Department of Data Science Syllabus for Bachelor of...

Department of Data Science

Syllabus

for

Bachelor of Science (Data Science)

Academic Year (2019)

CHRIST (Deemed to be University), LAVASA

BSc (Data Science) Course Matrix

SEMESTER - I

Course

Code

Course Name

Hrs/

Week

Credits

Marks

ENG111 PROFESSIONAL ENGLISH 3 2 100

CS151 OFFICE AUTOMATION TOOLS LAB 2 1 50

CS121P DIGITAL COMPUTER FUNDAMENTALS 4 4 100

CS122P INTRODUCTION TO PROGRAMMING USING C 5 4 100

MAT131 BASIC DISCRETE MATHEMATICS 3 3 100

MA141 PRINCIPLES OF MANAGEMENT 3 4 100

ENVIRONMENT SCIENCE 2 0

HOL HOLISTIC EDUCATION-I 1 1 50

TOTAL 24 19 600

SEMESTER - II

Course code

Course Name Hrs/

Week

Credits

Marks

ENG221 COMMUNICATIVE ENGLISH 3 2 100

CS221P DATABASE MANAGEMENT SYSTEM 5 4 100

CS222P OPERATING SYSTEM 5 4 100

MAT221P LINEAR ALGEBRA USING SCILAB 5 4 100

CS223 INTRODUCTION TO DATA SCIENCE 4 4 100

CS224 PROFESSIONAL ETHICS IN COMPUTING 2 2 100

HOL HOLISTIC EDUCATION-II 1 1 50

TOTAL 25 21 650

SEMESTER - III

Course code

Course Name

Hrs/

Week

Credits

Marks

CS331 DATA WAREHOUSING AND MINING 3 4 100

CS332 CYBER LAW 3 3 100

CS333P PYTHON PROGRAMMING 5 4 100

MAT321 STATISTICAL DATA ANALYSIS 3 3 100

CS334P DATA STRUCTURES 5 4 100

CS335 RESEARCH METHODOLOGY 3 3 100

HOL HOLISTIC EDUCATION-III 1 1 50

TOTAL 23 21 650

SEMESTER - IV

Course code

Course Name

Hrs/

Week

Credits

Marks

CS431P INTRODUCTION TO NOSQL 5 4 100

CS432 PROBABILITY AND QUEUING THEORY 3 3 100

MAT431 DIFFERENTIAL CALCULUS USING MAXIMA 5 4 100

CS433P ADVANCED PYTHON PROGRAMMING 5 4 100

CS434 DATA COMMUNICATION 3 3 100

CS435 SOFTWARE ENGINEERING 3 3 100

HOL HOLISTIC EDUCATION-IV 1 1 50

TOTAL 19 22 650

SEMESTER - V

Course code

Course Name

Hrs/

Week

Credits

Marks

CS531 ARTIFICIAL INTELLIGENCE 3 3 100

CS532P MACHINE LEARNING- USING R 5 4 100

CS533P BIG DATA AND CLOUD COMPUTING USING HADOOP 5 4 100

CS534 INTERNET OF THINGS 3 3 100

CS535E ELECTIVE – 1 3 3 100

CS561 GERMAN LANGUAGE-I 3 3 100

CS581 PROJECT PHASE - I 4 4 100

TOTAL 23 24 600

SEMESTER - VI

Course code

Course Name

Hrs/

Week

Credits

Marks



CS631 PROJECT MANAGEMENT 3 3 100

CS661 GERMAN LANGUAGE-II 3 3 100

CS681 PROJECT PHASE - II 15 9 300

TOTAL 24 21 600

ELECTIVE – 1

Course code

Course Name

Hrs/

Week

Credits

Marks

CS535E01 SOFTWARE QUALITY MANAGEMENT 3 3 100

CS535E02 SOFTWARE TESTING 3 3 100

ELECTIVE – 2

Course code

Course Name

Hrs/

Week

Credits

Marks

CS632E01 ECONOMETRICS 3 3 100

CS632E02 E COMMERCE 3 3 100

ELECTIVE – 3

Course code

Course Name

Hrs/

Week

Credits

Marks

CS633E01 TENSORFLOW FOR DEEP LEARNING RESEARCH 3 3 100

CS633E02 VISUALIZATION TECHNIQUES-TABLEAU 3 3 100

ENG111 PROFESSIONAL ENGLISH- I

Total Teaching Hours for Semester: 45 No of Lecture Hours/Week: 3

Max Marks: 100 Credits: 2

Course Objectives/Course Description

Introduce learners to language skills in their area of specialisation

Enable them to enhance career prospects and employability through English language skills

Help students gain understanding of language at the work place

To develop verbal and non-verbal skills in English communication

Learning Outcome

Comprehension and demonstration of language in the field of technology

Prepare individuals as independent communicators

Illustrate professional requirements through language proficiency

UNIT 1 Teaching Hours: 8

Reviewing Grammar

The unit undertakes to revise the foundation of grammar in context to language learning.

Task based activities will be implemented


Technical vocabulary

Learners will be acquainted with the basics of English language. They will be taught to

identify technical vocabulary from the general.


Rereading texts

Students will be introduced to the basic receptive skills. They will be mentored on mastering

the skill essential for professional development


Non-verbal communication

The ancillary of communication is dealt with here. The non -verbal and paralingual aspects

of communication will be taught through practical sessions.


Communication Strategies

An introduction to LSRW skills. The dos and place with reference to communication will be

learners


Writing skills

Various forms of written communication in an official context will be brought to the students

through case studies.·

UNIT-7 Teaching Hours: 8 Professional

Communication

Typical work place scenarios of group discussions, meetings and negotiations will be taught

to the learners'

Text Books and Reference Books:

Driscoll,Liz. Common Mistakes at Intermediate and How to avoid them. CUP, 2008.

Cater,Ronald and Michael Mc Carthy. Cambridge Grammar of English. CUP, 2006.

Essential Reading / Recommended Reading

Leech, Geoffrey and Jan Svartvik. A Communicative Grammar of English. Third edition.

New Delhi. Pearson educatikn, 20019.

Booher Dianna. E-Writing: Twenty-first century tools for effective communication.

Macmillan, 2008

CS151 OFFICE AUTOMATION TOOLS LAB


Max Marks: 50 Credit: 1


MS-WORD

The purpose of this course is to teach students to identify word processing

terminology and concepts, Create technical documents, Animation and

Design document, format and edit documents, use simple tools and utilities,

Mail merge concepts and Mathematical expressions.

MS-EXCEL

This course will teach you the skills you'll need to successfully use Excel.

This course will start with basic skills, and then move forward to more

advanced features and techniques.

Learning Outcome

Ability to Animate and Design the document.

Simplification of Mathematical expressions.

Create Format cells, rows, columns, and entire worksheets.

Create charts and diagrams for data.

Create data lists and forms.

Create and use pivot tables and pivot charts.

Work with VBA concept.


MS-WORD

1. Create and Design Admission/Enquiry Forms in Microsoft Word.

2. Create a mail to ‘n’ number of contacts from label and send mail to ‘n’ number of

contacts selected from label using mail merge.

3. Prepare a document about any topic in mathematics which uses mathematical

symbols.

At least 5 mathematical symbols should be used.

Assign a password for the document to protect it from unauthorized access.

Demonstrate the use of Hyperlink Option.

Write a macro that sets margins to your document, a font of size and double spaced

document.

4. Create and Design Seminar/Conference/Workshop brochure.


MS-EXCEL

1. Enter the order id, product name, unit price, quantity and discount. Perform the

following operation using MS – Excel.

a. Calculate the revenue and tax on the revenue for each product

b. Calculate the net come of each product

c. Calculate the total revenue of all products

d. Calculate the total net income of all products

e. Count the number of products in the list above

f. Count the number of products of Order ID <<X>>

g. Calculate the total net income of products of Order <<X>>

2. Enter the following details of 20 students data’s in column wise, s.no, roll no,

name, test – 1, test – 2 and test – 3 marks, total, mention and grade from

Cell A to h and do the following operations in excel

a. Calculate the total score of each student

b. Display the word "Fail" if the student failed and "Pass" if the student passed

in Mention column.

c. Students are considered failed if their total is less than 30. Otherwise, they

pass.

d. Count the number of students who failed in subject wise

e. Count the number of students who passed in a subject in the column "# of

passed students.

f. Calculate the percentage of students who failed in all subjects and write

"% of failed students"

g. Calculate the percentage of students who passed in all subjects write "% of

passed students"

Format the cell in percentage <="80--"> <="75--"> <="70--"> <="65--">

<="55--"> <="50--"> <="45--"> <="40--">

h. Display grade letter of each student in Grade column, based on the

following conditions

75 <total Score <=80 A

70 <total Score <=85 B+

65 <total Score <=70 B

55 <total Score <=65 C+

50 <total Score <=55 C

45 <total Score <=50 D+

40 <total Score <=45 D

35 <total Score <=40 E+

30 <total Score <=35 E

Total Score < 30 F

3. Create a basic calculator with VBA in Excel.

4. Write some code in VBA (Visual Basic for Application) to manipulate records

in Excel spreadsheet and work with VBA user form to build graphic user interface

application. In case that you have a lot of records in your data sheets,

manipulating records--add new, update, save, delete, move, and find record is

hard. With VBA, you can solve this problem.

5. Prepare a pay-bill using a worksheet. The work sheet should contain

Employee Id, Name, Designation, Experience and Basic Salary and Job ID.

If Job Id is 1 then DA is 45% of the basic salary. HRA is Rs. 5500.

If Job Id is 2 then DA is 40% of the basic salary. HRA is Rs. 4500.

For all the other Job ids DA is 35% of the basic salary and HRA is Rs.

3500.

For all the above Job ids PF to be deducted is 4%.

For the job ids 1&2 Rs. 100 to be deducted as Professional Tax.

a. Find the net pay.

b. Use filter to display the details of employees whose salary is greater

than 10,000.

c. Sort th e employees on the basis of their net pay.

Use advance filter to display the details of employees whose

designation is Programmer and Net Pay is greater than 20,000

with experience greater than 2 yrs.

6. Using Excel project the Product sales for any five products for five years.

a. Compute the total sales of each product in the five years.

b. Compute the total sales of all the products in five year.

c. Compute the total sales of all products for each year.

d. Represent annual sale of all the products using Pie-Chart

e. Represent annual sales of all products using Bar Chart.

f. Represent sale of a product for five years using Pie-Chart.

g. Label and format the graphs.

Evaluation Pattern

CIA-50 Marks

ESE-50 Marks

CS121 DIGITAL COMPUTER FUNDAMENTALS

Total Teaching Hours for Semester: 60 No of Lecture Hours/Week:4

Max Marks: 100 Credit: 4


This is an introductory course that provides required knowledge about digital fundamentals

of computer.

The course covers few topics like number systems, logic gates and flips flops.

The course starts with an introduction to number systems and its applications in

computers.

The discussion about working of devices like encoders and decoders, multiplexers and de

multiplexers are dealt with.

Learning Outcome

Ability to use math and Boolean algebra in performing computations in various number

systems.

Simplification of Boolean algebraic expressions.

Ability to design efficient combinational and sequential logic circuit implementations from

functional description of digital systems.


Introduction to Number System and Codes

Number systems: Decimal numbers , Binary numbers : Counting in binary, The weighted

structure of binary numbers, Octal numbers, hexadecimal numbers and their mutual

conversions ,Binary arithmetic : Addition, subtraction, multiplication and division of binary

numbers, 1‘s and 2‘s complement, signed numbers, arithmetic operations: addition,

subtraction with signed numbers, 9‘s and 10‘s complement, BCD numbers, BCD addition,

BCD subtraction, Gray code: Binary to Gray code conversion, Gray to Binary conversion,

Weighted code : 8421 code and Non weighted codes : ASCII and EBCDIC.


Boolean Algebra

Boolean operations and expressions, Laws and rules of boolean algebra, Demorgan‘s

Theorem, Boolean expressions, Simplification of Boolean expression.


Logic Gates

AND gate, OR gate, NOT gate , NAND gate , NOR gate , X-OR gate ,

X-NOR gate, The universal property of NAND gate and NOR gate, Realization of basic

gates. Boolean expression for logic circuits, Karnaugh map SOP with examples.

Self-Learning:

Universal property of NOR gate


Combinational Logic

Basic Adders : Half adder, Full adder, 4-bit Parallel adders, Subtractor : Half subtractor, Full

subtractor Implementation using logic gates, Decoders: 4 bit decoder, BCD to decimal

decoder, Encoder : Decimal to BCD encoder, Multiplexer : 4 to 1 multiplexer, Demultiplexer

: 1 to 4 demultiplexer.


Flip-flops

Latches : SR latch, Clocked flip-flops :SR flip-flop, D flip-flop, JK flipflop, Positive edge

triggered flip flops, Timing diagrams , Master slave JK flip-flop.


Registers and Counters

Modes of operation of registers: SISO, SIPO, PISO, and PIPO, Asynchronous counters:

Four bit ripple counter, Decade counter, Synchronous counters: Four bit synchronous

counter, Decade counter.


TEXT BOOK

Floyd, Thomas L: Digital Computer Fundamentals, 11th Edition, Pearson International,

2015.

REFERENCE BOOKS:

Malvino, Paul Albert , Leach, Donald P,GautamSaha: Digital Principles And

Applications, TMH ,8th Edition, 2015.

Bartee, Thomas C: Digital Computer Fundamentals, 6Edition,TMH, 2010.

CS122P INTRODUCTION TO PROGRAMMING USING C

Total Teaching Hours for Semester: 75 No of Lecture Hours/Week: 3+2L



The course provides students with a comprehensive study of C programming language. The

course lectures stress the strengths of C, which provides the outcome of writing efficient,

maintainable and portable code. Course includes few lab exercises to make sure the student

has not only gained the knowledge but can also apply and execute it.

Objectives of the course are

To study about algorithms, flowcharts and programs

To solve problems through logical thinking.

Learning Outcome

To clearly understand the logic of the problem.

To analyze the given problem and write the algorithm, flowchart.

To write structured C programs, this is the foundation of any programming language.


Introduction to Computers and Programming

Evolution of Computers, Generation of Computers, Classification of Computers.

Characteristics of Computers. Advantages of Computers. Block Diagram of a Digital

Computer. Types of Programming Languages. Structured Programming. Algorithms and

Flowcharts with Examples. Programming Logic.


Introduction to C

History of C- Character set - Structure of a C program constants, variables and keywords.

Expressions – Statements – Operators – Arithmetic, Unary, Relational and logical,

Assignment, Conditional. Library functions. Data Input and output – Single character input,

getchar, getch, getc – Single character output putchar, putc, Formatted I/O scanf, printf, gets,

puts.


Control Structures and Array

Branching: condition: if, if..else, switch. Looping: while, do..while, for, nested control

structures, break, continue statement, goto statement. Arrays: definition, processing, types -

One and Two dimensional arrays. String, string operations, arrays of strings.


Functions and Pointers

Functions: Definition, Accessing and prototyping, types of functions, passing arguments to

functions, recursion, passing arrays to functions. Pointers: Definition, notation, applications,

call by reference.


STRUCTURES, UNIONS AND FILES

Structure: Definition, Processing, user defined data type (typedef) - Unions – definition,

declaration and accessing union elements. Enumerated Data type. Files: File opening in

different modes, closing, reading and writing. (fopen, fclose, fprintf, fscanf, getw, putw.

UNIT 6 Teaching Hours:7

Low Level Programming and C Preprocessor

Storage Structures: extern, register, static, auto. Bitwise Operations: AND, OR, exclusive

OR, complement, right shift and left shift operators. Preprocessor: Types of C preprocessor

directives. Macros- comparison with functions. File Inclusion. Command line Arguments.


TEXT BOOK:

Byron Gottfried, Jitender Chhabra: Programming with C, 3rd Edition. Tata McGraw-

Hill, 2010. 1.

REFERENCE BOOKS

Balagurusamy, E. Programming in ANSI C, 4th Edition,Tata McGraw-Hill,2007

Deitel H M and Deitel P J, C - How to Program, 5th Edition, Prentice-Hall,2006.

Smarajit Ghosh, All of 'C', 2nd Edition, 2009.

M. T. Somashekara, Problem Solving with C, PHI, 2009.

C PROGRAMMING LAB

Total Teaching Hours for Semester:30 No of Lecture Hours/Week:2


To learn problem solving through procedural language programming technique and

Understand fundamentals of programming such as variables, conditional and iterative

execution, methods, etc.

Learning Outcome

Read, understand and trace the execution of programs written in C language.

Write the C code for a given algorithm.

Implement Programs with pointers and arrays, perform pointer arithmetic, and use the pre-

processor.

List of Programs

1. To demonstrate the usage of operators and data types in C

a) Write a program to print the size of all the data types with its modifiers supported

by C and its range.

b) Write a program to convert Fahrenheit to Celsius.

2. To demonstrate the usage of if, if-else

a) Write a program to check whether the given number is a Prime number or not.

b) Write a program to accept three numbers and find the largest and second largest

among them.

3. To demonstrate the concept of while, do-while, for loops, break and continue

a) Write a program to print all prime numbers between any 2 given limits.

b) Write a program to print all the Armstrong numbers between any 2 given limits.

4. To demonstrate the concept of arrays and strings

a) Write a program to check whether a string is a Palindrome.

b) Write a program to check whether a given matrix is an Identity matrix or not.

c) Write a program to perform matrix multiplication.

5. To demonstrate the concept of switch-case

a) Write a program to count the different vowels in a line of text.

b) Write a program to accept two numbers and perform various arithmetic operations

(+, -, *, /) based on the symbol entered.

6. To demonstrate the usage of functions and recursion

a) Write a program to find the roots of a quadratic equation

b) Write a recursive program to find the factorial of a number.

7. To demonstrate the concept of structures and unions

a) Create an employee structure and display the same.

b) Create a student database storing the roll no, name, class etc. Implement modify

and search operations.

8. To demonstrate the concept of

a) Write a function to swap two numbers using pointers

b) Write a program to access an array of integers using pointers

9. To demonstrate the concept of File

a) Create a file and store some records in it. Display the contents of the same.

Implement search, modify, and delete operations.

10. To demonstrate the concept of Bitwise operators and preprocessors

a) Perform the different bitwise operations (menu driven program) .The i/p and the

o/p should be displayed in Binary form.

b) Write a program to include your own header file.

Text Books and Reference Books

1. Byron Gottfried, JitenderChhabra ,Programming with C, 3rd Edition. Tata McGraw-Hill, 2010


1.Balagurusamy E., Programming in ANSI C, 6thEdition,Tata McGraw-Hill,2012.

2.Deitel H M and Deitel P J, C - How to Program, 5thEdition, Prentice-Hall, 2006.

3. SmarajitGhosh, All of ‘C’,2ndEdition, 2009.

4. M. T. Somashekara, Problem Solving with C, PHI, 2009

Evaluation Pattern

CIA weightage 50%

ESE weightage 50%

MAT131 BASIC DISCRETE MATHEMATICS

Total Teaching Hours for Semester: 45 No of Lecture Hours/Week:3

Max Marks: 100 Credits:3

Course Objectives/Course

Description

This course aims at introducing the students into the world of Discrete Mathematics. It

includes the topic like Set Theory, Functions and Relations. They gain a historical

perspective of the development of modern discrete mathematics and application of the same

in the field of Computer Science.

Learning Outcome

Demonstrate a working knowledge of set notation and elementary set theory, recognize the

connection between set operations and logic

Prove elementary results involving sets

Apply the different properties of injections, surjections, bijections, compositions, and inverse

functions

Demonstrate the use of mathematical reasoning by justifying and generalizing patterns and

relations

Determine when a relation is reflexive, symmetric, antisymmetric, or transitive, apply the

properties of equivalence relations and partial orderings, and explain the connection between

equivalence relations

UNIT-1 Teaching Hours:15

Set Theory and Theory of Functions Sets, Set Operations, Functions


Applications of Functions and Theory of Matrices

Sequences and Summations, Cardinality of Sets, Matrices


Relations and Their Properties, Equivalence Relations, Partial Orderings


TEXT BOOKS

K. H. Rosen, Discrete Mathematics and its Applications, 7th ed., McGraw – Hill, 2012.

REFERENCE BOOKS:

R.P. Grimaldi and B.V. Ramana, Discrete and Combinatorial Mathematics, An applied

introduction, 5th ed., Pearson Education, 2007.

D. S. Chandrasekharaiah, Discrete Mathematical Structures, 4th ed., India: PRISM Book

Pvt. Ltd., 2012

J. P. Tremblay and R. Manohar, Discrete Mathematical Structures with Application to

Computer Science, Reprint, India: Tata McGraw Hill Education, 2008.

MA141 PRINCIPLES OF MANAGEMENT



Course Objective:

The objective is to provide an understanding of basic concepts, principles and practices of

management. The aim is to inculcate the ability to apply multifunctional approach to

organizational objectives.

UNIT I Teaching Hours: 9

Introduction

Concept, Significance and Nature of Management, Management Process, Management

and Administration, Functions and Principles of Management, Levels of Management,

Functional areas of Management.

UNIT II Teaching Hours: 9

Planning and Decision Making

Concept and Nature of planning, Objectives and Components of planning, Nature and

Process of planning. Process of Planning, Dimensions / Types of Planning, Tools and

Techniques of planning. DecisionMaking –Nature, Significance and Process,Techniques of

decision making.

UNIT III Teaching Hours: 9

Organizing

Concept, Importance and Elements of Organization, Process and Principles of

organization, Theories of Organization, Organization structure, Organization charts and

manuals.

UNIT IV Teaching Hours: 9

Directing and Communication

Concept, Nature, Scope, Principles and Techniques of direction, Concept and Process of

communication, Channel / Media of communication, Barriers to effective communication.

UNIT V Teaching Hours: 9

Controlling

Concept, Objectives, Process and Principles of control, various control techniques

Text and Reference Books:

Koontz & Weirich, Essentials of Management, Tata McGraw Hill, 2010.

L.M. Prasad, Principles & Practices of Management, Sultan Chand, 2010.

Stephen Robbins, Management, Pearson, 2011.

ENG221 COMMUNICATIVE ENGLISH




Nurture an enquiring spirit through English language in technical communication

Enhance English learning for professional communication

Learning Outcome

Demonstrate better comprehension and interpretation of technical English

Initiate rudimentary research aptitude through language upgradation


Pronunciation

Phonetics, Most commonly committed grammar errors and Commonly mispronounced

words will be dealt.


Technical

Literature

Issuing of instructions, procedural instructions, commonly committed errors and glitches

and comprehensive questioning of procedural instructions will be handled.


Research orientation

Structure of essay, topic sentence recognition, thesis statement identification, data analysis

and project proposal writing will be taught


Analytical study

Rhetoric and critical analysis and paraphrasing will be acquainted to the learners through

collaborative learning methods


Official Correspondence

Official letters, internet correspondence and resume writing will be covered in this chapter


Speaking skill

Presentations, conferences and interviews will be imparted through practical sessions


TEXT BOOKS:

Raman, Meenakshi and Sangeetha Raman. Technical Communication, Principles and

Practise. Oxford University Press.

Day, R.A. Scientific English: a guide for scientists and other professionals. Hyderabad

University Press, 2000.

REFERENCE BOOKS:

Jay. Effective Presentations. Pearsons, 2010.

English for Effective Communication. Oxford University Press. 2013.

CS221P DATABASE MANAGEMENT SYSTEM



Course Objectives/Course Description:

This course concentrates on introduction, principles, design and implementation of DBMS.

It introduces about the distributed system and brief about data mining and data

warehouse.

Objective of the course is:

To provide strong foundation of database concepts and develop skills for the design and

implementation of a database application with a brief exposure to advanced database

concepts.

Learning Outcome

Understanding the core terms, concepts, and tools of relational database management

systems.

Understanding database design and logic development for database programming.


Introduction & DBMS Architecture

Introduction- Data, Database, Database management system, Characteristics of the database

approach, Role of Database administrators, Role of Database Designers, End Users,

Advantages of Using a DBMS and When not to use a DBMS.

DBMS Architecture – Data Models – Categories of Data models, Schemas, Instance, and

Database states, DBMS Architecture and Data Independence – The Three schema

architecture, Data Independence. DBMS language and interface, Classifications of Database

Management Systems.


Data Modelling Using Entity-Relationship Model

Using high level conceptual Data models for Database Design, Example Database

applications. Entity types, Entity Sets, Attributes and Keys. Relationships, Relationship

types, Roles and Structural constraints. Weak Entity Types and Drawing E- R Diagrams.


Database Design

Functional dependencies and Normalization for Relational Databases - Normalizati

on concepts, first, second, third normal forms.


SQL

SQL data definition and data types, specifying constraints in SQL, schema change

statements, Basic queries, INSERT, DELETE and UPDATE statements in SQL, Views

– Concept of a view in SQL.


Transaction Processing Concepts and Concurrency Control

Transaction and System concepts – Desirable properties of Transactions – Schedules and

Recoverability. Lock-Based Protocols – Locks, Granting of Locks, and Two phase locking

protocol.


Distributed Databases

Distributed database concepts, Data fragmentation, Replication, and Allocation Techniques

for Distributed database design, Types of Distributed database systems.


TEXT BOOK:

ElmasriRamez and NavatheShamkant B, Fundamentals of Database Systems, Addison-

Wesley, 6th Edition, 2010.

REFERENCE BOOKS:

Silberschatz, Korth, Sudarshan, Database System Concepts, 5 Edition, McGraw Hill, 2006.

O`neil Patricand, O`neil Elizabeth, Database Principles, Programming and Performance,

2nd Edition, Margon Kaufmann Publishers Inc, 2008.

DATABASE MANAGEMENT SYSTEM LAB



Provides the hands on the SQL language for retrieving the data from the database in different

scenarios. Theprimaryfocusistounderstandrelationaldatabaseconceptsanddesignby using SQL.

Learning Outcome

Upon successful completion of the course students will be able to

• Design and implement programming logic for a relational database.

• Manipulate data stored in an Oracle DBMS using Oracle SQL.

Oracle

1. SQL*Plus and SQL Teaching Hours: 4

a) Introduction

b) Logging on to SQL*Plus and Leaving SQL*Plus

c) Choosing and Describing Tables

d) Elements of the SQL Query

e) Editing SQL Statements

f) The System Dummy Table g. Selecting Columns

g) Duplicate Information (DISTINCT)

h) Sorting Information

2. SQL Functions Teaching Hours: 4

a) The Concatenation Operator

b) Column Aliases

c) String Functions

d) Arithmetic Functions

e) Date Functions

3. Advanced SQL Functions Teaching Hours: 2

a) Select with Minus, Union and Intersect

b) Handling NULL

4. Filtering Data Using Where Teaching Hours: 2

a) Where Operators

b) Where with Keywords and Logical Operators

5. Group By and Group By Teaching Hours: 2

a) Group Function Examples

b) Group Function with Having

6. Data Definition Language (DDL) Teaching Hours: 4

a) Create, Drop Alter

b) Tables

c) Column

d) Views

e) Object

f) Alter table

7. Data Manipulation Language (DML) Teaching Hours: 2

a) Insert,

b) Update

c) Delete

8. Integrity Constraints Teaching Hours: 2

a) Types of constraint

b) Referential Integrity

c) Defining Constraints

9. Retrieving Data from Multiple Tables Teaching Hours: 4

a) Joining Tables (Equi-Joins, Non-Equi-Joins)

b) Aliases for Table Names

10. Sub-Queries Teaching Hours: 4

a) Basic Sub queries

b) Multiple Column sub queries

c) Sub queries with Having


[1] Kevin Loney, Oracle Database 11g The Complete Reference, Oracle Press, McGraw Hill

Professional, 2008.


[1] Steven Feuerstein, Bill Pribyl , Oracle PL/SQL Programming, 6th Edition, Paperback, 2014.

Evaluation Pattern

50% - CIA

50% - End Semester

CS222P OPERATING SYSTEM




This course is an introduction to the concepts behind modern computer operating systems.

Topics will include what an operating system does (and doesn't) do, system calls and

interfaces, processes, resource scheduling and management (of the CPU, memory, etc.),

virtual memory. Objectives of the course are

To acquire the fundamental knowledge of the operating system architecture and its

components

To know the various operations performed by the operating system.

Learning Outcome

Upon completion of the course students will be able to:

Understand the basic working process of an operating system.

Understand the importance of process and scheduling.

Understand the issues in synchronization and memory management.

UNIT-1 Teaching Hours: 9

Introduction and System Structures

Operating System Fundamentals; Computer System organization and architecture;

Operating System structure and operations; Basics of process, memory and storage

management and protection and security; Operating System services; User interface; System

calls; System programs; Operating System structure; System boot.


Process Management

Process concept; Process scheduling; Operations on processes; Inter Process

Communication; Overview of Threads; Multi-threading models; Threading issues


Process Synchronization

Need of synchronization; Critical section problems; Peterson‘s solution; Synchronization

hardware; Mutex Locks; Semaphores, Classical problems of synchronization,

Synchronization examples, Thread synchronization using mutex and semaphore.


Scheduling

CPU Scheduling CPU Scheduling concepts; Scheduling criteria; Scheduling algorithms;

Overview of thread scheduling; Multi-processor scheduling


Memory Management

Memory Management Overview; Swapping; Memory allocation; Segmentation; Paging,

Structure of the page table Unit-6 Teaching Hours:10 Virtual Memory Overview; Demand

paging; Copy on Write; Page replacement; Allocation of Frames; Thrashing

Self Learning File system structure, Directory structure


Silberschatz, P.B. Galvin and G. Gagne, Operating System Concepts.9th Edition, New

Delhi: Wiley India, 2011.


Stalling William, Operating Systems: Internals and Design Principles. 7th Edition, Prentice

Hall, 2011.

Dietel et al, Operating System.3rd Edition. Pearson Education, 2004. [3] A.S. Tanenbaum,

Modern Operating Systems.3rd Ed, Prentice Hall, 2007.

OPERATING SYSTEM LAB



This lab introduces basic commands in LINUX and helps students in familiarizing the

concepts of operating system through various commands related to operating system

activities.

Learning Outcome

To make students able to implement various LINUX commands.

Students will also be able to implement different process related commands.

1. To study the execution of various file/directory handling commands.

2. To study the various commands operated in vi editor in LINUX.

3. To study the various File Access Permission and different types of users in LINUX

4. To study about process related commands.

5. To study about the commands related to memory allocation of variables for a process.

6. To study about commands for viewing system calls.

7. To study about commands used for debugging.

8. Write a program to demonstrate basic operations of a process.

9. Write a program to create a Zombie process and an orphan.

10. Write a program to demonstrate a one-way pipe between two processes.

11. Write a program to illustrate a two way pipe between two processes.

12. Write a program to demonstrate a one-way communication between two processes using FIFO

13. Write a program to demonstrate a two-way communication between two processes using FIFO

14. Demonstrate process synchronization using semaphore.

15. Demonstrate the basic operations of thread.

16. Demonstrate thread synchronization using mutex.

17. Demonstrate thread synchronization using semaphore

Evaluation Pattern

50% - CIA

50% - End Semester

MAT221P LINEAR ALGEBRA USING SCILAB




This course aims at providing hands on experience in using Scilab functions to illustrate

the notions vector space, linear independence, linear dependence, linear transformation and

rank.

Learning Outcome

On successful completion of the course, the students should be able to demonstrate

sufficient skills in using Scilab functions in the applying the notions of Vector space and

Linear transformations.


Vector Spaces

Vector space-Examples and Properties, Subspaces-criterion for a subset to be a subspace,

linear span of a set, linear combination, linear independent and dependent subsets, Basis

and dimensions, Standard properties, Examples illustrating concepts and results.


Linear Transformations

Linear transformations, properties, matrix of a linear transformation, change of basis,

range and kernel, rank and nullity, Rank, Nullity theorem


Inner product spaces

Inner product spaces and norms, Gram-Schmidt orthogonalisation process, orthogonal

complements, Bessel’s inequality, the adjoint of a linear operator, Least Squares

Approximation, minimal solutions to systems of linear equations, Normal and self-adjoint

operators, Orthogonal projections and Spectral theorem.


S. H. Friedberg, A. Insel, and L. Spence, Linear algebra, 4th ed., Pearson, 2015.

V. Krishnamurthy, V. P. Mainra, and J. L. Arora, An introduction to linear algebra.

New Delhi, India: Affiliated East East-West Press Pvt Ltd., 2003.

K. Hoffmann and R. A. Kunze, Linear algebra, 2nd ed., PHI Learning, 2014.


David C. Lay, Linear Algebra and its Applications, 3rd ed.-Indian Reprint, Pearson

Education Asia, 2007.

S. Lang, Introduction to Linear Algebra, 2nd ed., New York: Springer-Verlag,

2005.

Gilbert Strang, Linear Algebra and its Applications, 4th ed., Thomson Brooks/Cole,

2007.


Proposed Topics:

Vector space, subspace – illustrative examples

Expressing a vector as a linear combination of given set of vectors.

Linear Span, Linear Independence and Linear dependence.

Linear Transformation and Rank.

Verifying whether a given transformation is linear.

Finding matrix of a linear transformation.

Problems on rank and nullity.

Gram-Schmidt orthogonalization process


TEXT BOOKS:

C. Gomez, Engineering and Scientific Computing with Scilab, Birkhuser Boston, 2000.

REFERENCE BOOKS:

M. Affouf, Scilab by Example, CreateSpace Independent Publishing Platform, 2012.

VandeWouwer, P. Saucez and C. Vilas, Simulation of ODE/PDE Models with MATLAB,

OCTAVE and SCILAB: Scientific and Engineering Applications, Springer, 2014.

J. Russell and R. Cohn, Scilab, Book on Demand, 2012.

CS223 - INTRODUCTION TO DATA SCIENCE




To understand the basic concepts of Data science

Classification and clustering process

Data Visualization Techniques

Learning Outcome

Ability to analyse the data and carry out supervised, un-supervised Learning

processes

Ability implement Data Visualization Techniques

Ability to do regression, correlation and knowledge discovery of the data

Learning Outcome

CO1:Explore the fundamental concepts of data science

CO2:Understand data analysis techniques for applications handling large data

UNIT-1 Teaching Hours: 9+3

Introduction and Data Pre-processing

Why Data Mining?, What Is Data Mining?, What Kinds of Data Can Be Mined?, What Kinds

of Patterns Can Be Mined?, Which Technologies Are Used? Which Kinds of Applications

Are Targeted?, Major Issues in Data Mining, Data Pre-processing: An Overview, Data

Cleaning, Data Integration, Data Reduction, Data Transformation and Data Discretization


Mining Frequent Patterns, Associations, and Correlations: Basic Concepts and

Methods

Basic Concepts, Frequent Itemset Mining Methods, Which Patterns Are Interesting?—

Pattern 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


Classification

Basic Concepts, Decision Tree Induction, Bayes Classification Methods, Rule-Based

Classification, Model Evaluation and Selection, Techniques to Improve Classification

Accuracy, Support Vector Machines, Lazy Learners (or Learning from Your Neighbors)


Cluster Analysis : Basic Concept and Methods

Cluster Analysis, Partitioning Methods, Hierarchical Methods, Density-Based Methods,

Grid-Based Methods, Evaluation of Clustering, Clustering High-Dimensional Data,

Clustering Graph and Network Data


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


Text Book:

Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Third

Edition, Morgan Kaufmann, 2011.

Reference Books:

Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introductionto Data Mining”,

Person Education, 2007.

K.P. Soman, Shyam Diwakar and V. Ajay “, Insight into Data mining Theory and

Practice”, Easter Economy Edition, Prentice Hall of India, 2016.

Gupta, “ Introduction to Data Mining with Case Studies”, Easter EconomyEdition,

Prentice Hall of India, 2006.


IEEE Resources on Data Science

Coursera Machine learning courses

CS224 PROFESSIONAL ETHICS IN COMPUTING



Course Objectives

To educate existing and future business managers and IT professionals on the tremendous impact

ethical issues play in the use of information technology in the modern business world


Introduction to Computer Ethics

An overview of Ethics


Ethics for IT Professionals and Users

Ethics for IT Workers and IT Users


Computer and Internet Crime

Privacy-Freedom of Expression- Intellectual Property


Software Development

The impact of Information Technology on productivity and quality of life- Social Networking


Ethics in IT Organizations

A brief introduction to morality

Text Books:

1. George Reynolds, Ethics in Information Technology, Thomson Course Technology, 2007.

CS331- DATA WAREHOUSING AND MINING



Course Description

This course is an introductory course on data mining. It introduces the basic concepts,

principles, methods, implementation techniques, and applications of data mining, with a

focus on two major data mining functions:

Pattern discovery

Cluster analysis.

Course Goals and Objectives

Recall important pattern discovery concepts, methods, and applications, in particular, the

basic concepts of pattern discovery, such as frequent pattern, closed pattern, max-pattern,

and association rules.

Recall basic concepts, methods, and applications of cluster analysis, including the concept

of clustering, the requirements and challenges of cluster analysis, a multi-dimensional

categorization of cluster analysis, and an overview of typical clustering methodologies.

UNIT 1 Teaching Hrs. 9

Introduction

Why Data Mining?, What Is Data Mining? What Kinds of Data Can Be Mined?, What Kinds

of Patterns Can Be Mined?, Which Technologies Are Used?, Which Kinds of Applications

Are Targeted?, Major Issues in Data Mining

Getting to Know Your Data: Data Objects and Attribute Types, Basic Statistical Descriptions

of Data, Data Visualization, Measuring Data Similarity and Dissimilarity


Data Pre-processing

Data Pre-processing: An Overview, Data Integration, Data Reduction, Data Transformation

and Data Discretization


Data Warehousing and Online Analytical Processing

Data Warehouse: Basic Concepts, Data Warehouse Modeling: Data Cube and OLAP, Data

Warehouse Design and Usage, Data Warehouse Implementation, Data Generalization by

Attribute-Oriented InductionMining Frequent Patterns, Associations, and Correlations:

Basic Concepts and Methods


Classification: Basic Concepts

Decision Tree Induction, Rule-Based Classification, Model Evaluation and Selection,

Techniques to Improve Classification Accuracy Cluster Analysis: Basic Concepts and

Methods, Cluster Analysis

UNIT 5 Teaching Hrs 9

Outlier Detection

Outlier Detection Methods, Statistical Approaches, Clustering-Based Approaches,

Classification-Based Approaches, Outlier Detection in High-Dimensional Data

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


TEXT BOOK:

1. Han, J., Kamber, M., & Pei, J. Data mining: Concepts andtechniques (3rd ed.). Waltham:

Morgan Kaufmann, 2011.

REFERENCE BOOKS

1. Ramez Elmasri and Shamkant B. Navathe, “Fundamental Database Systems”, Third Edition,

Pearson Education, 2008.

2. Raghu Ramakrishnan, “Database Management System”, Tata McGraw-Hill Publishing

Company, 2003.

CS332- CYBER LAW




The course aims at appreciating one of the important area of law closely associated with the

application of computers. There are several areas of law which should be known to anyone

using computers and computer networks, as the ignorance of the same will expose the users

to severe legal consequences.

Learning Outcome

Upon successful completion of the course students will be able to

Exhibit familiarity with the concept of cyber space and its special characteristics, the need

for regulation

Exhibit familiarity with the concept of contracts and the rules governing validity of

contracts and apply the same to electronic contracts

Discuss the differences between unsecure and secure electronic documents and the

provisions of IT Act, 2000 in respect of digital signatures.

Discuss the privacy and taxation issues related to use of computers.

Define crimes and fix criminal liability on the basis of facts of a hypothetical case.


General introduction - Cyber space regulations

Cyber space – Meaning and characteristics – Need for regulation of cyber space – Cyber

libertarianism, Cyber-paternalism, Lessig‘s model of regulation – Regulators in cyberspace

- Introduction to Internet – ACLU v Reno – Digitization and Society, Legal Challenges of

the Information Society – Information Technology Act, 2000.


Law of e-commerce - online contracts

Contracts – Meaning and essential requirements - E-contracts – Application of rules of

contract – Incorporation of terms, Identity of contracting parties, extent of details –

Ecommerce directives and Regulations


Law of e-commerce - Digital Signatures

Provisions under IT Act, Certifying authorities, Issuing authorities, PKI, Electronic

Signature Certificate, Grant, Revocation and withdrawal of ESC


Cyber law - IPR issues

Digital Copyrights, Open Source – Linking and caching – Digital Rights Management,

DMCA, - Patents, Software Patents – Trademarks and domain names, Brand identities,

search engines and secondary market, ICANN, Database Right.


Cyber law - privacy and taxations issues

Digitization, personal data and data industry, Data protection principles, Conditions for

processing of personal data, CCTV, RFID tracking, Data retention and identity – Taxation

issues of e-commerce


Cyber-crimes

Computer misuse – identity theft, grooming and harassment, Hacking, Viruses, criminal

damage and mail bombing, Denial of service attack, Obscenity, child abuse, Stalking.

Morphing, webjacking, phishing etc., Cyber terrorism, Bandwidth theft, Convention on

cyber crime


1. Senthil, Surya, and Lakshmi Devi. Manual of Cyber Laws.New Delhi: Aditya Book

Company, 2010.

2. Singh, Ranbir and Ghanshyam Singh. Cyber Space and the Law: Issues and Challenges.

Hyderabad: NALSAR University, 2004.

3. Rowland, Diane, and Elizabeth Macdonald. Information Technology Law, Cavendish

Publishing Ltd, 3rd Edition, 2005.


1. Sharma, Vakul. Information Technology: Law & Practice. 2ndEdition, New Delhi:

UniversalLaw Publishing Co.

2. Singh, Yatindra Justice. Cyber Laws. 3rdEdition, Universal Law Publishing.

3. Jayashankar K. K., and Philip Johnson.Cyber Law. Pacific Books International, 2011.

4. Hiremath, Uma R. Dr.Inofmration Technology and Cyber Crimes. Bangalore: Karnataka

Institute for Law & Parliamentary Reforms, 2009.

5. Price, David, and Korieh Duodu. Defamation: Law Procedure and Practice. 3rd Ed.,

Thomson.

CS333P PYTHON PROGRAMMING



Course Objectives/Course

The major goals of this course are to learn how to use tools for acquiring, cleaning,

analyzing, exploring, and visualizing data; making data-driven inferences and decisions;

and effectively communicating results. These will be accomplished through course

activities on the following data science topics:

Introduction to data analysis tools in Python, Descriptive statistics, Linear

regression

Introductory hypothesis testing and statistical inference ,Web scraping and data

acquisition via APIs

Classification methods, including logistic regression, k-nearest neighbors,

decision trees, and support vector machines, Data visualization, Clustering methods

Dimensionality reduction, including principle component analysis, Network

analysis

Rating, ranking, and elections, Cleaning and reformatting messy datasets using

regular expressions or dedicated tools such as open refine, Ethics of big data

A major component of this course will be learning how to use python-based programming

tools to apply these methods to real-life datasets.

Learning Outcomes

At the end of the course, a student should be able to:

Acquire data through web-scraping and data APIs

Clean and reshape messy datasets

Use exploratory tools such as clustering and visualization tools to analyze data

Perform linear regression analysis

Use methods such as logistic regression, nearest neighbors, decision trees, and

support vector machines to build a classifier

Apply dimensionality reduction tools such as principle component analysis

Perform basic analysis of network data and can evaluate outcomes and make

decisions based on data

UNIT 1 Teaching Hrs: 9

Introduction

What is Data Science? Motivating Hypothetical: DataSciencester, A Crash Course in Python

The Basics, Getting Python, The Zen of Python, Whitespace Formatting, Modules

Arithmetic, Functions, Strings, Exceptions, Lists, The Not-So-Basics, Visualizing Data-

Matplotlib, Bar Charts, Line Charts, Scatterplots, Linear Algebra, Vectors, Matrices.


Statistics

Describing a Single Set of Data, Central Tendencies, Dispersion, Correlation, Simpson’s

Paradox, Some Other Correlational Caveats, Correlation and Causation. Probability:

Dependence and Independence, Conditional Probability.


Bayes’s Theorem, Random Variables, Continuous Distributions, The Normal Distribution,

The Central Limit Theorem. Hypothesis and Inference: Statistical Hypothesis Testing,

Example: Flipping a Coin, Confidence Intervals, P-hacking, Example: Running an A/B Test,

Bayesian Inference.


Gradient Descent: The Idea behind Gradient Descent, Estimating the Gradient, Using the

Gradient, Choosing the Right Step Size, Putting It All Together, Stochastic Gradient Descent

Getting Data: stdin and stdout, Reading Files. Scraping the Web, Using APIs, Example:

Using the Twitter APIs.


Working with Data: Exploring Your Data, Cleaning and Munging, Manipulating Data,

Rescaling, Dimensionality Reduction,

Machine Learning: Modeling, What Is Machine Learning?,Overfitting and Underfitting,

Correctness, The Bias-Variance Trade-off, Feature Extraction and Selection, k-Nearest

Neighbors, The Model, Example: Favorite Languages, The Curse of Dimensionality, Naive

Bayes.

Text Book and Reference Book:

TEXT BOOK:

1. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython ,2nd

edition, Wes McKinney,O’Reilly Media (2017)

REFERENCE BOOK

1. Data Science from Scratch: First Principles with Python, Joel Grus O’Reilly Media

(2015)

PYTHON LAB


Learn to program and programming paradigms brought in by Python with a focus on File

Handling and Regular Expressions

Learning Outcome

Able to walkthrough algorithm

Improve programming skills

Appreciate Python Programming Paradigm

Hands on Regular Expression

Ability to Text Processing scripts

Write to file handling scripts

List of Programs

1. Implement a sequential search

2. Create a calculator program

3. Explore string functions

4. Implement Selection Sort

5. Implement Stack

6. Read and write into a file

7. Demonstrate usage of basic regular expression

8. Demonstrate use of advanced regular expressions for data validation.

9. Demonstrate use of List

10. Demonstrate use of Dictionaries

11. Create Comma Separate Files (CSV), Load CSV files into internal Data Structure

12. Write script to work like a SQL SELECT statement for internal Data Structure made in

earlier exercise

13. Write script to work like a SQL Inner Join for an internal Data Structuremade in earlier

exercise

14. Demonstrate Exceptions in Python


Mark Summerfield, Programming in Python 3 A Complete Introduction to the Python

Language,Addison-Wesely Reprint 2011


Allen Downey, Think Python, Version 2.0.17, Green Tea Press, Needham,

Massachusetts,2012.

MAT321- STATISTICAL DATA ANALYSIS




The course statistics describes the concept of correlation and regression, probability

distribution and testing hypothesis.


To acquaint students with various statistical methods.

To cultivate statistical thinking among students.

To prepare students for future courses having quantitative components.

Learning Outcome

Upon successful completion of the course one should be able to

Understand and analyze bivariate data with respect to their association.

Apply different distributions at the appropriate situations.

Apply various tests of hypothesis understand their interpretation.


Correlation and Regression

Scatter diagram, Karl Pearson’s and Spearman’s correlation coefficient. Regression and

properties of regression coefficient.


Probability Distributions

Discrete and continuous random variables. Probability mass and density functions.

Expectation. Binomial, Poisson and normal distribution.


Sampling distribution and confidence interval

Sampling, distribution and estimation. Parameter and statistic. Chisquare t and F

distributions (definitions only) Confidenceinterval Single mean and difference known and

unknown variances. Single proportion and difference of proportions.


Testing of Hypothesis

Types of hypothesis. Level of significance. Types of errors. Test for single mean and

difference of means. Paired t test. Tests for proportions. Chi square test for independence of

attributes.


TEXT BOOKS:

Berenson and Levine, Basic Business Statistics, New Jersey, Prentice- Hall India, 6th ed.

1996.

SP Gupta, Statistical Methods, Sultan Chand & Sons, new Delhi, 41st Revised Edition, 2011.

REFERENCE BOOKS:

C.Montogomery and G.C.Runger, Applied Statistics and Probability for engineers, New

Jersey, John Wiley and Sons, 3rd ed. 2003.

CS334P DATA STRUCTURES




Data Structure is considered as one of the fundamental paper towards a more comprehensive

understanding of programming and application development. Student is expected to work

towards a sound theoretical understanding of Data Structures and also compliment the same

with hands on implementing experience.


• To be able to practically implement the data structures like stack, queue, array etc.

• To understand and implement different searching and sorting techniques.

Learning Outcome

• Understand the need for Data Structures when building application.

• Appreciate the need for optimized algorithm.

• Able to walk through insert and delete for different data structures.

• Ability to calculate and measure efficiency of code.

• Appreciate some interesting algorithms like Huffman, Quick Sort, and Shortest Path etc.

• Able to walkthrough algorithm.

• Improve programming skills


Arrays Introduction to data structures-

Arrays and Structures: Abstract Data Type, Array in C, Dynamically Allocated Arrays,

Structures, Unions, Internal Implementation of Structures, Self-Referential Structures,

Polynomial Representation, Polynomial Additions, Sparse matrix.


Searching and String

Linear Search, Iterative Binary Search, Recursions, Recursive Binary Search, String

Abstract Data Type, String in C, Pattern Matching.


Stacks and Queues

Stacks- stacks using dynamic arrays- queues – circular queue using dynamic arrays-

Evaluation of Expressions, Evaluating Postfix Expressions, Infix to Postfix.


Linked Lists

Pointers, Using Dynamically Allocated Storage, Singly Linked Lists, Dynamically Linked

Stacks and Queues, Polynomials, Representing Polynomials as Singly Linked Lists,

Adding Polynomials, Erasing Polynomials, Polynomials as Circularly Linked Lists,

Doubly Linked Lists.


Trees

Introduction, Terminology, Representation of Trees, Binary Trees, Abstract Data Type,

Properties of Binary Trees, Binary Tree Representations, Binary Tree Traversals Binary

Search Trees: Introduction, Searching a Binary Search Tree, Inserting an Element,

Deleting an Element, Height of Binary Search Tree.

Sorting Techniques and Graphs : Introduction, Bubble Sort, Insertion Sort, Selection Sort,

Quick Sort, Merge Sort. Graphs—Introduction-Definition-representation-Depth first

search-Breadth first search.


1. Horowitz Sahni Anderson-Freed, Fundamental of Data Structures in C, Universities Press,

Reprint 2009.


1. Yashwant Kanetkar, Data Structures through C, 9th Edition, BPB Publication 2010.

2. Tremblay J.P and Sorenson P.G: An Introduction to Data Structures with Applications, 2nd

Edition, 2002, TMH.

Evaluation Pattern

CIA1 - 10%

CIA2- 25%

CIA3 -10%

Attendance 5%

ESE 50%

DATA STRUCTURES LAB


The course is designed to provide a practical exposure on data structure and its applications.

Learning Outcome

Upon completion of the course, the students acquire the knowledge to build the logic and

develop a solution for a problem statement.

List of programs

1. Strings:

a) Write a menu driven program to compare, concatenate, copy strings and find the length of

a string.

b) Write a menu driven program to find the index of a pattern in a given string and to extract

a substring.

2. Arrays

a) Write a program to insert and delete an element(s) in one dimensional array.

b) Write a program to insert and delete an element(s) in two dimensional arrays.

3. Sparse Matrix

a) Write a menu driven program to read a sparse matrix of integer values and to search the

sparse matrix for any element specified by the user.

b) Write a program to print the appropriately triple < row, column, "value" > that represents

the elements in the sparse matrix.

4. Searching Techniques:

a) Write a program to implement Linear Search with sentinels.

b) Write a program to implement Binary Search using recursion.

5. Sorting techniques:

a) Write a menu driven program to implement insertion sort

b) Write a menu driven program to implement selection sort.

c) Write a menu driven program to implement quick sort using recursion

d) Write a menu driven program to implement merge sort using recursion.

6. Singly linked list:

Write a menu driven program to implement singly linked lists creation, insertion and deletion

7. Stack:

Write a menu driven program to implement different operations on a stack using an array and

linked list.

8. Queue:

Write a menu driven program to implement different operations on a queue using an array and

linked list.

9. Binary search trees:

Write a menu driven program to create a binary search tree and to perform Insertion and

different types of traversal

10. Graphs:

a) Write a menu driven program to implement breadth first search (bfs)

b) Write a menu driven program to implement depth first search (dfs)

CS335 RESEARCH METHODOLOGY




To orient the student to make an informed choice from the large number of alternative

methods and experimental designs available, It enable the student to present a good research

proposal and familiarize the student with the nature of research and scientific writing skills

and they need to undertake a research project, to present a conference COURSE and to write

a scientific article.

Learning Outcome

● Define research and discuss research process and research methods.

● Apply knowledge gained of research process in order to plan and execute a research

project.

● Effectively and efficiently use the library and its resources in gathering information related

to the learners' research project.

● Experiment methods to perform basic operations with Excel spreadsheets and to sketch

graphs and diagrams using Excel and to insert these graphs and diagrams into Word

● Interpret ideas to present a conference COURSE/poster at a national/international level.


Research Methodology

An Introduction Meaning of Research, Objectives of Research, Motivation in Research,

Types of Research, and Research approaches, Research Method versus Methodology,

Research and Scientific Method, Importance of Knowing How Research is Done, Research

Process, Criteria of Good Research, problem Encountered by Researchers in India.

Defining the Research Problem: Definition of Research Problem, Selecting the Problem,

Necessity of Defining the Problem Technique Involved in Defining a Problem.


Measurement and Scaling Technique

Measurement in Research, Measurement Scales, Sources of Error in Measurement, Tests of

Sound Measurement, Technique of Developing Measurement Tools, Scaling, Meaning of

Scaling, Scale Classification Bases, Important Scaling Techniques, Scale Construction

Techniques.


Analysis of algorithm

The role of algorithm in computing –Insertion sort–Analyzing and designing algorithms.


Sampling Fundamentals: Need for Sampling, Some Fundamental Definitions, Central Limit

Theorem, Sampling Theorem, Sandler’s A-test, Concept of Standard Error, Estimation,

Estimating the Population Mean, Estimating the Population Proportion, Sample size and its

Determination, Determination of Sample Size through the Approach, Based on Precision

Rate and Confidence Level, Determination of Sample Size through the Approach, Based on

Bayesian Statistics.


Interpretation and Report Writing

Meaning of Interpretation, Technique of Interpretation: Precaution in Interpretation, Case

study.


TEXT BOOKS

1. Kothari C.R., “Research Methodology – Methods and Techniques”, New Age International,

New Delhi, (reprint 2011).

2. Montgomery, Douglas C., “Design and Analysis of Experiments”, Willey India, 2007.

3. Montgomery, Douglas C. & Runger, George C. “Applied Statistics & Probability for

Engineers”, Wiley India, 2010.

REFERENCE BOOKS

1. Krishnaswamy, K.N. Sivkumar , Appa Iyer and Mathiranjan M., “Management Research

Methodology: Integration of Principles, Method and Techniques”, Pearson Education, New

Delhi, 2006.

2. Charlie Catlett, Wolfgang Gentzsch, Lucio Grandinetti, Gerhard Joubert, and Jose Luis

Vasquez-Poletti, “Cloud computing and Big data”, Published/Distributed: Amsterdam :

Washington, DC : IOS Press, 2013.


Research article will be recommended in the class.

Evaluation Pattern

CIA1 - 10%

CIA2- 25%

CIA3 -10%

Attendance 5%

ESE 50%

CS431P INTRODUCTION TO NO SQL



CourseObjectives/CourseDescription

After successful completion of the course students be able to

Define, compare and use the four types of NoSQL Databases (Document-oriented,

KeyValue Pairs, Column-oriented and Graph).

Demonstrate an understanding of the detailed architecture, define objects, load data, query

data and performance tune Column-oriented NoSQL databases.

Explain the detailed architecture, define objects, load data, query data and performance tune

Document-orients NoSQL databases.


Introduction

Overview, and History of NoSQL Databases Definition of the Four Types of NoSQL

Database, the Value of Relational Databases, Getting at Persistent Data, Concurrency,

Integration, Impedance Mismatch, Application and Integration Databases, Attack of the

Clusters, the Emergence of NoSQL, Key Points comparison of relational databases to new

NoSQL stores, MongoDB, Cassandra, HBASE, Neo4j use and deployment, Application,

RDBMS approach, Challenges NoSQL approach, Key-Value and Document Data Models,

Column-Family Stores, Aggregate-Oriented Databases.


Replication and sharding

Replication and sharding, MapReduce on databases. Distribution Models, Single Server,

Sharding, Master-Slave Replication, Peer-to-Peer Replication, Combining Sharding and

Replication.

NoSQL Key/Value databases using MongoDB, Document Databases, What Is a Document

Database? Features, Consistency, Transactions, Availability, Query Features, Scaling,

Suitable Use Cases, Event Logging, Content Management Systems, Blogging Platforms,

Web Analytics or Real-Time Analytics, E-Commerce Applications, When Not to Use,

Complex Transactions Spanning Different Operations, Queries against Varying Aggregate

Structure.


Column- oriented NoSQL databases using Apache HBASE

Column- oriented NoSQL databases using Apache HBASE, Column-oriented NoSQL

databases using Apache Cassandra, Architecture of HBASE, What Is a Column-Family Data

Store? Features, Consistency, Transactions, Availability, Query Features, Scaling, Suitable

Use Cases, Event Logging, Content Management Systems, Blogging Platforms, Counters,

Expiring Usage, When Not to Use.


NoSQL Key/Value databases using Riak

NoSQL Key/Value databases using Riak, Key-Value Databases, What Is a Key-Value Store,

Key- Value Store Features, Consistency, Transactions, Query Features, Structure of Data,

Scaling, Suitable Use Cases, Storing Session Information, User Profiles, Preferences,

Shopping Cart Data, When Not to Use, Relationships among Data, Multi-operation

Transactions, Query by Data, Operations by Sets.


Graph NoSQL databases using Neo4

Graph NoSQL databases using Neo4, NoSQL database development tools and programming

languages, Graph Databases, What Is a Graph Database? Features, Consistency,

Transactions, Availability, Query Features, Scaling, Suitable Use Cases, Connected Data,

Routing, Dispatch, and Location-Based Services, Recommendation Engines, When Not to

Use.

Practical Content

1. Introduction to MongoDB and its Installation on Windows & Linux

2. Description of mongo Shell, Create database and show database

3. Commands for MongoDB and To study operations in MongoDB – Insert, Query, Update,

Delete and Projection

4. Where Clause equivalent in MongoDB

5. To study operations in MongoDB – AND in MongoDB, OR in MongoDB, Limit Records

and Sort Records. To study operations in MongoDB – Indexing, Advanced Indexing,

Aggregation and Map Reduce.

6. Practice with ' macdonalds ' collection data for document oriented database. Import

restaurants collection and apply some queries to get specified output.

7. Column oriented databases study, queries and practices

Text Books

1. NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence , Author:

Sadalage, P. & Fowler, Publication: Pearson Education

Reference Books

Redmond, E. & Wilson, Author: Seven Databases in Seven Weeks: A Guide to Modern

Databases and the NoSQL Movement Edition: 1st Edition.

Evaluation Pattern

CIA1 - 10%

CIA2- 25%

CIA3 -10%

Attendance 5%

ESE 50%

CS432 PROBABILITY AND QUEUING THEORY




At the end of the course, the students would

Have a fundamental knowledge of the basic probability concepts.

Have a well – founded knowledge of standard distributions which can describe real life

phenomena.

Acquire skills in handling situations involving more than one random variable and

functions of random variables.

Understand and characterize phenomena which evolve with respect to time in a

probabilistic manner.

Be exposed to basic characteristic features of a queuing system and acquire skills in

analyzing queuing models.

Learning Outcome

Upon successful completion of the course, students would be able to:

Develop analytical capability in Statistical methods and Queuing theory.

Analyze real world problems using the knowledge of Statistical methods and its

applications.


Probability and Random Variable

Axioms of probability - Conditional probability - Total probability – Baye’s theorem

Random variable - Probability mass function - Probability density function - Properties –

Moments - Moment generating functions and their properties.


Standard Distributions

Binomial, Poisson, Geometric, Negative Binomial, Uniform, Exponential, Gamma,

Weibull and Normal distributions and their properties - Functions of a random variable.


Two Dimensional Random Variables

Joint distributions - Marginal and conditional distributions – Covariance – Correlation and

regression - Transformation of random variables - Central limit theorem


Random Processes and MARKOV Chains

Classification - Stationary process - Markov process - Poisson process - Birth and death

process - Markov chains - Transition probabilities - Limiting distributions. Transition

Diagram.


Queuing Theory

Markovian models – M/M/1, M/M/C, finite and infinite capacity M/M/∞ queues - Finite

source model - M/G/1 queue (steady state solutions only) – Pollaczek – Khintchine formula

– Special cases.Single and Multiple Server System.


TEXT BOOKS

Ross, S., “A first course in probability”, Pearson Education, Sixth Edition, Delhi, 2002.

Medhi J., “Stochastic Processes”, New Age Publishers, New Delhi, 1994. (Chapters 2, 3,4)

T.Veerarajan, “Probability, Statistics and Random process”, Tata McGraw Hill, Second

Edition, New Delhi, 2003

REFERENCE BOOKS

Allen, A.O., “Probability, Statistics and Queuing Theory”, Academic press, New Delhi,

1981.

Taha, H. A., “Operations Research-An Introduction”, Pearson Education Edition Asia,

Seventh Edition, Delhi, 2002.

Gross, D. and Harris, C.M., “Fundamentals of Queuing theory”, John Wiley.


Nptel lectures

Evaluation Pattern

CIA1 - 10%

CIA2- 25%

CIA3 -10%

Attendance 5%

ESE 50%

MAT421P CALCULUS USING MAXIMA

Total Teaching Hours for Semester: 75 No of Lecture Hours/Week: (3+2L)



The course Calculus of Several Variables using Maxima is aimed at enabling the students to

explore and study the Calculus with Several variables in a detailed manner with the help of the

mathematical software Maxima. This course is designed with a learner-centric approach wherein

the students will acquire mastery in understanding Multivariate Calculus using Maxima as tool.

Learning Outcome

This course aims at providing hands on experience in using Maxima functions for Multivariate

Calculus. The objective is to familiarize students in using Maxima for

Plotting lines in two and three dimensional space.

Finding the tangent vector and the gradient vector field.

Evaluation of Line integral.

Applications of Line integrals.

Evaluation of double integral.

Applications of double integrals.


Limits, Continuity, Differentiability and Mean Value Theorems

Definition of the limit of a function (ε-δ) form – Continuity, Uniform Continuity – Types of

discontinuities – Properties of continuous functions on a closed interval – Differentiability – Mean

Value Theorems: Rolle’s theorem – Lagrange’s and Cauchy’s First Mean Value Theorems –

Taylor’s theorem (Lagrange’s form and Cauchy’s forms of remainder) – Maclaurin’s theorem and

expansions -Indeterminate forms.- Maxima and Minima.


Successive and Partial Differentiation

Successive differentiation – nth derivatives of functions – Leibnitz theorem and its applications –

Partial differentiation – First and higher order derivatives – Differentiation of homogeneous

functions – Euler’s theorem – Taylor’s theorem for two variables (only statements and problems)-

Maxima and Minima of functions of two variables.


Curve Tracing

Tangents and Normals, Curvature, Asymptotes, Singular points, Tracing of curves (Parametric

representation of curves and tracing of parametric curves, Polar coordinates and tracing of curves

in polar coordinates).


G.B. Thomas, M.D.Weir and J. Hass, ThomasCalculus, 12th ed., Pearson Education

India, 2015.


H. Anton, I. Birens and S. Davis, Calculus, John Wiley and Sons Inc., 2002.

F. Ayres and E. Mendelson, Schaum's Outline of Calculus, 6th ed. USA: Mc. Graw Hill.,

2013.

J. Stewart, Single Variable Essential Calculus: Early Transcendentals, 2nd ed.: Belmont,

USA: Brooks/Cole Cengage Learning., 2013.

S. Narayanan & T. K. M. Pillay, Calculus, Reprint, India: S. Viswanathan Pvt. Ltd.,

2009. (vol. I & II.)

M. Spivak, Calculus, 3rd ed., Cambridge University Press, 2006.

T.M. Apostol, Calculus, Vol-II , Wiley India Pvt. Ltd., 2011.

J. Edwards, An elementary treatise on the differential calculus: with applications and

numerous example, Reprint, Charleston, USA: BiblioBazaar, 2010.

N. P. Bali, Differential Calculus, New ed. New Delhi, India: Laxmi Publications (P) Ltd.,

2012.

DIFFERENTIAL CALCULUCS LAB

Proposed Topics

1. Introduction to Basic commands and plotting of graph using draw command.

2. Vectors-dot and cross products, Plotting lines in two and three dimensional space, Planes

and Surfaces.

3. Arc length, Curvature and Normal Vectors.

4. Curves in sphere: Tangent vectors and velocity- Circular helix with velocity vectors.

5. Functions of two and three variables: Graphing numerical functions of two Variables

6. Graphing numerical functions in polar coordinates. Partial derivatives and the directional

derivative.

7. The gradient vector and level curves- The tangent plane -The gradient vector field. Vector

fields: Normalized vector fields- Two dimensional plot of the vector field.

8. Double Integrals - User defined function for calculating double integrals - Area properties with

double integrals.

9. Line Integrals – Curl and Green’s theorem- Divergence theorem.


TEXT BOOKS

Zachary Hannan, wxMaxima for Calculus I (Creative Commons Attribution-Non Commercial-

Share Alike 4.0 International), 1sted: Zachary Hannan - Solano Community College, 2015.

Zachary Hannan, wxMaxima for Calculus II (Creative Commons Attribution-Non Commercial-

Share Alike 4.0 International), 1st ed: Zachary Hannan - Solano Community College, 2015.

REFERENCE BOOKS:

M. D. Weir, J. Hass and F. R. Giordano, Thomas’ Calculus, 11th ed., USA: Pearson, 2012.

J. Stewart, Multivatialble calculus, 7th ed.: Belmont, USA: Brooks/Cole Cengage Learning., 2013.

CS433P ADVANCED PYTHON PROGRAMMING



The major goals of this course are to learn how to use tools for acquiring, cleaning, analyzing,

exploring, and visualizing data; making data-driven inferences and decisions; and effectively

communicating results. These will be accomplished through course activities on the

following data science topics:

Classification methods, including logistic regression, k-nearest neighbors, decision trees,

and support vector machines.

Data visualization, Clustering methods, Dimensionality reduction, including principle

component analysis, Network analysis

Rating, ranking, and elections , Cleaning and reformatting messy datasets using regular

expressions or dedicated tools such as open refine

Natural language processing, Ethics of big data

A major component of this course will be learning how to use python-based programming

tools to apply these methods to real-life datasets.

Learning Outcomes

At the end of the course, a student should be able to:

Acquire data through web-scraping and data APIs, Clean and reshape messy datasets

Use exploratory tools such as clustering and visualization tools to analyze data

Perform linear regression analysis, Use methods such as logistic regression, nearest

neighbors, decision trees, and support vector machines to build a classifier

Apply dimensionality reduction tools such as principle component analysis

Perform basic analysis of network data, Evaluate outcomes and make decisions based on

data, Effectively communicate results


Introduction to Python Programming

A Really Dumb Spam Filter, A More Sophisticated Spam Filter, Implementation, Testing

Our Model, Simple Linear Regression, Using Gradient Descent, Maximum Likelihood

Estimation.


Multiple Regression

Further Assumptions of the Least Squares Model, Fitting the Model, Interpreting the Model,

Goodness of Fit, Digression: The Bootstrap, Standard Errors of Regression Coefficients,

Regularization.

Logistic Regression

The Problem, The Logistic Function, Applying the Model, Goodness of Fit, Support Vector

Machines.


Decision Trees

What Is a Decision Tree? Entropy, The Entropy of a Partition, Creating a Decision Tree,

Putting It All Together, Random Forests. Neural Networks

Perceptrons, Feed-Forward Neural Networks, Backpropagation, Example: Defeating a

CAPTCHA, Clustering: The Idea, The Model, Example: Meetups, Choosing k, Example:

Clustering Colors, Bottom-up Hierarchical Clustering,


Natural Language Processing

Word Clouds, n-gram Models, Grammars, An Aside: Gibbs Sampling, Topic Modelling,

Network Analysis, Betweenness Centrality, Eigenvector Centrality, Directed Graphs and

PageRank, Recommender Systems, Manual Curation, Recommending What’s Popular,

Item-Based Collaborative Filtering.


Databases and SQL

CREATE TABLE and INSERT, UPDATE, DELETE, SELECT, GROUP BY, ORDER BY,

JOIN, Subqueries, Indexes, Query Optimization, NoSQL

MapReduce, Word Count, Why MapReduce?, MapReduce More Generally, Analyzing

Status Updates, Example: Matrix Multiplication, Go Forth and Do Data Science, IPython,

Mathematics, Not from Scratch, Find Data, Do Data Science.


TEXT BOOK:

1. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython ,2nd edition,

Wes McKinney,O’Reilly Media, 2017.

REFERENCE BOOK

2. Data Science from Scratch: First Principles with Python, Joel Grus

O’Reilly Media, 2015.

ADVANCED PYTHON PROGRAMMING LAB

Project Based on advanced python programming concepts

CS434 DATA COMMUNICATION




The course is intended to provide a supportive foundation that will guide through an

examination of the various components of the information domains of information security.

Objectives of this course are:

To understand the broader field of Data Communication and Network Models.

To examine the errors in Data Link layers, Detection and Correction codes.

To investigate the Parity checking, Hamming Code and Error Control process.

To discuss the Internetwork concepts and Routing concepts.

To learn the Application layer concepts like DNS, SMTP, FTP and WWW.

To examine various factors in the security threats and discuss the Cryptography.

Learning Outcomes:

After completing this course the student must demonstrate the knowledge and ability to:

Independently understand basic computer network technology.

Understand and explain Data Communications System and its components.

Identify the different types of network topologies and protocols.

Enumerate the layers of the OSI model and TCP/IP and explain the function(s) of each layer.

Identify the different types of network devices and their functions within a network

Understand and building the skills of subnetting and routing mechanisms.

Familiarity with the basic protocols of Computer Networks, and how they can be used to

assist in network design and implementation.


Data Communications

http://www/

Components – Direction of Data flow – Networks – Components and Categories – Types of

Connections – Topologies – Protocols and Standards – ISO / OSI model – Transmission

Media – Coaxial Cable – Fiber Optics – Line Coding – Modems – RS232 Interfacing

sequences.


Data Link Layer

Error – Detection and Correction – Parity – LRC – CRC – Hamming code –Low Control

and Error control -Stop and Wait – Go back-N ARQ – Selective Repeat ARQ - Sliding

window – HDLC. LAN - Ethernet IEEE 802.3 - IEEE 802.4 - IEEE 802.5 - IEEE 802.11 –

FDDI - SONET – Bridges.


Network Layer

Internetworks – Packet Switching and Datagram approach – IP addressing methods –

Subnetting – Routing – Distance Vector Routing – Link State Routing – Routers.


TRANSPORT LAYER

Duties of transport layer – Multiplexing – Demultiplexing – Sockets – User Datagram

Protocol (UDP) – Transmission Control Protocol (TCP) – Congestion Control – Quality of

services (QOS) – Integrated Services.


APPLICATION LAYER

Domain Name Space (DNS) – SMTP – FTP – HTTP - WWW – Security – Cryptography-

Case study.


Text Book:

Behrouz A.Forouzan, “Data communication and Networking”, Tata McGraw-Hill, 5th

Edition 2013.

Reference Books:

James F.Kurose and Keith W.Ross,“Computer Networking: A Top-Down Approach

Featuring the Internet”, Pearson Education, 2012.

Larry Peterson and Peter S. Davie, “Computer Networks”, Morgan Kaufmann, 5th Edition,

2011.

Andrew S. Tanenbaum, “Computer Networks”, 5thEdition, Pearson 2011.

William Stallings,“Data and Computer Communication”, 8th Edition, Pearson Education,

2016.


Online Learning Resources:

(i) http://nptel.ac.in/courses/106105081/ (Video Lectures)

(ii) http://nptel.ac.in/courses/106105080/ (Web Course)

(iii) http://nptel.ac.in/courses/106106091/ (Certificate Course)

(iv) https://www.edx.org/course/network-security-ritx-cyber504x (Certificate Course)

(v) https://alison.com/course/diploma-in-computer-networking

http://nptel.ac.in/courses/106105081/



http://www.edx.org/course/network-security-ritx-cyber504x

http://www.edx.org/course/network-security-ritx-cyber504x

CS435 SOFTWARE ENGINEERING




To be aware of Different life cycle models; Requirement dictation process; Analysis

modelling and specification; Architectural and detailed design methods; Implementation and

testing strategies; Verification and validation techniques; Project planning and management

and Use of CASE tools.

Learning Outcome

● Implement the different life cycle models.

● Demonstrate the ability to manage a project including planning, scheduling and risk

assessment/management.

● Author a formal specification for a software system and software testing plan.

● Demonstrate proficiency in rapid software development techniques.

● Identify specific components of a software design that can be targeted for reuse.

● Demonstrate proficiency in software development cost estimation.

● Estimate software development cost.


Software Process

Introduction –S/W Engineering Paradigm – life cycle models (water fall, incremental, spiral,

WINWIN spiral, evolutionary, prototyping, object oriented) - system engineering –

computer based system – verification – validation – life cycle process – development process

–system engineering hierarchy.


Software Requirements

Functional and non-functional - user – system –requirement engineering process – feasibility

studies – requirements – elicitation – validation and management – software prototyping –

prototyping in the software process – rapid prototyping techniques – user interface

prototyping -S/W document. Agile methods, Extreme Programming, SCRUM.


Design Concepts and Principles

Design process and concepts – modular design – design heuristic – design model and

document. Architectural design – software architecture – data design – architectural design

– transform and transaction mapping – user interface design – user interface design

principles. Real time systems - Real time software design – system design – real time

executives – data acquisition system - monitoring and control system. SCM – Need for SCM

– Version control – Introduction to SCM process – Software configuration items.


Testing

Taxonomy of software testing – levels – test activities – types of s/w test – black box testing

– testing boundary conditions – structural testing – test coverage criteria based on data flow

mechanisms – regression testing – testing in the large. S/W testing strategies – strategic

approach and issues - unit testing – integration testing – validation testing – system testing

and debugging.


Software Project Management

Measures and measurements – S/W complexity and science measure – size measure – data

and logic structure measure – information flow measure. Software cost estimation – function

point models – COCOMO modelDelphi method.- Defining a Task Network – Scheduling –

Earned Value Analysis – Error Tracking - Software changes – program evolution dynamics

– software maintenance – Architectural evolution. Taxonomy of CASE tools – Case Study.


TEXT BOOKS

Roger S. Pressman, Software engineering- A Practitioner’s Approach, McGraw-Hill

International Edition, 6th Edition 2012.

REFERENCE BOOKS

Ian Sommerville, “Software engineering,” Pearson education Asia, 9th Edition 2013.

Pankaj Jalote- “An Integrated Approach to Software Engineering,” Narosa publishing

house 2011.

James F Peters and Witold Pedryez, “Software Engineering – An Engineering Approach”,

John Wiley and Sons, New Delhi, 2010.

Ali Behforooz and Frederick J Hudson, “Software Engineering Fundamentals”, OUP India

2012.

http://nptel.iitm.ac.in/courses/Webcoursecontents/IIT%20Kharagpur/Soft%20Engg/New

_index1.html.

http://nptel.iitm.ac.in/courses/Webcoursecontents/IIT%20Kharagpur/Soft%20Engg/New

CS531 ARTIFICIAL INTELLIGENCE




Introduction

Intelligent Agents – Agents and environments - Good behavior – The nature of environments

– structure of agents Problem Solving - problem solving agents – example problems –

searching for solutions – uniformed search strategies - avoiding repeated states – searching

with partial information.


Searching Techniques

Informed search and exploration – Informed search strategies – heuristic function – local

search algorithms and optimistic problems – local search in continuous spaces – online

search agents and unknown environments - Constraint satisfaction problems (CSP) –

Backtracking search and Local search for CSP – Structure of problems - Adversarial Search

– Games – Optimal decisions in games – Alpha – Beta Pruning – imperfect real-time decision

– games that include an element of chance.


Knowledge Representation

First order logic – representation revisited – Syntax and semantics for first order logic –

Using first order logic – Knowledge engineering in first order logic - Inference in First order

logic – prepositional versus first order logic – unification and lifting – forward chaining –

backward chaining - Resolution - Knowledge representation Ontological Engineering -

Categories and objects – Actions Simulation and events - Mental events and mental objects


Learning

Learning from observations - forms of learning - Inductive learning - Learning decision trees

- Ensemble learning Knowledge in learning – Logical formulation of learning Explanation

based learning – Learning using relevant information – Inductive logic programming -

Statistical learning methods - Learning with complete data - Learning with hidden variable

- EM algorithm - Instance based learning - Neural networks - Reinforcement learning –

Passive reinforcement learning - Active reinforcement learning - Generalization in

reinforcement learning.

Unit-5 Teaching Hours: 9

Applications

Planning – planning as search – partial order planning – construction and use of planning

graphs –Communication – Communication as action – Formal grammar for a fragment of

English – Syntactic analysis – Augmented grammars – Semantic interpretation – Ambiguity

and disambiguation – Discourse understanding – Grammar induction


TEXT BOOKS

Stuart Russell and Peter Norvig, “Artificial Intelligence – A Modern Approach”, 3rd Edition,

Pearson Education, 2014.

Elaine Rich and Kevin Knight, “Artificial Intelligence”, 3rd Edition, Tata McGraw-Hill,

2012.

REFERENCE BOOKS

Nils J. Nilsson, “Artificial Intelligence: A New Synthesis”, 1st Edition, Harcourt Asia Pvt.

Ltd., 2012.

George F. Luger, “Artificial Intelligence-Structures and Strategies for Complex Problem

Solving”, 6th Edition, Pearson Education / PHI, 2009.

CS532P MACHINE LEARNING USING R




To understand basic concepts of machine learning

Discover how to build machine learning algorithms, prepare data, and use

different techniques using R

Learning Outcome

Implement different machine learning algorithm techniques using R


Introduction

Introduction to Machine Learning- The origins of Machine Learning-Uses and abuses of

machine learning-How machine learn-Machine learning in practice- Machine learning with

R


Managing and Understanding Data

R data structures- Managing data with R- Exploring and understanding data – Exploring the

structure of data- Exploring numeric variables- categorical variable- relationship between

variables


Classification Using Nearest Neighbors

Understanding nearest neighbour classification, The k-NN algorithm, Example

Probabilistic Learning- Classification Using Naïve Bayes- Basic concepts of Bayesian

methods, The Naïve Bayes algorithm


Forecasting Numeric Data-Regression Methods

Understanding Regression– Learning- Example, understanding regression trees and model

trees.

Black box methods- Neural networks and support vector machines- understanding neural

networks- example – modelling strength of concrete with ANNs.


Association Rules, Clustering with K-means, Model Performance

Understanding association rules- Example, Understanding clustering- Example – Evaluating

Model Performance- Measuring performance for classification in R


TEXT BOOKS:

Christopher Bishop, “Pattern Recognition and Machine Learning” Springer, 2006.

Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press, 2012.

Ethem Alpaydin, “Introduction to Machine Learning”, Prentice Hall of India, 2005.

Hastie, Tibshirani, Friedman, “The Elements of Statistical Learning” (2nd ed)., Springer,

2008.

Stephen Marsland, “Machine Learning –An Algorithmic Perspective”, CRC Press, 2009.

REFERENCE BOOKS:

Tom Mitchell, "Machine Learning", McGraw-Hill, 1997.

Brett Lantz, “Machine Learning with R”, Second Edition, 2015.

R PROGRAMMING LAB


1. Write a R program to create a simple bar plot of five subjects marks.

2. Write a R program to create an array of two 3x3 matrices each with 3 rows and 3

columns from two given two vectors. Print the second row of the second matrix of the

array and the element in the 3rd row and 3rd column of the 1st matrix.

3. Write a R program to extract 3rd and 5th rows with 1st and 3rd columns from a given data

frame

4. Write a R program to create a data frame using two given vectors and display the

duplicated elements and unique rows of the said data frame.

5. Write a R program to rotate a given matrix 90 degree clockwise rotation.

6. Create the vector (x1 + 2x2 − x3, x2 + 2x3 − x4, . . . , xn−2 + 2xn−1 − xn)

7. Write a R program to find Sum, Mean and Product of a Vector, ignore element like NA

or NaN.

8. Write a R program to create a list of dataframes and access each of those data frames

from the list.

9. Reading the data into R and apply Simple statistics on it with required graphs.

10. Take any dataset and perform following operations on it.

o Basic row manipulations

o Advanced row selection

o Basic column operations

o Permanently changing the column order

o Adding and removing new columns

o Adding new columns; Advanced

o Counting observations

o Working with keys and subsetting

o Selecting existing columns and reshaping

o Getting counts for grouped data

CS533P BIG DATA AND CLOUD COMPUTING USING HADOOP




In this course, you will understand:

What Big Data is, the limitations of the traditional solutions for Big Data problems, how

Hadoop solves those Big Data problems, Hadoop Ecosystem, Hadoop Architecture, HDFS,

Anatomy of File Read and Write & how MapReduce works.

Learning Outcome

Prepare and equip students for opportunities in ever changing technology with hands-on

training.

Transform the students to become globally competent professionals

Illustrate the core concepts of the cloud computing paradigm: how and why this paradigm

shift came about, the characteristics, advantages and challenges brought about by the various

models and services in cloud computing.

Interpret the fundamental concepts in data-centres to understand the trade-offs in power,

efficiency and cost.

Discuss system virtualization and outline its role in enabling the cloud computing system

model.

Summarize the fundamental concepts of cloud storage and demonstrate their use in storage

systems.

Analyse various cloud programming models and apply them to solve problems on the cloud.


Big Data Introduction

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

environment-First steps with the Hadoop “ecosystem”.


Introduction to Hadoop

Hadoop components: MapReduce/Pig/Hive/HBase-Loading data into Hadoop-Handling

files in Hadoop-Getting data from Hadoop-Querying big data with Hive-Introduction to the

SQL Language -From SQL to HiveQL.


Cloud Computing Technology

Hardware and Infrastructure: Clients – Security – Network –Services; Accessing the Cloud:

Platforms – Web Applications – Web API; Cloud Storage: Overview – Cloud storage

providers – Standards: Application – client – Infrastructure – Service.

Using Cloud Platforms: Understanding Abstraction and Virtualization– Capacity Planning

– Exploring Platform as a Service – Using Google web services – Using Amazon web

services – Using Microsoft Cloud services.


Cloud Services and Applications

Understanding Service Oriented Architecture– Moving Applications to the cloud – Working

with cloud based storage – Working with productive software – Using Web mail services –

Communicating with the cloud – Using media and streaming.


Developing Applications, Thin Clients and Migration

Develop applications using Google, Microsoft – Google App Engine – Microsoft Windows

Azure – Virtualizing your Organization – Server Solutions – Thin Clients – Migrating

cloud services for individuals, Enterprise cloud offerings, Wave approach.


TEXT BOOKS

Judith Hurwitz, Alan Nugent, Dr. Fern Halper, Marcia Kaufman, Big Data for Dummies,

2012.

Anthony Velte, Toby Velte, and Robert Elsenpeter, “Cloud Computing – A Practical

Approach”, 1st Edition, McGraw Hill. 2010.

Rajkumar Buyya and Vecchiola, Selvi, “Mastering Cloud Computing”, 1st Edition, McGraw

Hill. 2013.

Barrie Sosinsky, “Cloud Computing Bible”, 1st Edition, John Wiley & Sons, 2010.

REFERENCE BOOKS

Massimo Cafaro and Giovanni Aloisio, “Grids, Clouds and Virtualization”, Springer, 2011.

Rajkumar Buyya, James Broberg, Andrzej M. Goscinski, “Cloud Computing: Principles and

Paradigms”, Wiley Publications, 2011.

Michael Miller, “Cloud Computing: Web-Based Applications that Change the Way You

Work and Collaborate Online”, Que Publishing, August 2008.

LAB EXERCISE HADOOP

1. Word count application in Hadoop.

2. Sorting the data using MapReduce.

3. Finding max and min value in Hadoop.

4. Implementation of decision tree algorithms using MapReduce.

5. Implementation of K-means Clustering using MapReduce.

6. Generation of Frequent Itemset using MapReduce.

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

8. Using hadoop’s map-reduce, Evaluating Number of Products Sold in Each Country in the

online shopping portal. Dataset is given.

9. Analyze the sentiment for product reviews, this work proposes a MapReduce technique

provided by Apache Hadoop.

10. Trend Analysis based on Access Pattern over Web Logs using Hadoop.

Essential Reading

Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, Professional Hadoop Solutions,

Wiley, 2015.

Tom White, Hadoop: The Definitive Guide, O’Reilly Media Inc., 2015.

Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013.

Recommended Reading

Pethuru Raj, Anupama Raman, DhivyaNagaraj and Siddhartha Duggirala,

HighPerformance Big-Data Analytics: Computing Systems and Approaches, Springer,

2015.

Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop Real-World Solutions

Cookbook, Packt Publishing, 2013.

Tom White, HADOOP: The definitive Guide, O Reilly, 2012.

CS534 INTERNET OF THINGS




Course Description

The subject provides fundamental concepts on Internet of Things, Sensors, actuators and

network connectivity, different platforms. It focuses on architecture of the IoT, different

platforms available for development of the IoT Projects along with case studies, which

gives a clear input to the students to understand the basic concepts of censors, I/O module

and data communication. This course gives a comprehensive understanding of IoT is

provided to the students in this course.

Learning Outcomes

Course Learning Outcome

To know the principle of a IoT Architecture

Ability to use concepts of sensors, I/O module on different boards

Ability to design efficient IoT solution to the requirements given by industry.


Overview and Introduction

Introduction to IoT - Definition and Characteristics, Key-features, Advantages,

Disadvantages, Functional Blocks, Communication Models- Communication APIs, different

Development platforms, Hardware-Sensors, wearable electronics, standard devices.


IoT software and Hardware

The software stack required, different hardware requirements, IoT boards (Raspberry Pi,

Arduino, Breadware). IoT Design Methodology. Smart systems

Case Study: Weather Monitoring, LED control systems, Smart light monitoring system


IoT Coding

Writing code, building a program, deploying to a device, writing to actuators, blinking

Led, Reading from sensors, Light switch, voltage reader.


HTTP

Device as HTTP server, relaying messages to and from the Netduino, request handlers,

Web Html, Handling sensor Requests, handling Actuator Requests. Bluetooth – BLE

Programming, Timers, Serial Communication, Interrupt Programming.


IoT Applications and Case Study

Home automation, Smart cities, Environment, Energy, Retail, Logistics, Agriculture,

Industry, Health and Life style, IoT and M2M


TEXT BOOKS

Rob Barton, Gonzalo Salgueiro, David Hanes, “IoT fundamentals : Networking

Technologies, Protocols and use cases for Internet of Things”, Cisco Press, 2017.

Adrian McEwen and Hakim Cassimally, “Designing the Internet of Things”, John Wiley &

sons, 2013.

CunoPfister ,”Getting Started with the Internet of Things: Connecting censors and

Microcontrollers to the cloud”, Maker Media 2011.

REFERENCE BOOKS

Rob Barton, Gonzalo Salgueiro, David Hanes, “IoT fundamentals : Networking

Technologies, Protocols and use cases for Internet of Things”, Cisco Press, 2017.

Adrian McEwen and Hakim Cassimally, “Designing the Internet of Things”, John Wiley &

sons, 2013.

Cuno Pfister,”Getting Started with the Internet of Things: Connecting censors and

Microcontrollers to the cloud”, Maker Media 2011.

CS561 GERMAN LANGUAGE-I


Max Marks:100 Credits:03


The Basic Course in German aims to provide students a good knowledge of the language,

enabling them to read, write and speak German, whereby the emphasis is laid on speech.

Learning Outcome

At the end of the course Students are in the position to communicate in a basic manner.


Greetings, ordering, requesting, saying thank you Grammar

Greetings, ordering, requesting, saying thank you Grammar - the article the, conjugation of verbs


Shopping Grammar

Shopping Grammar - adjectives, endings before nouns.


Addresses, Occupations, Studies Grammar

Addresses, Occupations, Studies Grammar - verb to be, the definite/indefinite articles.


Leisure Time, Sport, Hobbies Grammar

Leisure Time, Sport, Hobbies Grammar - position of a verb in a main clause.


At a Restaurant, Food and Drink Grammar

At a Restaurant, Food and Drink Grammar - the personal pronoun in the Nominative,

Accusative.


Haeusermann/Dietrich/Guenther, Sprachkurs Deutsch, 6th Edition,

Kaminski/Woods/Zenker, Delhi: Goyal 1997.


Dollenmayer/Hansen, Neue Horizonte, 4th Edition, Lexington: D.C. Heath 1996.

CS631 PROJECT MANAGEMENT




The purpose of this course is to lay the foundation for a solid understanding of project

management concepts and principles and to familiarize students with the complexity and

challenge of managing public or private projects with tight schedules and limited resources.

Students will gain a sound understanding of project management concepts and principles by

applying relevant tools and techniques and by making extensive use of case studies and

simulation exercises to assimilate that knowledge

Course Objectives:

The course aims at the following learning targets:

To understand the concepts of project definition, life cycle, and systems approach.

To develop competency in project scooping, work definition, and work breakdown

structure (WBS).

To handle the complex tasks of time estimation and project scheduling, including

PERT and CPM.

To develop competencies in project costing, budgeting, and financial appraisal.

Learning Outcome:

Upon completion of this course, the students will be able to

CO1: Apply the concept of project management in engineering field through project

management life cycle.

CO2: Analyze the quality management and project activity in engineering field through work

breakdown structure.

CO3: Analyze the fundamentals of project and network diagram in engineering and

management domain through PDM techniques.

CO4: Evaluate the concept of network analysis through PERT and CPM techniques

CO5: Apply the concept of scheduler based on resource availability in engineering and

management field through project proposal.


Introduction to Project and Project Management

Introduction to Project: Definition of a Project, Sequence of Activities, Unique activities,

Complex Activities, Connected Activities, One Goal, Specified Time, Within Budget,

According to Specification. Defining a Program, Project parameters: Scope, Quality, Cost,

Time, Resources; The scope triangle: Time, Cost, and Resource Availability, Project

Classification

Project Management: Principles of Project Management: Defining, Planning, Executing,

Controlling, Closing; Project Management Life Cycle: Phases of Project Management,

Levels of Project Management.


Quality Management and Project Activities:

Quality Management: Continuous Quality Management Model, Process Quality

Management Model; Risk Management, Risk Analysis; Relationship between Project

Management and other Methodologies

Project Activities: Work Breakdown Structure, Uses of WBS, Generating the WBS: Top-

Down/ Bottom-Up Approach, WBS for Small Projects, Intermediate WBS for large projects;

Criteria to Test for Completeness in the WBS: Measurable Status, Bounded, Deliverable,

Cost/Time Estimate, Acceptable Duration Limits, Activity Independence; Approaches to

Building the WBS: various approaches, Representing WBS.


Activity Duration, Resource Requirements, & Cost and Fundamentals of Project

Network Diagram

Activity Duration, Resource Requirements, & Cost: Duration: Resource Loading versus

Activity Duration, Variation in Activity Duration, Methods for Estimating Activity

Duration, Estimation Precision; Resources; Estimating Cost, JPP Session to Estimate

Activity Duration & Resource Requirements, Determining Resource Requirements

Fundamentals of Project Network Diagram: Project Network Diagram, Benefits to

Network- Based Scheduling, Building the Network Diagram Using the PDM, Analyzing the

Initial Project Network Diagram.


Network Analysis: PERT and Network Analysis- CPM

Network Analysis – PERT: Introduction to Project Evaluation and Review Technique,

Event, Activity, Dummy, Network rules, Graphical guidelines for network, Common partial

situations in network, numbering the events, Cycles; Developing the Network, Planning for

network construction, modes of network construction, steps in developing network,

hierarchies; Time Estimates in PERT, Uncertainties and use of PERT, Time estimates,

Frequency distribution, Mean, Variance & standard deviation, Probability distribution, Beta

distribution, Expected time; Time Computations in PERT, Earliest expected time,

Formulation for TE, Latest allowable occurrence time, Formulation for TL, Combined

tabular computations for TE, TL; Slack, Critical Path, Probability of meeting schedule date.

Network Analysis- CPM: Introduction to Critical Path Method, Procedure, Networks,

Activity time estimate, earliest event time, Latest allowable occurrence time, Combined

tabular computations for TE and TL, Start & Finish times of activity, Float, Critical activities

& Critical path. Crashing of project network, Resource levelling and Resource allocation


Schedules Based on Resource Availability and Joint Project Planning Session

Schedules Based on Resource Availability: Resources, Levelling Resources, Acceptability

Levelled Schedule, Resource Levelling Strategies, Work Packages: Purpose of a Work

Package, Format of a Work Package

Joint Project Planning Session: Planning the Sessions, Attendees, Facilities, Equipment,

Complete Planning Agenda, Deliverables, Project Proposal Text Books And Reference

Books:


TEXT BOOKS:

“Effective Project Management”, Robert K. Wysocki, Robert Beck. Jr., and David B. Crane;

- John Wiley & Sons 2003.

“Project Planning and Control with CPM and PERT” Dr. B.C. Punmia & K.K.Khandelwal;

- Laxmi Publications, New Delhi 2011.


REFERENCE BOOKS:

“Project Management” S. Choudhury, - TMH Publishing Co. Ltd, New Delhi 1998.

“Total Project Management- The Indian Context” P. K. Joy, Macmillan India Ltd., Delhi

2017.

“Project Management in Manufacturing and High Technology Operations” Adedeji

Bodunde Badiru, - John Wiley and Sons 2008.

“Course in PERT & CPM” R.C.Gupta, - DhanpatRai and Sons, New Delhi.

“Fundamentals of PERT/ CPM and Project Management” S.K. Bhattacharjee; - Khanna

Publishers, New Delhi 2004.

CS661 GERMAN LANGUAGE-II


Max Marks:100 Credits:03


In Level II the students will review and utilize all the knowledge they received in Level I. The

course will increase the levels of speaking, listening, reading and writing, but the emphasis here

is also on speech.

Learning Outcome

At the end of the course

• Students will be in the position to communicate with native speakers.


The Technical World, Ownership

Grammar – the verb to have, Nominative and Accusative


Staying in a hotel

Grammar – Modalverbs, Past Tense of to have and to be


Travel

Grammar – irregular Verbs, Perfect to be


Work and Profession

Grammar –Prepositions, Word Order in a sentence.


Haeusermann/Dietrich/Guenther, Sprachkurs Deutsch, 6th Edition,

Kaminski/Woods/Zenker, Delhi: Goyal 1997.


Dollenmayer/Hansen, Neue Horizonte, 4th Edition, Lexington: D.C. Heath 1996.

PROJECT PHASE - 2


The Main project is intended to give practical exposure in solving the real time problems. The

main project is to introduce the student to the methodology for solving a problem and preparing a

report using the steps of software engineering. Student should take up a project of any domain and

submit a report.

The students should carry out the project during the allotted lab hours in the computer lab.

The project could be of any domain.

The guide can award internal marks by evaluating the performance of the students during

the course of the project work.

The format of the project report shall be instructed by the faculty in-charge.

Learning Outcome

Students will gain knowledge about product development and different stages of product

development.

Evaluation Pattern:

S No. Contents Marks

1 Document Submission

i) Synopsis 05

ii) Software requirement specification 10

iii) Database design 10

iv) User Interface design 05

v) Final copy 10

2 Presentation 15

3 Demo

Demo 1 20

Demo 2 20

4 Attendance 05

Total 100

ELECTIVE – 1

CS535E01 SOFTWARE QUALITY MANAGEMENT




It provide students to understand an integrated approach to software development

incorporating quality management methodologies, Software quality models, Quality

measurement and metrics, Quality plan, implementation and documentation, Quality tools

including CASE tools, Complexity metrics and Customer Satisfaction and International

quality standards – ISO, CMM

Learning Outcomes

Software quality models.

Quality measurement and metrics.

Quality plan, implementation and documentation.

Quality tools including CASE tools.

Quality control and reliability of quality process.

Quality management system models.

Complexity metrics and Customer Satisfaction.

International quality standards – ISO, CMM.


Introduction to Software Quality

Software Quality – Hierarchical models of Boehm and McCall – Quality measurement –

Metrics measurement and analysis – Gilb’s approach – GQM Model


Software Quality Assurance

Quality tasks – SQA plan – Teams – Characteristics – Implementation – Documentation –

Reviews and Audits


Quality Control And Reliability

Tools for Quality – Ishikawa’s basic tools – CASE tools – Defect prevention and removal –

Reliability models – Rayleigh model – Reliability growth models for quality assessment


Quality Management System

Elements of QMS – Rayleigh model framework – Reliability Growth models for QMS –

Complexity metrics and models – Customer satisfaction analysis.


Quality Standards

Need for standards – ISO 9000 Series – ISO 9000-3 for software development – CMM and

CMMI – Six Sigma concepts.


TEXT BOOKS

Allan C. Gillies, “Software Quality: Theory and Management”, Thomson Learning, 2003.

(UI : Ch 1-4 ; UV : Ch 7-8)

Stephen H. Kan, “Metrics and Models in Software Quality Engineering”, Pearson Education

(Singapore) Pte Ltd., 2002. (UI : Ch 3-4; UIII : Ch 5-8 ; UIV : Ch 9-11)

REFERENCE BOOKS

Norman E. Fenton and Shari Lawrence Pfleeger, “Software Metrics” Thomson, 2003

Mordechai Ben – Menachem and Garry S.Marliss, “Software Quality”, Thomson Asia Pte

Ltd, 2003.

Mary Beth Chrissis, Mike Konrad and Sandy Shrum, “CMMI”, Pearson Education

(Singapore) Pte Ltd, 2003.

ISO 9000-3 “Notes for the application of the ISO 9001 Standard to software development”.


(i) IEEE Transactions on Software Engineering

(ii) IEEE Transactions on Network and Service Management

(iii) IEEE Transactions on Reliability

(iv) Software Quality Journal - Springer

(v) Software Quality Assurance Journal - Elsevier

CS535E02 SOFTWARE TESTING




This course will examine fundamental software testing and related program analysis

techniques. In particular, the important phases of testing will be reviewed, emphasizing the

significance of each phase when testing different types of software. The course will also

include concepts such as test generation, test oracles, test coverage, regression testing,

mutation testing, program analysis (e.g., program-flow and data-flow analysis), and test

prioritization.

Learning Outcome

Students who complete this course will learn:

Various test processes and continuous quality improvement.

Types of errors and fault models.

Methods of test generation from requirements.

Behavior modeling using UML: Finite state machines (FSM).

Test generation from FSM models.

Input space modeling using combinatorial designs.

Combinatorial test generation.

Test adequacy assessment using: control flow, data flow, and program mutations.

The use of various test tools.

Application of software testing techniques in commercial environment.


Software Development Life Cycle models

Phases of Software project – Quality, Quality Assurance, Quality control – Testing,

Verification and Validation – Process Model to represent Different Phases - Life Cycle

models. White-Box Testing: Static Testing – Structural Testing –Challenges in White-Box

Testing.


White Box and Black Box Testing

Black-Box Testing: What is Black-Box Testing? - Why Black-Box Testing? – When to do

Black-Box Testing? – How to do Black-Box Testing? – Challenges in White Box Testing -

Integration Testing: Integration Testing as Type of Testing – Integration Testing as a Phase

f Testing – Scenario Testing – Defect Bash.


System and Acceptance Testing

System Testing Overview – Why System testing is done? – Functional versus Non-

functional Testing - Functional testing – Non functional Testing – Acceptance Testing –

Summary of Testing Phases.


Performance Testing

Factors governing Performance Testing – Methodology of Performance Testing – tools for

Performance Testing – Process for Performance Testing – Challenges. Regression Testing:

What is Regression Testing? – Types of Regression Testing – When to do Regression

Testing – How to do Regression Testing – Best Practices in Regression Testing.


Test Plan and Process

Test Planning, Management, Execution and Reporting: Test Planning – Test Management –

Test Process – Test Reporting –Best Practices. Test Metrics and Measurements: Project

Metrics – Progress Metrics – Productivity Metrics – Release Metrics.


TEXT BOOK:

Software Testing Principles and Practices – Srinivasan Desikan & Gopalswamy Ramesh,

2006, Pearson Education.

REFERENCE BOOKS:

Effective Methods of Software Testing–William E.Perry, 3rd edition, Wiley India, 2010.

Software Testing – Renu Rajani, Pradeep Oak, 2007, TMH.

ELECTIVE – 2

CS632E01 ECONOMETRICS



Course Objectives:

To provide the basic knowledge of econometrics. While the course is ambitious in terms of

its coverage of technical topics, equal importance is attached to the development of an

intuitive understanding of the material that will allow these skills to be utilised effectively

and creatively, and to give participants the foundation for understanding specialized

applications through self-study with confidence when needed.


Introduction

Nature and scope of Econometrics


Statistical Inference

Normal distribution; chi-sq, t- and F-distributions-Estimation of parameters-Testing of

hypotheses-Defining statistical hypotheses-Distributions of test statistics-Testing

hypotheses related to population parameters

Type-I and Type-II errors; Power of a test

Tests for comparing parameters from two samples.


Simple Linear Regression Model: Two Variable Case

Estimation of model by method of ordinary least squares-Properties of estimators-

Goodness of fit-Testing of Hypotheses-Scaling and units of measurement-Confidence

intervals-Gauss Markov Theorem-Forecasting


Multiple Linear Regression Model

Estimation of parameters-Properties of OLS estimators-Goodness of fit- R2 and Adjusted

R2 -Partial regression coefficients-Testing Hypotheses: Individual and Joint-Functional

Forms of Regression Models-Qualitative (dummy) independent variables


Violations of Classical Assumptions: Consequences, Detection and Remedies

Multicolinearity-Heteroscedasticity-Serial Correlation-Omission of a relevant variable-

Inclusion of irrelevant variable-Tests of specification


TEXT BOOKS:

D. N. Gujarati and D.C.Porter, Essentials of Econometrics, 4th Edition, McGraw Hill

International Edition, 2010.

Christopher Dougherty, Introduction to Econometrics, 4th edition, OUP, Indian edition,

2011.

REFERENCE BOOKS

Jay L. Devore, Probability and Statistics for Engineers, Cengage Learning, 2010.

John E. Freund, Mathematical Statistics, Prentice Hall, 2011.

Irwin Miller and Marylees Miller, John E. Freund's Mathematical Statistics with

Applications, 8th edition, Pearson.

CS632E02 E COMMERCE



Course Objectives

This course intends to make the students familiar with the required mechanisms for

conducting business transactions through electronic means. As a prerequisite, the students

should be having a basic knowledge about computer networks and information technology.

Learning Outcome

To provide exposure to the students about business through information technology.

To provide them with the fundamental knowledge of the use of computers in business.

To understand the various concepts of e-commerce.

To understand the methodology for online business dealing using e-commerce

infrastructure.

To understand the interrelationships between two media channels –mobile and social and

how brands can engage consumers through these channels.

To develop a strategic approach to define how mobile phones can be aligned and integrated

into an overall marketing strategy in organizations.


Introduction to E-Commerce

The Scope of Electronic Commerce, Definition of Electronic Commerce, Electronic E-

commerce and the Trade Cycle, Electronic Markets, Electronic Data Interchange, Internet

Commerce, E-Commerce in Perspective.

Business Strategy in an Electronic Age

Supply Chains, Porter’s Value Chain Model, Inter Organizational Value Chains,

Competitive Strategy, Porter’s Model, First Mover Advantage Sustainable Competitive

Advantage, Competitive Advantage using E-Commerce, Business Strategy, Introduction to

Business Strategy, Strategic Implications of IT, Technology, Business Environment,

Business Capability, Exiting Business Strategy, Strategy Formulation & Implementation

Planning, E-Commerce .Implementation, E-Commerce Evaluation.


Business-to-Business Electronic Commerce

Characteristics of B2B EC, Models of B2B Ec, Procurement Management Using the Buyer’s

Internal Marketplace, Just in Time Delivery, Other B2B Models, Auctions and Services from

Traditional to Internet Based EDI, Integration with Back-end Information System, The Role

of Software Agents for B2B EC, Electronic marketing in B2B, Solutions of B2B EC,

Managerial Issues.

Electronic Data Interchange (EDI) The Nuts and Bolts, EDI & Business.


Internet and Extranet

Automotive Network Exchange, The Largest Extranet, Architecture of the Internet, Intranet

and Extranet, Intranet software, Applications of Intranets, Intranet Application Case Studies,

Considerations in Intranet Deployment, The Extranets, The structures of Extranets, Extranet

products & services, Applications of Extranets, Business Models of Extranet Applications,

Managerial Issues.

Electronic Payment Systems

Is SET a failure, Electronic Payments & Protocols, Security Schemes in Electronic payment

systems, Electronic Credit card system on the Internet, Electronic Fund transfer and Debit

cards on the Internet, Stored – value Cards and E- Cash, Electronic Check Systems, Prospect

of Electronic Payment Systems, Managerial Issues


Public Policy

From Legal Issues to Privacy : EC- Related Legal Incidents, Legal Incidents, Ethical & Other

Public Policy Issues, Protecting Privacy, Protecting Intellectual Property, Free speech,

Internet Indecency & Censorship, Taxation & Encryption Policies.

Other Legal Issues. Contracts, Ethics, Consumer & Seller Protection in EC.


Infrastructure for EC

It takes more than Technology, A Network Of Networks, Internet Protocols, Web- Based

client/ Server, Internet Security, selling on the web, Chatting on the Web, Multimedia

delivery, Analyzing Web Visits, Managerial Issues. Mobile Commerce: Introduction to

Mobile Commerce; Mobile Marketing; M-commerce Applications; M-commerce Strategy

and Security, Social and Ethical Issues in M-commerce.


TEXT BOOKS:

David Whiteley, “E-Commerce”, Tata McGraw Hill, 2014.

Rayudu, C. S. (2004). E- Commerce. Himalaya Publishing House, 2012.

REFERENCE BOOKS:

Rayudu, C. S. (2004). E- commerce . (2012 ed.). Himalaya Publishing House.

Murthy, C.S.V. (2002). E-Commerce – Concepts, Models, Strategies. (2012 ed.). Himalaya

Publishing House.

Andersson, C., Freeman, D. James, I., Johnston, A. and Ljung, S. (2006) Mobile Media and

Applications, From Concept to Cash: Successful Service Creation and Launch. Wiley.

Bouwman, H., de Vos, H. and Haaker, T. (2010) Mobile Service Innovation and Business

Models. Springer.

ELECTIVE – 3

CS633E01 TENSORFLOW FOR DEEP LEARNING REASEARCH



Course Objectives:

Learn the Tensorflow for Deep Learning, including how to perform basic computation.

Build simple learning systems to understand their mathematical foundations.

Dive into fully connected deep networks used in thousands of applications.


Introduction to Deep Learning

Deep Learning Primitives- Deep learning architectures- Deep Learning Frameworks


Introduction to TENSORFLOW PRIMITIVES

Introducing Tensors- Basic Computations in Tensorflow-Imperative and Declarative

Programming

Overview of Tensorflow -Why Tensorflow? -Graphs and Sessions- Operations-Basic

operations, constants, variables -Control dependencies -Data pipeline-TensorBoard.


Linear and Logistic Regression with Tensorflow

Mathematical Review- Learning with Tensorflow, Training Linear and Logistic Models in

Tensorflow


Convolutional Neural Network

Introduction to Convolutional Architectures- Application of Convolutional Networks-

Training a Convolutional Network in TensorFlow


Recurrent Neural Networks

Overview of Recurrent Architectures- Recurrent Cells- Reinforcement Learning- Markov

Decision Processes- Reinforcement Algorithms- TensorFlow- Keras


Text Book:

Reza Bosagh Zadeh, Bharath Ramsundar, “TensorFlow for Deep Learning”, 2018.

Reference Book:

Ian Goodfellow, “Deep Learning”, 2016.

Francois Chollet, “Deep Learning with Python”, 2017.

CS633E02 VISUALIZATION TECHNIQUES USING TABLEAU



Course Objectives

This course provides an introduction as well as hands on experience in data visualization. It

introduces students to design principles for creating meaningful displays of quantitative and

qualitative data to facilitate managerial decision-making.

Course Objectives

Provide an overview and brief history of the practice of data visualization.

Introduce students to the key design principles and techniques for visualizing data.

Develop an understanding of the fundamentals of communication and alignment around

concepts that are required for effective data presentation.

Provide an overview and develop an introductory level of competency on the use of several

available software tools that can be used for data visualization.

Allow for project-based opportunities to identify, understand, analyze, prepare, and present

effective visualizations on a variety of topics.

Learning Outcomes

After taking this course,

Students should be able to collect and process data, create an interactive visualization, and

use it to demonstrate or provide insight into a problem, situation, or phenomenon.

Moreover, students should have the basic knowledge needed to critique various

visualizations (good and bad), and to identify design principles that make good visualizations

effective. Students should also have a basic understanding of some of the challenges present

in making data understandable across a wide range of potential audiences.

Finally, students will have the opportunity to demonstrate their own skills in identifying a

visualization that can be improved, completing their own design and/or analysis on the

underlying data, and working to publish or promote acceptance of their presentation.


Introduction to the Art and Science of Data Visualization, What is Data Visualization and

why does it matter?, Brief History of Data VisualizationCurrent visualization practitioners

of note.

Introduction to Tableau Importing Data / Connecting to External Sources Interface Overview

Creating Sheets and Dashboard.


Design Fundamentals, Design Principles, Colors, and “Chart Junk”, Design perspectives

from the experts, The Shaffer 4 C’s of Data Visualization, Not-so-best practices

(examples)Critique and redesign. Creating a good data set for analysis Data modeling

fundamentals for analyticsSelecting data for your KPIs. Advanced Excel Techniques Data

Bars, Sparklines, Box Plots, Mapping, and Bullet Charts. In Depth Design Fundamentals.

Storytelling with Data What are the main approaches to storytelling with data?, Dashboards

vs. Storyboards vs. Infographics, Designing with the user in mind, The Duell Rules for

Actionable Visualizations.


Advanced Tableau Topics: Interactive Visualization Features –build interactive

visualization,

Actions and filters, calculated measures, Data blending, joins, and custom queries, Custom

Shape File. Infographics and other Visualizations, Infographics Examples, OECD Better

Life Index. Geocoding and Mapping: Geocoding Digital Cartographer Eric Fischer and John

Nelson Using geocoded data in Tableau Map Projections.


Advanced Tableau: Advanced Chart types, Custom Color Palettes, WMS Servers, R-

Integration, Business Alignment and Leadership, what are the social aspects of visualization,

BI, and analytics? Creating a vision and alignment among stakeholder.


Trends in Data Visualization and Other Tools, Stanford Visualization Group, Data Wrangler,

D3.js, R and Shiny.

Other Data Visualizations Compare and Contrast real - world examples Flowing Data -

Nathan Yau Information is Beautiful Tableau Vizzes in the wild.

TEXT BOOK:

Donabel Santos Tableau 10 Business Intelligence Cookbook, Packt Publishing,

1786465639, 9781786465634, 2016.

Suggested Reading:

The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting

Data, Facts, and Figures Dona M. Wong, W. W. Norton & Company (2010).

Information Dashboard Design: Displaying Data for At-a-Glance Monitoring

Stephen Few, O’Reilly Media (2013).

Show Me the Numbers: Designing Tables and Graphs to Enlighten Stephen Few, Analytics

Press (2004).

Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics

Nathan Yau, Wiley (2011).

Now You See It, Stephen Few, Analytics Press (2009).

The Visual Display of Quantitative Information, Edward Tufte, Graphics

Press, 2nd Edition (2001).

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