman, Charles E Leiserson and Ronald L Rivest, ClifforStien, “Introduction to

Algorithms”, Prentice Hall of India Pvt. Ltd,Third Edition.

[2] Kenneth A. Berman and Jerome L. Paul, “Algorithms”, Sanat Printers, 2008.

[3] E. Horowitz and S. Sahani, “Fundamentals of Computer Algorithms”, Galgotia Publications, 2013

[4] AnnanyLevitin, “Introduction to the Design and Analysis of Algorithms”, Pearson Education, 2nd

Edition

ADVANCED ALGORITHMS LAB




1. Binary Search Program – Deriving the complexity of Binary Search Algorithm using Master

Theorem and Substitution Method

2. Merge sort Program - Deriving the complexity of Merge sort Algorithm using Master

Theorem and Substitution Method

3. Sum of subset problem

4. Dynamic Programming : Knapsack 0/1 problem

5. Backtracking : Generate all possible configurations of queens on board and print a

configuration that satisfies the given constraints

6. Parallel Algorithm for matrix operations (Multiplication)

7. Parallel Algorithm for longest common subsequence (LCS) problem

8. The Boyer-Moore String matching algorithm

9. The Knuth-Morris-Pratt Algorithm for string matching

10. Randomized Quick sort

11. Primality testing : Probabilistic Algorithm

12. Traveling Salesperson problem : Approximation Algorithm

Part A



Viva-Voce 05 Marks

Total 35 Marks

MCS2WTCC-08: WEB TECHNOLOGY


Course Outcomes:

CO1: Understand the basics of internet technology, web system architecture and web services.

CO2: Develop static web pages using HTML and add dynamic content in web pages using JavaScript.

CO3: Create dynamic websites using PHP and understand the significance of cookies and sessions.

CO4: Understand the basic AJAX techniques and use JQuery to create dynamic web pages.

CO5: Develop dynamic websites by integrating mysql, JQuery, AJAX with PHP and explore various web

services with AJAX.

REFERENCE BOOKS

[1] DT Editorial Services, “ Web Technologies Black Book”, Dreamtech Press, Reprint 2018.

[2] Matt Doyle, ―Beginning PHP 5.3‖, Wiley Publishing, 2010

[3] Kogent Learning Solutions Inc, “HTML 5 Black Book”, Wiley India Pvt Ltd, 2011

[4] StevenHolzner, “PHP: The Complete Reference”, Tata McGraw Hill Publishers, 2008

[5] Deitel&Deitel ,”Perl How to Program”, Prentice Hall, 2011 Edition

Module

I

Introduction to Web Technologies:

History of Web, Understanding Web System Architecture, Web Browsers,

Overview of HTTP, Exploring Web Technologies, Web Services

8 hrs

Module

II

Perl Programming:Introduction, Control Structures, Arrays and Hashes,

Subroutines and functions, Modules, Introduction to CGI, Regular Expressions,

String Manipulation, File processing, File and directory manipulation,

Formatting,.

8 hrs

Module

III

PHP Programming: Features of PHP, Writing PHP Script, Variables and

Constants, Controlling Program Flow, Functions, Arrays, Files & Directories,

Working with Forms and Database: Form elements, Using PHP and MySql

Handling Cookies and Sessions: Cookies Creation, Reading and Removing

Cookies, Adding Session Data, Reading and Removing Session, Ending a

Session.

10 hrs

Module

IV

Understanding JavaScript for AJAX :Document Object Model, Creating

JavaScript Application with AJAX, XMLHttpRequestObject, Basic AJAX

Techniques, XMLHttpRequest Object VsIframes

Implementing AJAX Framework: Implementing AJAX Framework: Working

with JQuery Framework, Prototype, Dojo Toolkit, DWR, JPSpan, Rico, Spry

Framework.

10 hrs

Module

V

Integrating PHP with AJAX: AJAX- PHP Frameworks, Handling XML Data

Using PHP and AJAX, Fetching Records from mysql Database Using PHP and

AJAX

Consuming Web Services with AJAX: SOAP, WSDL, UDDI, REST

9 hrs

WEB TECHNOLOGY LAB 4 HRS/WEEK

SECTION A

SECTION B

Project

Students are expected to develop a dynamic website using the techniques that they learnt during their course

of study.





Sl.No Exercise

1 Working with Perl Scripts

2 Working with Perl Subroutines

3 Creating Perl Modules

4 Cascading Style Sheets

5 Handling Events in JavaScript

6 Web Page Validation Using JQuery

7 Fetching records from MySql database in PHP

8 Insert records in to MySql database in PHP

9 Fetch and Delete Records in Mysql database

10 View and modify Records in Mysql database

11 Demonstrate the significance of cookies

12 Handling Sessions in PHP

13 Implementing AJAX Famework

14 Integrating PHP with AJAX

15 Creating Web Services

Part A



Viva-Voce 05 Marks

Part B

Demo 10 Marks

Add- on 05 Marks

Total 50 Marks

MCS2MADAC-03: MOBILE APPLICATION DEVELOPMENT

Total No. of Hours: 52 L:T:P 0:0:4

Objective: To acquire knowledge of developing mobile applications using Android.

PART A

1 a) Getting familiarize and Working with different activities

b) Working with programs on Intents, Adapters

2

a) Working with programs with Different Layouts

b) Working with various UI controls, TextView, EditView, various Buttons,

ProgressBar, Spinner, TimePicker, DatePicker

3 a) Working with programs on Images and Graphics

b) Working with programs on Audio and Video

4 a) Working with programs on Event Listeners & Event Handlers

5 a) Working with programs on Database SQLite

6 a) Working with programs on File Manipulation

b) Working with programs on Camera Sensors

7 a) Working with programs on Connectivity, sending Messages, Accessing WiFi

b) Develop a native application that uses GPS location information.

8 a) Publishing the Apps in Net

PART B

Mini Project





Part A



Viva-Voce 05 Marks

Part B

Demo 10 Marks

Add- on 05 Marks

Total 50 Marks

MCS2RMAC-04: RESEARCH METHODOLOGY L:T:P 2:0:0

Objectives:

To enable the students to

Understand the methodology of research/ principles and techniques.

Develop skill in conducting research from planning to report writing.

Outcomes: Enable students to identify the overall process of designing a research study from its inception to

its report. Students can select and define appropriate research problem, organize and conduct research in a

structured manner. Also enables students to prepare a project proposal, to write a research report, articles

and thesis in a decipherable manner.

Module

I

Introduction to Research: Meaning, definition, objectives and

characteristics of research. Types of research- basic research

(fundamental research), applied research, action research, descriptive

research, analytical research, evaluation research, historical research,

exploratory research, industrial research, development research.

4 hrs

Module

II

Research Process: Research design, important experimental designs,

sample design. Census and sample method; theoretical basis for

sampling, methods of sampling, size of sample, merits and limitations

of sampling, sampling and non-sampling errors, reliability of sampling.

Data and methods of data collection; types of data- primary and

secondary data. Primary data collection methods- direct personal

investigation, direct oral investigation schedules and questionnaires,

interviews and type of interviews. Pre-testing and pilot study.

6 hrs

Module

III

Measurement and scaling technique: Measurement in research;

measurement scales- nominal scale, ordinal scale, interval scale, and

ratio scale. Sources of error in measurement. Scaling- meaning,

classification basis, important scaling techniques- rating scale, ranking

scale, arbitrary scale, summated scale.

4 hrs

Module

IV

Intellectual Property Rights: Patenting - definition of patent.

Patenting and fundamental research. Product and process patents,

Patent infringement, Copyright infringement and Trademarks.

Data analysis using Excel: Analysis of quantitative data and effective

presentation with tables, graphs etc., Use of Excel for Formulae

Function, Charts and Graphs, Table formula, t-test, Anova and

Correlation.

06 hrs

Module

V

Scientific writing: Research resources: reviews, abstracts, books,

journal and magazine articles- Exploration and communication;

Resources: online and print; Review of latest literature (peer

reviewed). Logical format for writing thesis and papers. Essential

features of abstract, introduction, review of literature, materials and

methods, and discussion. Reference styles. Understanding Plagiarism:

definition, unintentional plagiarism and consequences; Collaborative

work.

06 hrs

REFERENCE BOOKS

[1] Research Methods for the Biosciences. Holmes, Moody & Dine. Oxford University

Press.

[2] Experimental Design for Biologists. David J. Glass. Cold Spring Harbor Laboratory.

[3] Experimental Design for the Life Sciences. Ruxton&Colegrave. Oxford University

Press.

[4] Research Methodology, Kothari, C. R. (2005) New Delhi, Vikas Publication House.

[5] Successful Scientific writing: A step-by- step Guide for Biomedical Scientists. 2nd ed. Matthews.

Cambridge University Press, 2001.

[6] Green. R. H. Sampling Design and Statistical Methods for Environmental Biologists. John Wiley &

Sons, 1979.

[7] Swain AKPC (2008), A Textbook Of Research Methodology, 1st Edition, Ludhiana, Kalyani Publishers

[8] Sunder Rao and Richard BS (2006), an introduction to bio statistics, a manual for students in health

sciences, 4th edition, New Delhi, Prentice Hall

[9] Gupta S.P.,Statistical methods, 28th ed. Sultan Chand and Co, New Delhi,1998.

[10] Sinha, S. CandDhiman,A.K.(2002) Research methodology, EssEss Publication 2 Volumes.

MCS3SDSEC-01: Statistics for Data Science


Course Outcomes:

CO1: To differentiate among kinds of data and know various ways to present them.

CO2: To learn the distributions to perform analysis of various kinds of data.

CO3: Infer the concept of correlation and regression for relating two or more related variables.

CO4: Demonstrate the probabilities for various events.

REFERENCE BOOKS

[1] “Practical Statistics for data Scientists”, Peter Bruce and Andrew Bruce, O’Reilly Publications.

[2]. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons

Inc., New Jersey, 2015.

[3]. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand &

Sons, New Delhi, 2014.

[4]. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015.

[5]. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists,

Pearson, New Delhi, 2017

Module

1

Organization of Data & Descriptive Statistics: Origin and development of

Statistics, Types of data: primary, secondary, quantitative and qualitative data.

Types of Measurements: nominal, ordinal, discrete and continuous data.

Presentation of data by tables: construction of frequency distributions for discrete

and continuous data, graphical representation of a frequency distribution by

histogram and frequency polygon, cumulative frequency distributions.

10 hrs

Module

II

Representation of Data and Sampling Distribution: Measures of location or

central tendency: Arithmetic mean, Median, Mode, Geometric mean, Harmonic

mean. Partition values: Quartiles, Deciles and percentiles. Measures of

dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of

variation. Moments: measures of skewness, Kurtosis.

Random sampling and sample bias, selection bias, Sampling Distribution of a

statistic, Confidence Intervals, Normal Distribution, Binomial Distribution,

Poisson Distribution.

14 hrs

Module

III

Statistical Experiments and Significance Testing: Hypothesis test, Resampling.

Statistical Significance and P-values, t-tests, ANOVA, Chi square test.

12 hrs

Module

IV

Correlation And Regression (8 hours): Correlation: Scatter plot, Karl Pearson

coefficient of correlation, and Spearman’s rank correlation coefficient.

Regression: Concept of errors, Principles of Least Square, Simple linear

regression and its properties, Multiple Linear Regression, Polynomial and Spline

Regression.

8 hrs

Module

V

Probability theory (8 hours): Sample Spaces- Events - Laws of total probability

- Axioms – Counting - Conditional Probability- Bayes’ theorem and its

applications, Theorems on probability.

8 hrs

Statistics for Data Science- Tutorials Session

The student is expected to solve several problems on each of these topics.

S. No Topics

1. Measures of central tendency and partition values

2. Measures of dispersion

3. Confidence Interval

4. Distributions

5. Hypothesis Testing

6. ANOVA

7. Correlation & Regression

8. Probability theory

9. Conditional Probability and Bayes’ theorem


MCS3MLEC-02: Machine Learning Total No. of Hours: 45 L:T:P : 3:0:4

Course Outcomes:

CO1: To have a good understanding of the fundamental issues and challenges of machine learning: data,

model selection and model complexity.

CO2: To have an understanding of the strengths and weaknesses of machine learning approaches.

CO3: To appreciate the underlying relationships within and across Machine Learning algorithms and the

paradigms of supervised and un-supervised learning.

REFERENCE BOOKS

[1] Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das, “Machine Learning”, Pearson Education

[2] E. Alpaydin, “Machine Learning”, MIT Press.

[3] T. Hastie, R. Tibshirani and J. Friedman, “Elements of Statistical Learning”, Springer.

[4] C. Bishop, “Pattern Recognition and Machine Learning”, Springer

[5] ShaiShalev-Shwartz, Shai Ben-David, “Understanding Machine Learning: From

Theory to Algorithms”, Cambridge University Press.

Module

I

Introduction to Machine Learning: Introduction, What is Human Learning,

Types of Human Learning, What is Machine Learning? Types of Machine

Learning, Applications of Machine Learning, Tools in Machine Learning,

Issues in Machine Learning.

Exploring Data: Elements of structured Data, Machine Learning Activities,

Basic Types of Data in Machine Learning, Exploring Structure of Data, Data

Quality and Remediation, Data Pre- Processing.

08 hrs

Module

II

Modelling, Evaluation and Feature Engineering: Introduction, Selecting a

Model, Training a Model, Model Representation and Interpretability,

Evaluating Performance of a Model, Improving Performance of a Model.

Feature Engineering: Introduction, Feature Transformation, Feature Subset

Selection.

8 hrs

Module

III

Supervised Learning: Introduction, Example, Classification Model,

Classification Learning Steps, Common Classification Algorithms ( k-

Nearest Neighbour, Decision Tree, Random Forest Model, Support Vector

Machines)

9 hrs

Module

IV

Unsupervised Learning: Introduction, Unsupervised vs Supervised Learning,

Applications of Unsupervised Learning, Clustering, Finding Patterns using Association Rule

10 hrs

Module

V

Other Types of Learning: Bayesian Concept Learning- Introduction, importance, Bayes’ Theorem and Concept Learning, Bayesian Belief Network. Neural Network: Introduction, Understanding the Biological Neuron,

Exploring the Artificial Neuron, Types of Activation Functions, Early implementations of ANN, Architectures of Neural Network, Learning Process in ANN, Back Propagation, Deep Learning.

Representation Learning: Supervised, Neural Networks and Multilayer Perception, Independent Component Analysis, Autoencoders, Various forms of Clustering.

10 hrs

Machine Learning Lab 4 HRS/WEEK





Sl. No. TOPIC

1 Exploratory Data Analysis

2 Model building

3 Supervised Learning –Any three

KNN

Decision Tree

Random Forest

SVM

4 Unsupervised Learning

Clustering

Hierarchical Clustering

5 Neural Network Model

Single Layer Networks

Multi-layer Networks

Part A



Viva-Voce 05 Marks

Part B

Demo 10 Marks

Add- on 05 Marks

Total 50 Marks

MCS3CCDEC-03: Cloud Computing for Data Science


Objective: To help students understand how cloud is used to deploy data science solutions.

Course Outcomes:

CO1: To understand the common terms and definitions of virtualization and cloud computing.

CO2: To analyze the technical capabilities and business benefits of virtualization and cloud computing.

CO3: To summarize the fundamental concepts of cloud storage and demonstrate their use in storage

systems.

CO4: To discuss system virtualization and outline its role in enabling the cloud computing system model.

CO5: To analyze various cloud programming models and apply them to solve problems on the cloud.

Module

I

Cloud Computing Basics - cloud computing Overview – Cloud components,

Infrastructure, Services, Applications – Storage, Database services, Intranets and the

cloud – components, Hypervisor applications, First Movers in the Cloud (Amazon,

Google, Microsoft). “Bring cloud to the edge” – Introduction.

Organization and Cloud Computing – When you can use Cloud computing,

Benefits, Limitations, Security Concerns, Regulatory Issues.

12 Hrs

Module

II

Cloud Computing Technology :Hardware and Infrastructure – Clients – Mobile,

thin, Thick, Security- Data leakage, Offloading work, Logging, Forensic,

Development, Auditing, Network – Basic public Internet, The accelerated Internet,

Optimized Internet overlays, Cloud providers, cloud consumers, Services.

Accessing the Cloud – Platforms – Web Application framework, Web hosting

service, Proprietary methods, Web Applications, Web APIs- What are APIs, How

APIs work, API Creators, Web Browsers

12 Hrs

Module

III

Cloud Storage – Overview-The Basics, Storage as a service, Providers, Security,

Reliability, Advantages, Cautions, Outages, Theft, Cloud storage provider 6 Hrs

Module

IV

Developing Applications- Google, Microsoft, Intuit QuickBase, Cast Iron cloud,

Bungee connect, Development, Trouble shooting, Application Management, Local

clouds and Thin Clients

Virtualization -Virtualization in your Organization- why virtualize, How to

virtualize, concerns, security, Server solutions- Microsoft Hyper-V,VMware,

VMware Infrastructure.

10 Hrs

Module

V

Big data- definition and taxonomy-Big data value for the enterprise-Setting up the

demo environment-First steps with the Hadoop “ecosystem”.

Amazon Web Services – Amazon Elastic Compute Cloud (Amazon EC2), Amazon

SimpleDB, Amazon Simple Storage Service (Amazon S3), Amazon CloudFront,

Amazon Simple Queue Service, Elastic Block Store.

12 Hrs

REFERENCE BOOKS

[1] Anthony TVelte, Toby JVelte and Robert Elsenpeter, Cloud Computing –A Practical Approach, Tata

McGraw Hill Education Pvt Ltd, 2010.

[2] Judith Hurwitz, Alan Nugent, Dr. Fern Halper, Marcia Kaufman, Big Data for Dummies, 2012.

[3] Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions, Wiley, 2015.

[4] Michael J.Kavis, “Architecting the Cloud: Design Decisions for Cloud Computing Service Models

(SaaS, PaaS, and IaaS)”, John Wiley & Sons Inc., Jan 2014.

[5] RajkumarBuyya, Christian Vecchiola and S. ThamaraiSelvi, “Mastering Cloud Computing” -

Foundations and Applications Programming , MK publications, 2013.

[6] Gautam Shroff, “Enterprise Cloud Computing: Technology, Architecture, Applications” by Cambridge

University Press, 2010.

Cloud Computing Lab Using Hadoop 2 HRS/WEEK

1 Implementation of Para-Virtualization using VM Ware‘s Workstation/

Oracle‘s Virtual Box and Guest O.S

2 Installation and Configuration of Hadoop

3 Word count application in Hadoop

4 Sorting the data using MapReduce

5 Finding max and min value in Hadoop

6 Implementation of decision tree algorithms using MapReduce

7 Implementation of K-means Clustering using MapReduce

8 Generation of Frequent Itemset using MapReduce

9 Count the number of missing and invalid values through joining two large given datasets

10 Demonstrate sentiment analysis for product reviews ( Apache Hadoop )

11 Trend Analysis based on Access Pattern over Web Logs using Hadoop




Part A



Viva-Voce 05 Marks

Total 35 Marks

MCS3BDAEC-04: Big Data Analytics


CO1: Understand the fundamentals of data analytics techniques and platforms CO2: Design and Apply data analytics ecosystem and visualization techniques to solve various problems

CO3: Analyze the results of data analytics and visualization for various problems CO4: Evaluate the solutions of data analytics ecosystems

REFERENCE BOOKS [1] Cielen, D., Meysman, A., & Ali, M. (2016). Introducing data science: big data, machine learning, and

more, using Python tools. Manning Publications Co.

[2] Tom White, “Hadoop – The Definitive Guide; Storage and Analysis at Internet scale”, O’Reilly, Shroff

Publishers & Distributers Pvt. Ltd., 4th Edition, 2015, ISBN – 978-93-5213-067-2

[3] DT Editorial Services “Big Data – Black Book” Dreamtech Press, Edition – 2015, ISBN - 978-93- 511-

9-757-7.

[4] Dirk deRoos, Paul C. Zikopoulos, Roman B. Melnyk, Bruce Brown, Rafael Coss “Hadoop for

Dummies”, John Wiley & Sons, Inc., 2014 ISBN: 978-1-118-60755-8 (pbk); ISBN 978-1-118- 65220-6

(ebk); ISBN 978-1-118-70503-2 (ebk).

[5] Nathan Marz and James Warren,”Big Data Principles and Best Practices of Scalable Real time data

systems”, 2015, ISBN 9781617290343.

Module

I

Data Science in a Big world:

Benefits and uses of Data Science in Big data, Facets of data, Big data ecosystem

and Data Science, Data Science process.

8 hrs

Module

II

Hadoop Fundamentals: Data, Data Analysis and storage, Comparison with other

systems – Relational Database Management Systems, Grid Computing, Volunteer

Computing, History of Apache Hadoop The Hadoop Distributed File system The

Design of HDFS, HDFS Concepts – Blocks, Name nodes and Data nodes, Block

Caching, HDFS Federation, HDFS High Availability, The command-Line

Interface, Hadoop File system – Interfaces The Java Interface – Reading data from

Hadoop URL, Reading Data using File system API, Writing Data, Directories,

Querying the File system, Deleting Data Data Flow – Anatomy of a File Read,

Anatomy of a File Write, Coherency Model Parallel Copying with distcp –

Keeping an HDFS cluster Balanced

12 hrs

Module

III

Map Reduce: Data format, Analyzing the data with Unix Tools, Analyzing the

Data with Hadoop, Scaling Out Working of Map Reduce – Anatomy of a Map

Reduce Job Run, Failures, Shuffle and Sort, Task Execution Map Reduce Formats

– Input Formats, Output Formats

10 hrs

Module

IV

Pig Environment: Execution types, Running Pig programs, Grunt, Pig Latin

Editors An Example – Generating Examples, Comparison with databases Pig

Latin – Structure, Statements, Expressions, Types, Schemas, Functions, Macros

User-Defined Functions – A Filter UDF, An Eval UDF, A Load UDF Data

Processing Operators – Loading and storing of data, Filtering data, Grouping and

Joining data, Sorting data, Combining and splitting data Pig in Practice–

Parallelism, Anonymous Relations, Parameter Substitution

12 hrs

Module

V

Hive: Installing Hive – The Hive shell, An Example; Running Hive – Configuring

hive, Hive services, the Meta store

Comparison with Traditional Databases – Schema on Read Versus Schema on

Write, Updates, Transactions and Indexes, SQL-on-Hadoop Alternatives Hive QL

– Data Types, operators and functions Tables – Managed Tables and External

Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables,

Dropping Tables Querying Data – Sorting and Aggregating, Map Reduce scripts,

Joins, Sub queries, Views

10 hrs

Big Data Analytics Lab – PART A

1

HDFS

Review the commands available for the Hadoop Distributed File System:

a) Copy file foo.txt from local disk to the user’s directory in HDFS

b) Get a directory listing of the user’s home directory in HDFS

c) Get a directory listing of the HDFS root directory

d) Display the contents of the HDFS file user / fred / bar.txt

e) Move that file to the local disk, named as baz.txt

f) Create a directory called input under the user’s home directory

g) Delete the directory input old and all its contents

h) Verify the copy by listing the directory contents in HDFS

2

Map Reduce

a) Create a Job and submit to cluster

b) Track the job information

c) Terminate the job

d) Counters in MR Jobs

e) 5.Listing of Jobs

3

Pig

a) Load the data into Apache Pig from the file system (HDFS/ Local) using Load

operator

b) Store data in Apache Pig using the Store operator.

c) Execute the Diagnostic operators

d) Group the data in one or more relations

e) Perform various join operations in Pig Latin

f) Display the contents of a relation in a sorted order based on one or more fields.

4

Advanced Concepts in Pig

a) Merge the content of two relations

b) Split a relation into two or more relations.

c) Select the required tuples from a relation based on a condition.

d) Remove redundant (duplicate) tuples from a relation.

e) Explore the built-in functions provided by Pig.

5

Hive

a) Create and Drop database in Hive

b) Create, Alter and Drop Table in Hive

c) Create and Drop Views in Hive

d) Demonstrate the built-in functions in Hive

6

Process and analyze structured data using Hive QL

Select Where, Select Order By, Select Group By, Select Join

a) Visualization: Construct Bar Chart, Stacked Bars.

Big Data Analytics LAB – PART B

1

Build a word cloud using text mining tools.

a) Read a text file

b) Create a corpus from the collection of text files

c) Data Processing on the text files

d) Convert the text file into term document matrix and create a data frame

e) Making the word cloud

2

Social Network Analysis and Visualization

a) Create a graph and plot the graph

b) Show the various centrality scores such as degree, between’s, closeness,

Transitivity

c) Show Neighbourhood of graph vertices

d) Find Cliques

e) Display maximal connected components of a graph

f) Calculate cohesive blocks

3

Sentiment Analysis for products reviews using Customer Feedback

a) Load the required data set

b) Perform stemming and cleaning

c) Display the sentiment score for Neutral, Positive polarity and Negative Polarity




Part A



Viva-Voce 05 Marks

Total 35 Marks

MCS3DVTAC-05: Data Visualization Techniques


Course Objectives: To understand the purpose of data visualization and use it for data analytics

CO1: To design, create and interpret data visualizations

CO2: To conduct exploratory data analysis using visualization.

CO3: To identify appropriate data visualization techniques given particular requirements imposed by the

data.

CO4: To identify opportunities for application of data visualization in various domains.

REFERENCE BOOKS

[1] “Visual Analytics with Tableau”, Alexander Loth, Wiley Publications, 1st edition (2019).

[2] “Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures”, Claus

O Wilke, O’ Reilly Media Publications, 1st edition (2019).

[3] “Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master”, Ryan Sleeper, O’

Reilly Media Publications, 1st edition (2018).

[4] “Information Dashboard Design: Displaying Data for At-a-Glance Monitoring”, Stephen Few, Analytics

Press; 2nd Edition (2013)

Module

1

Introduction to data visualization : Introduction- Data visualization options –

Data for data visualization- Design principles- Categorical, time series, and

statistical data visualization 5 hrs

Module

II

Tableau: Introduction to Tableau- installation, architecture and environment.

Various data sources, data joining, data blending, Worksheets in Tableau.

5 hrs

Module

III

Operators and Filters in Tableau: Operators, Functions, Numeric and Non-

numeric calculations, Sorts and filters in Tableau- Basic, Quick, Context, Condition

and Top filters

5 hrs

Module

IV

Charts and Plots: The Visualization Dashboard, Charts in Tableau- bar chart, line

chart, pie chart, crosstab, scatter plot, bubble chart, bullet graph, box plot, tree map,

bump chart, Gantt chart, histogram, motion chart and waterfall chart.

6 hrs

Module

V

Introduction to other visualization tools: R, plotly in Python, Sea Born library in

Python, d3.js library 5 hrs

MCS4AMLEC-05: Advanced Machine Learning Total No. of Hours: 45 L:T:P : 3:0:4

Course Outcomes:

CO1: To understand the definition of a range of neural network models.

CO2: To be able to derive and implement optimization algorithms for these models.

CO3: To know how to evaluate a learned model in practice.

CO4: To be able to design and implement various machine learning algorithms in a range of real-world

applications.

REFERENCE BOOKS

[1] Tom M. Mitchell, “Machine Learning”, McGraw Hill Education

[2] Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning”, 2nd edition,

springer series in statistics.

[3] Ethem Alpaydın, “Introduction to machine learning”, MIT press.

[4] Ian Goodfellow, YoshuaBengio, and Aaron Courville, “Deep Learning” MIT Press.

[5] Richard S. Sutton and Andrew G. Barto,“Reinforcement Learning: An Introduction” , MIT Press.

Module

I

Introduction: Well-Posed Learning Problems, Designing a Learning System,

Perspectives and Issues in Machine Learning.

Concept- Learning and the General-to-Specific Ordering: Introduction,

Concept Learning Task, Concept Learning as a Search, FIND-S, Version

Spaces and the Candidate- Elimination Algorithm, Remarks, Inductive Bias.

8 hrs

Module

II

Decision Tree Learning: Introduction, Representation, Appropriate

Problems for Decision Tree Learning, Basic Algorithm, Hypothesis Space

Search, Inductive Bias, Issues in Decision Tree Learning.

08 hrs

Module

III

Artificial Neural Network: Introduction, Neural Network Representations,

Appropriate Problems for Neural Network Learning, Perceptrons, Multilayer

Networks and the Backpropagation Algorithm, Remarks, An Illustrative

Example, Advanced Topics I Artificial Neural Networks.

8 hrs

Module

IV

Convolution Networks: Motivation - Pooling - Variants of the Basic

Convolution Function -Efficient Convolution Algorithms -Random or Unsupervised Features, Sequence Modeling: Recurrent and Recursive Nets - Unfolding Computational Graphs - Recurrent Neural Networks - Bidirectional

RNNs - Encoder-Decoder Sequence-to-Sequence Architectures -Deep Recurrent Networks -Recursive Neural Networks, Applications

10 hrs

Module

V

Reinforcement Learning: Introduction, The Learning Task, Q Learning, Nondeterministic Rewards and Actions, Temporal Difference Learning, Generalizing from Examples, Relationship to Dynamic Programming.

11 hrs

Advanced Machine Learning Lab 4 HRS/WEEK

Practical Examination Question Paper Pattern for 50 marks

Scheme of Evaluation:




Sl. No. TOPIC

1 Exploring Learning System

2 Decision Tree Learning

3 ANN- Modelling and Exploring Neural Network

4 CNN- Modelling and Exploring Convolution Nets

5 Reinforcement Learning

Part A



Viva-Voce 05 Marks

Part B

Demo 10 Marks

Add- on 05 Marks

Total 50 Marks

MCS4OTEC-06: Optimization Techniques


Course Outcomes:

CO1: To understand basic concepts of operation research and linear programming

CO2: To comprehend the theory of optimization methods and algorithms developed for solving

various types of optimization problems

CO3: To apply the mathematical results and numerical techniques of optimization theory to solve

problems.

REFERENCE BOOKS

[1] P. Sankara Iyer-”Operations Research”- Tata McGraw-Hill, 2008.

[2] P. K. Gupta and D. S. Hira - “Operations Research” - S. Chand & co., 2007.

[3] Taha, H.A -”Operations Research – An Introduction” - Macmillan, Eighth Edition.

Module

I

Basics of Operation Research: Definition, characteristics, scope,

objectives, phases, models and limitations of Operational Research.

Linear Programming: Requirements, assumptions and formulation of linear

programming problems, graphical method of solution Simplex Method,

Artificial variables, big-M method, two-phase method, degeneracy and

unbound solutions.

8hrs

Module

II

Transportation Model: Definition, formulation, solution of

Transportation models, unbalanced Transportation Problem,

Basic Feasible Solutions - Northwest corner rule, least cost method and

Vogel’s approximation method, Optimality test - the stepping stone method

and MODI method. Assignment Model: Formulation. Hungarian method for

optimal solution. Solving unbalanced problem. Traveling salesman problem

and assignment problem.

10hrs

Module

III

Network Models: Definition, Minimum Spanning Tree algorithm, Shortest

Route problem, Maximum flow problem, CPM & PERT- Network

representation, Critical Path Computations, Linear Programming formulation

of CPM, PERT Networks

10hrs

Module

IV

Dynamic programming: Characteristics of dynamic programming.

Dynamic Programming approach for Priority Management employment

smoothening. Games Theory. Competitive games, rectangular game, saddle

point, minimax (maximin) method of optimal strategies, value of the game.

Solution of games with saddle points, dominance principle. Rectangular

games without saddle point – mixed strategy for 2 X 2 games

12hrs

Module

V

Queuing System: Elements of Queuing model, Pure birth and death models,

Generalized Poisson Queuing model, specialized Poisson, Queues - Steady-

state Measure of performance, single sever models, Multiple server models,

Matching serving model.

12hrs

Optimization Techniques- Tutorial Session 2 Hrs/week

Sl.No Problem

1 Linear Programming Problems

a) Graphical Method

b) Big-M Method

c) Two Phase Method

2 Transportation Model:

a) Unbalanced Transportation Problem

b) Least cost method

c) Vogel’s Approximation Method

3 Assignment model:

a) Implementing Hungarian Method

b) Traveling Salesman Problem

4 Network Model: a) Implementation of CPM & PERT- Network representation b) Implementing Shortest Route Problem

5 Dynamic Programming: Implementing Game Theory

6 Queuing System:

a) Implementation of Pure birth and death models

b) Implementation of Generalized Poisson Queuing model

c) Implementation of Multiple and Sever Models


MCS4IOTEC-07: INTERNET OF THINGS

Total No. of Hours: 52 L :T :P: 4:0:0

Course Outcome: CO1: Understand constraints and opportunities of wireless and mobile networks for Internet of Things.

CO2: Analyze the societal impact of IoT systems and its domains.

CO3: Develop critical thinking skills.

CO4: Analyze, design or develop parts of an Internet of Things solution and map it toward selected business

model(s)

CO5: Evaluate the impact of cloud technology and its issues related to Internet of Things

REFERENCE BOOKS:

[1] ArshdeepBahga and Vijay Madisetti,“Internet of Things- A Hands-on Approach” Universities Press,

2015, ISBN: 9788173719547

[2] Matt Richardson & Shawn Wallace, “Getting Started with Raspberry Pi”, O'Reilly (SPD),

2014, ISBN: 9789350239759

[3] Marco Schwartz, “Internet of Things with the Aruino Yun”, Packt Publishing, 2014.

Module

I

Introduction to Internet of Things –Definition and Characteristics of IoT,

Physical Design of IoT – IoT Protocols, IoT communication models, IoT

Communication APIs IoT enabled Technologies – Wireless Sensor Networks,

Cloud Computing, Big data analytics, Communication protocols, Embedded

Systems, IoT Levels and Templates - Domain Specific IoT’s – Home, City,

Environment, Energy, Retail, Logistics, Agriculture, Industry, health and

Lifestyle

12 hrs

Module

II

IoT and M2M – Software defined networks, network function

virtualization, difference between SDN and NFV for IoT

Basics of IoT System Management with NETCOZF, YANG- NETCONF,

YANG, SNMP NETOPEER

10 hrs

Module

III

Introduction to Python - Language features of Python, Data types, data

structures, Control of flow, functions,

modules, packaging, file handling, data/time operations, classes, Exception handling

Python packages - JSON, XML, HTTP Lib, URL Lib, SMTP Lib

10 hrs

Module

IV

IoT Physical Devices and Endpoints - Introduction to Raspberry PI-Interfaces

(serial, SPI, I2C)

Programming – Python program with Raspberry PI with focus of interfacing

external

gadgets, controlling output, reading input from pins

10 hrs

Module

V

IoT Physical Servers and Cloud Offerings – Introduction to Cloud Storage

models and communication APIs

Webserver – Web server for IoT, Cloud for IoT, Python web application

framework Designing a RESTful web API

10 hrs

PR-01: Project I Practical Hours per week: 12

Students (maximum two) are expected to take up a research based/ application development project

using the techniques that they learnt during their course of study and publish their work in a

research journal.

Practical Examination Question Paper Pattern

Scheme of Evaluation:

Project Demo 60 Marks

Viva-Voce 20 Marks

Add-on Module 20 Marks

Project Report 30 Marks

Research Publication 20 Marks

Total 150 Marks