Department of


Syllabus for

1 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MCS131  PROGRAMMING IN JAVA  4  4  100 
MCS132  UNIX OPERATING SYSTEM  4  4  100 
MCS133  DIGITAL LOGIC AND ASSEMBLY LANGUAGE PROGRAMMING  4  4  100 
MCS134  ADVANCED DATABASE MANAGEMENT SYSTEM  4  4  100 
MCS135  DISCRETE MATHEMATICAL STRUCTURES  4  4  100 
MCS136  RESEARCH METHODOLOGY  4  4  100 
MCS151  JAVA PROGRAMMING LAB  4  2  100 
MCS152  UNIX LAB  4  2  100 
2 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MCS231  DATA STRUCTURES  4  4  100 
MCS232  MOBILE APPLICATION  4  4  100 
MCS241A  DIGITAL IMAGE PROCESSING  4  4  100 
MCS241B  SOFTWARE DEFINED NETWORKS  4  4  100 
MCS241C  SOFTWARE QUALITY AND TESTING  4  4  100 
MCS241D  DATA WAREHOUSING AND DATA MINING  4  4  100 
MCS241E  COMPUTER GRAPHICS UISING OPEN GL  4  4  100 
MCS241F  COMPUTER ARCHITECTURE  4  4  100 
MCS242A  CYBER LAW AND IT SECURITY  4  4  100 
MCS242B  OOAD USING UML  4  4  100 
MCS242C  WEB ENGINEERING  4  4  100 
MCS242D  COMPILER DESIGN  4  4  100 
MCS242E  WIRELESS AND MOBILE NETWORKS  4  4  100 
MCS242F  THEORY OF COMPUTATION  4  4  100 
MCS242G  DATA ANALYTICS  4  4  100 
MCS243A  MATLAB PROGRAMMING  4  4  100 
MCS243B  R PROGRAMMING  4  4  100 
MCS243C  NETWORK SIMULATION USING NS3  4  4  100 
MCS243D  HADOOP  4  4  100 
MCS243E  PYTHON PROGRAMMING  4  4  100 
MCS251  DATA STRUCTURES LAB  4  2  100 
MCS252  MOBILE APPLICATION LAB  4  02  100 
MCS271  SEMINAR  2  1  50 
MCS281  ADBMS PROJECT LAB  4  2  100 
3 Semester  2018  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MCS331  DIGITAL IMAGE PROCESSING  4  4  100 
MCS332  MOBILE APPLICATIONS  4  04  100 
MCS333  DESIGN AND ANALYSIS OF ALGORITHMS  4  04  100 
MCS341A  DATA WAREHOUSING AND DATA MINING  4  4  100 
MCS341B  MACHINE LEARNING  4  4  100 
MCS341C  PARALLEL COMPUTING WITH OPEN CL  4  4  100 
MCS341D  SOFTWARE PROJECT MANAGEMENT  4  04  100 
MCS341E  CLOUD COMPUTING  4  4  100 
MCS341F  ARTIFICIAL INTELLIGENCE  4  04  100 
MCS341G  STORAGE AREA NETWORK  4  4  100 
MCS351  DIGITAL IMAGE PROCESSING LAB  4  2  100 
MCS352  MOBILE APPLICATION LAB  4  02  100 
MCS353  SPECIALIZATION PROJECT LAB  4  2  100 
MCS371  RESEARCH  MODELING / IMPLEMENTATION  4  2  50 
MCS372  SEMINAR  2  1  50 
4 Semester  2018  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
MCS451  INDUSTRY PROJECT  2  6  300 
MCS471  RESEARCH PUBLICATION  4  4  100 
 
Assesment Pattern  
Theory Assessment
 
Examination And Assesments  
 
Department Overview:  
Department of Computer Science of CHRIST(Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.  
Mission Statement:  
The Department of Computer Science endeavors to imbibe the vision of the University Excellence and Service. The department is committed to this philosophy which pervades every aspect and functioning of the department.
Mission: To develop IT professionals with ethical and human values.
To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the students to be sociall  
Introduction to Program:  
MSc Computer Science is a 4semester programme which includes the core areas of Computer Science. The objective of the course is to mould students to acquire analytical, creative and problem solving skills to meet the industry standards and be well prepared for research activities.  
Program Objective:  
PSO 1:PROBLEM ANALYSIS AND DESIGN:
Ability to identify, analyze and solve research based interdisciplinary computational challenges.
PSO 2 :CONDUCT INVESTIGATION:
Undertake quality research to contribute academia and industry.
PSO 3:MODERN TOOL USAGE:
Apply their knowledge and experience on modern computing tools and platforms for continuing professional development.
PSO 4:LIFE LONG LEARNING: Adapt to the continuous technological change in computational science and update themselves through lifelong learning process.
PSO 5:SOCIETAL AND ENVIRONMENTAL CONCERN: Utilize the computational knowledge efficiently for societal and environmental concerns .
PSO 6:INNOVATION AND ENTREPRENEURSHIP : Produce innovative IT products and services based on global needs and trends.  
MCS131  PROGRAMMING IN JAVA (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To introduce the concepts and principles of Java Programming language and to design, implement objectoriented solutions to simple and complex problems. To give students experience in Java Programming and program development within an integrated development environment. 

Learning Outcome 

Upon completion of the course, the student will able to CO 1: Understand the principles of objectoriented programming concepts CO 2: Apply logical and critical thinking skills in programming to address the dynamic requirements coming from the Industry CO 3: Design/Develop highly professional applications in Java targeting to the Industry standard

Unit1 
Teaching Hours:12 
Introduction to Java Programming, Language Fundamentals


History of Java, Characteristics of Java, The Java Environment – JVM, JDK & JRE, Different versions of Java, OOP Principles, Comparison of Java with C and C++.  
Unit1 
Teaching Hours:12 
Language Fundamentals


Data Types, Expressions, Keywords, Operators and Control Flow Statements, Structure of Java Program, Creating and Running Java Programs, Arrays.  
Unit1 
Teaching Hours:12 
Class and Objects


Creating class and objects, methods, this keyword, Constructors, Garbage Collection, the finalize () method, Access Control, Static Blocks, Finals, Nested and Inner Classes, String Class and String Buffer Class, Command Line Arguments.  
Unit2 
Teaching Hours:13 
Inheritance in Java


Inheritance in classes, using super, Method overriding, Dynamic Method Dispatch.Abstract Classes, Using final with inheritance, the Object Class.  
Unit2 
Teaching Hours:13 
Interfaces and Packages


Inheritance in java with Interfaces – Defining Interfaces, Implementing Interfaces, Extending Interfaces, Creating Packages, CLASSPATH variable, Access protection, Importing Packages, Interfaces in a Package.  
Unit2 
Teaching Hours:13 
Exception Handling in Java


trycatchfinally mechanism, throw statement, throws statement, Packages and Classes for Exception Handling.  
Unit3 
Teaching Hours:13 
Input / Output in java


java.io package, I/O Streams, Readers and Writers, Using various I/O classes: Reader, Writer, Input Stream and Output Stream, Serialization of objects.  
Unit3 
Teaching Hours:13 
Multithreading


Life cycle of a thread, Java Thread priorities, Runnable interface and Thread Class, sharing limited resources, shared Object with synchronization.  
Unit3 
Teaching Hours:13 
Applets


Life cycle of Applet, Applet Architecture, Applet restrictions, Creation and Execution of Java Applets, Animation in Applets, Advantages of Applets, Applets vs. Applications.  
Unit4 
Teaching Hours:11 
GUI Components (AWT & SWING)


GUI concepts in Java, Basic GUI Components in AWT, Container Classes, Layout Managers, Flow Layout, Border Layout, Card Layout, Box Layout, Difference between AWT and SWING.  
Unit4 
Teaching Hours:11 
SelfLearning SWING


Java foundation Classes – javax. swing and Model View Controller, creating a Frame in Swing, Displaying Image in Swing, JComponent class methods – Creating components in Swing, Writing GUI programs in java (with AWT or SWING), Event Handling – Handling Keyboard Events and Mouse Events.  
Unit5 
Teaching Hours:11 
Database and Client Server Communication


Creating a server that sends data, creating a client that receives data, two way communication between server and client, Stages in a JDBC program, Registering the driver, connecting to a database, Preparing SQL statements, Improving the performance of a JDBC program.  
Text Books And Reference Books: [1] Schildt Herbert, Java: The Complete Reference, Tata McGrawHill, 10th Edition, 2017.  
Essential Reading / Recommended Reading [1] Deitel&Deitel, Java How to Program, Pearson Education Asia, 8th Edition, 2010. [2] Rao Nageswara, Core Java, An Integrated Approach, Dreamtech press, 2^{nd} Edition, 2010.  
Evaluation Pattern CIA  50% ESE  50%  
MCS132  UNIX OPERATING SYSTEM (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The course provides comprehensive understanding of the philosophy and development of the layered architecture of UNIX operating system. It also provides learning opportunities to appreciate the principles and system calls for File System, Process Management, Inter Process Communication, Memory Management and IO System. 

Learning Outcome 

Upon completion of the course,student will be able to CO1: Understand the UNIX Operating System Architecture CO2: Analyze the kernel level data structures used for keeping track of different entities CO3: Identify system calls for file system, process management and Inter Process Communication CO4: Apply memory management concept and IO system principles 
Unit1 
Teaching Hours:12 
Conceptual Foundation of UNIX


History  System Structure  User Perspective  Operating System Services Architecture of UNIX  System Concepts  Kernel Data Structures  Buffer Cache  Principles of Computation through C – Steps in compilation, storage class of variables, memory layout, address binding, address space – logical Vs physical – Concept of a process – Process Control Block.  
Unit2 
Teaching Hours:12 
File System


Internal representation of files – inodes  Structure of a regular files –Directories  Conversion of path name to an inode  Super block  inode assignment to a file  Allocation of disk blocks  System calls for file system open, read, write, lseek, close, file creation, stat and fstat, dup, link, unlink and mounting.  
Unit3 
Teaching Hours:12 
Process Management


Process states and transitions  Layout of system memory  Process context  Process Creation – Process Termination – Waiting for a process – Invoking other programs – System Boot and Init Process – Process Scheduling – Concept of a thread  Thread creation  Thread termination  waiting for a thread.  
Unit4 
Teaching Hours:12 
Inter Process Communication


Process Tracing – Pipe – FIFO  System V IPC  Message queue, Shared memory, Semaphore  Network communications – Socket – Thread Synchronization – Mutex, Condition Variables, Semaphores.  
Unit5 
Teaching Hours:12 
Memory Management and IO Subsystem


Swapping allocation of swap space, swapping processes out, Swapping processes in  Demand Paging Data structures for demand paging, Page fault  Hybrid System. Driver Interfaces – System calls and driver interface, Interrupt handlers, Disk Drivers  Terminal Drivers – Clists, Terminal driver in canonical mode, Terminal driver in raw mode, Terminal polling – Streams.  
Text Books And Reference Books: 1. Bach M.J., “The Design of the Unix Operating System”, Prentice Hall India, reprint 2009. 2. Richard Stevens, “Advanced programming in the UNIX environment “, Addison Wesley, 3rd edition, 2013  
Essential Reading / Recommended Reading 1. Sumitabha Das,” Unix Concepts and Applications”, Tata McGrawHill, 4th edition, 2017.  
Evaluation Pattern CIA  50% ESE  50%  
MCS133  DIGITAL LOGIC AND ASSEMBLY LANGUAGE PROGRAMMING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The proposed course provides a foundation in design and analysis of the operation of digital gates. Design and implementation of combinational and sequential logic circuits. Concepts of Boolean algebra, Karnaugh maps, flipflops, registers, and counters along with various logic families and comparison of their behavior and characteristics are discussed. This course also covers microprocessor basic architecture, various instruction sets. 

Learning Outcome 

Upon successful completion of the course, students should be able to: CO 1: Understand the number systems and conversions from one to another. CO 2: Apply Boolean algebra and methods to simplify the expression using K maps and design logic circuits. CO 3: Understand the concepts and working of sequential circuits and combinational circuits, CO 4: Identify the basic element, functions, and architecture of 8085 microprocessor. CO 5: Develop a microprocessorbased assembly language program. 
Unit1 
Teaching Hours:12 
Introduction


Number System (Binary, Hexadecimal, Octal, Decimal, Binary Arithmetic, Hexadecimal addition & subtraction, BCD addition & subtraction – 1's and 2's Compliment addition and subtraction, Basic gates, Universal gates, Positive and negative logic, Boolean Algebra, Simplification using Boolean Laws. DeMorgan’s Theorem, Karnaugh map, sum of products, PairsQuadsOctets, Karnaugh map simplifications, Don't care conditions. Multiplexers, Demultiplexers, Decoders.  
Unit2 
Teaching Hours:12 
Sequential Circuits


RS FlipFlops, EdgeTriggered RS, D, JK FlipFlops, FlipFlop Timing, JK MasterSlave FlipFlops. Types of Registers, Serial inSerial out, Serial inParallel out, Parallel inSerial out, Parallel inParallel out. Asynchronous Counters, Synchronous Counters, Decade Counters.
 
Unit3 
Teaching Hours:12 
Introduction to Microprocessors


Introduction to Microprocessors Microprocessor Architecture and its operations – Address Bus, Data Bus, Control Bus, Internal data operations and Registers, The 8085 MPU – Architecture, Communication and Bus Timings, Demultiplexing the Bus, Generating Control Signals. Selflearning Memory, I/O Devices.  
Unit4 
Teaching Hours:12 
8085 Programming model


8085 Programming model The 8085 programming model, Instruction classification, Instruction and Data Format, Data Transfer Operations, Arithmetic Operations, Logic Operations, Branch Operations, Writing ALP Programs. Looping, Counting, Indexing, Additional Data Transfer and 16Bit Arithmetic Instructions, Arithmetic operations related to memory, Rotate and Compare of Logic operations. Assembly Language Programming – Addition of two 8bit Hexadecimal numbers, Addition of N Hexadecimal numbers, Interchange N one byte numbers, etc.  
Unit5 
Teaching Hours:12 
Time delays and Interrupts


Counters and Time Delays, Stack, Subroutines, Restart –Simulate a decade counter to count up to 99. The 8085 Interrupt – RST, SIM and RIM Instructions, Multiple Interrupts and Priorities, 8085 Vectored Interrupts – TRAP, RST 7.5, 6.5, 5.5.  
Text Books And Reference Books: [1] Mano, Morris M and Kime, Charles R, Logic and Computer Design Fundamentals, Pearson Education, 4th Edition, 2013 [2] Ramesh.S. Goankar Microprocessor Architecture, Programming & Applications With 8085, Penram International, 5th Edition, 2011.
 
Essential Reading / Recommended Reading [1] Tokheim, Digital Electronics Principles and Applications, Tata McGrawHill, 6th Edition, 2010. [2] Charles M Gilmore, Pal Ajit, Microprocessor Principles and Applications, Tata McGrawHill, 2nd Edition, 1998. [3] Hall. D.V, Microprocessor and Digital System, McGraw Hill Publishing Company, 2nd Edition, 1990.  
Evaluation Pattern CIA50% ESE50%  
MCS134  ADVANCED DATABASE MANAGEMENT SYSTEM (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The Course focus on providing strong foundation for database design and application development and helps in understanding the underlying core database concepts and emerging database technologies. Practical concepts of PLSQL and Document based databases are discussed. 

Learning Outcome 

The successful completion of this course will enable the students to: CO 1: Develop basic understanding of Database management system concepts. CO 2: Apply SQL and PLSQL concepts to solve real world problems. CO3: Analyze the relevance of various advanced database systems and their applications in real world.

Unit1 
Teaching Hours:12 
Conceptual Modeling and Database Design


Using HighLevel Conceptual Data Models for Database Design  Entity Types, Entity Sets, Attributes, and Keys  Relationship Types, Relationship Sets, Roles, and Structural Constraints  Weak Entity Types  ER Diagrams, Naming Conventions, and Design Issues  Relationship Types of Degree Higher than Two  Subclasses, Superclasses, and Inheritance – Enhanced Entity Relationship Model  Relational Database Design by ER and EERtoRelational Mapping  Role of Information Systems in Organizations  Database Design and Implementation Process  
Unit2 
Teaching Hours:12 
Normalization, File Organization and Indexing


Design Guidelines for Relation Schemas  Functional Dependencies  Normal Forms Based on Primary Keys  Second and Third Normal Forms  BoyceCodd Normal Form  Multivalued Dependency and Fourth Normal Form  Join Dependencies and Fifth Normal Form  Inference Rules, Equivalence and Minimal Cover  Properties of Relational Decompositions  Nulls and Dangling Tuples  File Organization  Organization of Records in Files  Ordered Indices  B+ Tree Index Files  Static Hashing  Bitmap Indices  
Unit3 
Teaching Hours:12 
PL/SQL Programming


PL/SQL Block Structure – Identifiers – Literals – Comments  Conditional and Sequential Control  Iterative Processing with Loops  Exception Handlers – Data Retrieval with Cursors  Procedures, Functions, and Parameters – Packages  
Unit4 
Teaching Hours:12 
DocumentOriented Database


Introduction  Documents and Collections  Data Types  Create, Read, Update and Delete Operations  Querying using Find  Query Criteria – TypeSpecific Queries – Where Queries  
Unit5 
Teaching Hours:12 
Emerging Database Technologies and Applications


Mobile databases – Multimedia Databases – Geographic Information Systems – Genome Databases  
Text Books And Reference Books: [1] Elmasri, Navathe, Fundamentals of database systems, Pearson, Sixth Edition, 2014. [2]Korth, Sudershan, Database System Concepts, McGraw Hill, Sixth Edition, 2013. [3]Kristina Chodorow, MongoDB: The Definitive Guide, O’Reilly, Second Edition, 2013. [4]Steven Feuerstei, Oracle PL/SQL Programming, O’Reilly, Sixth Edition, 2014.  
Essential Reading / Recommended Reading [1] Ramakrishnan and Gehrke, Database Management System, McGrawHill, 3rd Edition, 2003. [2] ShashankTiwari, Professional NoSQL, Wrox, Second Edition, 2013.  
Evaluation Pattern CIA  50% ESE  50%  
MCS135  DISCRETE MATHEMATICAL STRUCTURES (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course description: This is a fundamental course in Discrete Mathematics involving Mathematical Logic, functions, Divisibility, Congruences, Graph Theory and Group Theory. Course objectives: To prepare the students for a background in abstraction, notation, and critical thinking in the Discrete Mathematics topics which are closely related to Computer Science. 

Learning Outcome 

The successful completion of this course will enable the students to: CO1: construct mathematical arguments using logical connectives and quantifiers. CO2: verify the correctness of an argument using propositional and predicate logic and truth tables. CO3: understand how Graphs are used as tools and Mathematical Models in the study of networks. CO4: apply algorithms and use definitions to solve problems to prove statements in elementary number theory. CO5: perform operations on discrete structures such as sets, relations and functions and be familiar with concepts like Groups and Rings. 
Unit1 
Teaching Hours:15 

Foundations


How to do Mathematics? – Compound statements – Existential and Universal statements – Proof techniques – Logical operations – Logical equivalence Conditional statements – Universal and Existential quantifiers – Concept of a function – Types of functions – Composition of functions.  
Unit2 
Teaching Hours:15 

Techniques


Introduction to numbers – Divisibility – Greatest common divisor – Existence and uniqueness of prime factorization – Partition of a set – Partition of a positive integer –Even and odd permutations – modular arithmetic – Latin squares.  
Unit3 
Teaching Hours:15 

Networks


Types of relations – Graphs as network – Types of graphsRepresentation of graphs – Representation of relations through graphs – Paths and Cycles Eulerian and Hamiltonian properties of paths – Equality of graphs – Trees – Coloring of graphs – MaxFlow –MinCuttheorem.  
Unit4 
Teaching Hours:15 

Algebraic Structures


Groups – Axiom of a group – Examples and basic algebra in groups – Order of an element of a group – Isomorphism of groups – Cyclic groups – Subgroups – Cosets and Lagrange’s theorem – RingsFields.  
Text Books And Reference Books: N L Biggs, Discrete Mathematics, Oxford University Press, New Delhi, 2nd Edition, 2003.  
Essential Reading / Recommended Reading R. P. Grimaldi, Discrete and Combinatorial Mathematics, Pearson education, 5th Edition, 2004. B. Kolman, R. C. Busby and S. C. Ross, Discrete Mathematical Structures, Pearson Education, 5th Edition, 2004. T. Koshy, Discrete Mathematics with Applications, Elsevier Academic Press,London, 2004. K. H. Rosen, Discrete Mathematics and Its Applications, Tata McGrawHill, 6th Edition, 2006. G.S. Rao, Discrete Mathematical Structures, New Age International, 2009. J. P. Trembly and R. Manohar, Discrete Mathematics with Applications to Computer Science,Tata McGrawHill, 2003.  
Evaluation Pattern
 
MCS136  RESEARCH METHODOLOGY (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The research methodology module is intended to assist students in planning and carrying out research projects. The students are exposed to the principles, procedures and techniques of implementing a research project. The course starts with an introduction to research and carries through the various methodologies involved. The process involeved in research from Identifying and wrinting problem statement, conducting organized literature survey, basic statistics required for research and ends with linear regression.


Learning Outcome 

Upon completion of the course, the student will be able to CO1: Understand the acquired knowledge to select and formulate research problem statement effectively. CO2: Analyze and apply the concept of collected relevant literature. CO3: Investigate various statistical techniques to propose a research design to find a solution for a research problem CO4: Demonstrate the proficiency to write an appropriate research article. 
Unit1 
Teaching Hours:12 
Research Methodology


Defining research problem  selecting the problem  necessity of defining the problem  techniques involved in defining a problem Ethics in Research  
Unit2 
Teaching Hours:12 
Research Design


Principles of experimental design: Working with Literature Importance, finding literature, using your resources, managing the literature, keep track of references, using the literature, literature review Online Searching: Database – SciFinder – Scopus  Science Direct  Searching research articles  Citation Index  Impact Factor  Hindex etc,  
Unit3 
Teaching Hours:12 
Research Data


Measurement of Scaling: Quantitative, Qualitative, Classification of Measure scales, Data Collection, Data Preparation.  
Unit4 
Teaching Hours:12 
Statistics


Descriptive Statistics: Measures of Central Tendency, Measures of Dispersion, Measure of Skewness, Kurtosis, Measure of Relationship. Linear Regression Analysis: Dependent and Independent variables, Simple Linear Regression Model.  
Unit5 
Teaching Hours:12 
Report Writing


Scientific Writing and Report Writing: Significance, Steps, Layout, Types, Mechanics and Precautions, Latex: Introduction, text, tables, figures, equations, citations, referencing, and templates (IEEE style), paper writing for international journals, Writing scientific report.  
Text Books And Reference Books: [1] C. R. Kothari, Research Methodology Methods and Techniques, 3rd. ed. New Delhi: New Age International Publishers, Reprint 2014. [2] Zina O’Leary, The Essential Guide of Doing Research, New Delhi: PHI, 2005.  
Essential Reading / Recommended Reading [1] J. W. Creswell, Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th. ed. SAGE Publications, 2014. [2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 3rd. ed. Indian: PE, 2010.  
Evaluation Pattern Exercises: • Review 1 PreSelected Papers (U: Unique for each student) • Review 1 papers in areas of scholars choice (U) • Using an IEEE MS Word Template and convert literature review done in previous paper reviews. Follow Reference Styles • Gather data from Wikipedia and populate a spreadsheet. (U) • Using the data collected, analyze the data using 7 different spreadsheet statistics functions. • Script a paper in IEEE LATEX template(U) Evaluation Pattern CIA for this course is similar to other regular courses in the curriculum as per university guidelines. Additionally, students have to maintain a record of the following: • Papers reviewed (minimum 5 papers) • Data collected (7 different graphs) • Analysis of data • Statistical functions
ESE is conducted by the department during which each student should come up with a research proposal of any domain of his/her interest. Students should incorporate the research objectives and methodologies along with justification for why those methodologies were suggested to achieve the objectives. Each student is evaluated based on a presentation that highlights the application of various research methodology concepts learnt in the semester. Evaluation Rubrics S. NoCriteria for Evaluation Marks 1CIA I, Mid Semester Examination (MSE), CIA III 45 2End Semester Examination (ESE) 50 3Attendance 5  
MCS151  JAVA PROGRAMMING LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

To introduce the concepts and principles of Java Programming language and to design, implement objectoriented solutions to simple and complex problems. To give students experience in Java Programming and program development within an integrated development environment. 

Learning Outcome 

Upon completion of the course, the student will able to CO 1: Understand the features of Object Oriented Programming Languages CO 2: Design / Develop highly professional applications in Java targeting to the Industry standards CO 3: Apply logical and critical thinking skills in developing applications to address the dynamic requirements coming from the Industry 
Unit1 
Teaching Hours:60 
Section A


1. Demonstrate various data types and operators. 2. Demonstrate method overloading and constructor overloading. 3. Demonstrate the usage of static keyword in Java – use static data and static block. 4. Demonstrate the final keyword with respect to variable, method, and class. 5. Demonstrate inner classes in Java. 6. Demonstrate multilevel inheritance and usage of the keywords this & super. 7. Demonstrate an abstract class. 8. Demonstrate the usage of interface for multiple inheritances. 9. Differentiate the usage of throw, throws, and trycatchfinally by writing a Java program.
 
Unit1 
Teaching Hours:60 
Section B


10. Demonstrate various I/O streams in Java. 11. Demonstrate the Reader/Writer classes in Java. 12. Demonstrate the multithreading concept by implementing Runnable interface. 13. Demonstrate the multithreading concept by extending Thread class. 14. Make some graphics in an applet program using paint function. 15. Demonstrate the usage of different Layouts in Java. 16. Demonstrate various GUI components in Java (AWT / SWING) with appropriate Event Handling. 17. Implement two way communication between server and client. 18. Retrieve data from the table of the database.
 
Text Books And Reference Books: [1] Schildt Herbert, Java: The Complete Reference, Tata McGrawHill, 10th Edition, 2017.
 
Essential Reading / Recommended Reading [1] Deitel&Deitel, Java How to Program, Pearson Education Asia, 8th Edition, 2010. [2] Rao Nageswara, Core Java, An Integrated Approach, Dreamtech press, 2^{nd} Edition, 2010.
 
Evaluation Pattern CIA50% ESE50%  
MCS152  UNIX LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

The course provides comprehensive understanding of the philosophy and development of the layered architecture of UNIX operating system. It also provides learning opportunities to appreciate the principles and system calls for File System, Process Management, Inter Process Communication, Memory Management and IO System.


Learning Outcome 

Upon completion of the course , the student will be able to CO1: Understand the UNIX Operating System Architecture CO2: Analyse system calls for file system, process management ,kernel working and Inter Process Communication CO3: Apply memory management concepts and IO system principles 
Unit1 
Teaching Hours:60 
Section A


1. Write a shell script to print prime numbers up to a given range using arguments 2. Write a shell script to find out the factorial of a number using functions 3. Write a shell script to count the number of lines in a file accepted as command line argument 4. Write a shell script which merge the contents of file1, file2, file3, sort them and display the sorted output on the screen page by page 5. Write a shell script to locate users who have logged in today or earlier but have not logged out and mail the list to root. Users who have logged more than once should appear in the list only once 6. Write a program to demonstrate open, read, write and close system calls 7. Write a program to demonstrate dup, link, unlink and lseek system calls 8. Write a program to demonstrate fork and wait / waitpid system calls 9. Write a program to demonstrate exec family of system calls 10. Write a program to demonstrate thread creation, waiting and termination of threads
 
Unit1 
Teaching Hours:60 
Section B


1. Write program to demonstrate file descriptor behavior in fork and exec family system calls 2. Write a program to demonstrate twoway data transfer between processes using pipes 3. Write a program to demonstrate twoway data transfer between processes using FIFOs 4. Write a program to demonstrate twoway data transfer between processes using message queues 5. Write a program to demonstrate twoway data transfer between processes using shared memory 6. Write a program to implement a iterative server and client using TCP sockets 7. Write a program to implement a forked concurrent server and client using TCP sockets 8. Write a program to implement a iterative server and client using UDP sockets 9. Write a program to demonstrate thread synchronization using mutex 10. Write a program to demonstrate thread synchronization using semaphore  
Text Books And Reference Books: 1. Bach M.J., “The Design of the Unix Operating System”, Prentice Hall India, reprint 2009. 2. Richard Stevens, “Advanced programming in the UNIX environment “, Addison Wesley, 3rd edition, 2013
 
Essential Reading / Recommended Reading 1. Sumitabha Das,” Unix Concepts and Applications”, Tata McGrawHill, 4th edition, 2017.
 
Evaluation Pattern CIA50% ESE50%  
MCS231  DATA STRUCTURES (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Data Structure is considered as one of the fundamental subjects 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 

Learning Outcome 

The successful completion of this course will enable the students to: CO 1: Understand the need for Data Structures when building applications. CO 2: Analyse the efficiency to optimize the algorithms CO 3: Apply algorithms for linear and nonlinear data structures. CO 4: Implement various data structures and their algorithms for real time applications. 
Unit1 
Teaching Hours:12 
Introduction and overview, Stacks and Queues


Introduction and overview Introduction, Basic Terminology, Data Structures, Operations, Algorithms: Time & Space Complexity, Algorithmic Notation, Abstract Data Types. Programming standards and ethics. Stacks and Queues Stacks, Array Representation, Arithmetic Expressions, Polish Notation, Application of Stacks, Recursion, Towers of Hanoi, Implementation of Recursive procedures by Stack, Queues, Queue Array RepresentationIntroduction and overview
Introduction, Basic Terminology, Data Structures, Operations, Algorithms: Time & Space Complexity, Algorithmic Notation, Abstract Data Types. Programming standards and ethics.
Stacks and Queues Stacks, Array Representation, Arithmetic Expressions, Polish Notation, Application of Stacks, Recursion, Towers of Hanoi, Implementation of Recursive procedures by Stack, Queues, Queue Array Representation.  
Unit2 
Teaching Hours:11 
Linked Lists


Introduction, Linked lists and Memory Representation, Traversing, Searching, Memory Allocation, Garbage Collection, Insertion, Deletion, Circular Linked list, Twoway Lists (Doubly). Linked List Implementation of Stack and Queue Self Learning Infix to Prefix
 
Unit3 
Teaching Hours:12 
Sorting, Searching


Sorting Introduction, Sorting, Insertion Sort, Selection Sort, Shell Sort, Merging, MergeSort, Quick Sort, Radix Sort, External Sorting. Searching Hashed List Searches: Hashing Methods  Direct method, Subtraction Method, Modulodivision Method, Digitextraction Method, Midsquare Method, Folding Method, Rotation Method, Pseudorandom Hashing. Collision Resolution – Open addressing, Linear Probe, Quadratic Probe, Pseudorandom Collison Resolution, Linked List Collision Resolution, Bucket Hashing, Combination Approaches. Text Searching using KnuthMorrisPratt algorithm.  
Unit4 
Teaching Hours:12 
Trees, Balanced Tree


Trees Introduction, Binary Trees, Representing Binary Trees in memory, Traversing Binary Trees, Traversal Algorithms, Binary Search Trees, Searching, Inserting and deleting in Binary Search Trees, Heap, Heap sort, Huffman’s Algorithm. Balanced Tree AVL Trees: AVL Balance Factor, Balancing Trees, AVL node structure, AVL Tree Rotate Algorithms. Self learning Heap, Heap Sort, splay and Red Black tree.  
Unit5 
Teaching Hours:13 
Multiway Search Trees, BTrees, Graphs


Multiway Search Trees, BTrees BTrees: BTree insertion, Deletion, Traversal and Search algorithm, Simplified B Trees, 23 Tree, 234 Tree, Variations of B Tree  B+ Tree, B* Tree. Graphs Graph Theory Terminology, Sequential representation of Graphs, Adjacency matrix, Path matrix, Linked representation of a Graph, Operations on Graphs, Depth First and Breadth First Traversing a Graph, Minimum Spanning Tree Algorithm.
 
Text Books And Reference Books: [1] Gilberg, F Richard &Forouzan, A Behrouz, Data Structures A Pseudocode approach with C,2nd Edition, Cengage, 2008.
 
Essential Reading / Recommended Reading [1] Horowitz Sahni AndersonFreed, Fundamental of Data Structures in C, Universities Press, Reprint 2008. [2] Richard Johnsonbaugh, Algorithms, Pearson Education, 2nd Edition, 2008 [3] Robert Sedgwick, Algorithm in C++, AddisonWesley Publishing Company. [4] Knuth, Donald E, Art of Computer Programming, Sorting & Searching, AddisonWesley, 2005.
 
Evaluation Pattern CIA50% ESE50%  
MCS232  MOBILE APPLICATION (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This Course Description aimed at helping learners create applications using Google's Android™ open source platform. The Course Description explains what Android™ is and how it compares to other mobile environments, the setup of the Android™ Eclipsebased development tools, the Android™ SDK, all essential features, as well as the advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS. 

Learning Outcome 

Upon successful completion of the course student will be able to CO 1: Build your own Android apps. CO 2: Explain the differences between Android and other mobile development environments. CO 3: Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus. CO 4: Secure, tune, package and deploy Android Applications. 
Unit1 
Teaching Hours:12 

Introduction


Brief History of Embedded Device Programming, Introduction to Android, Get to know the required tools, Creating your first Android application, Anatomy of android Application. Understanding Activities, linking Activities using intents, fragments, calling Builtin Applications using Intents, Displaying Notifications.  
Unit2 
Teaching Hours:12 

User Interface and Designing with views


Understanding the components of a screen, adapting to display orientation, managing changes to screen orientation, Utilizing the Action Bar, Creating the user Interface programmatically, Listening for UI Notifications. Using Basic Views, Using Picker views, Using List views to display lists, Understanding specialized fragments.  
Unit3 
Teaching Hours:12 

Displaying with views, Data persistence and Content Providers


Using Image Views to display pictures, using menus with views, some additional views. Saving and loading user preferences, persisting Data Files, Creating and using Databases. Sharing Data in Android, using content provider, creating your own content providers, using content providers.  
Unit4 
Teaching Hours:12 

Messaging and Location based services


SMS Messaging, Sending Email, Displaying Maps, Getting Location Data, Monitoring a Location. Hands on project: Building a Location Tracker  
Unit5 
Teaching Hours:12 

Creating Services


Creating your own services, Establishing Communications between a service and an activity, binding activities to services Self Learning understanding Threads, Preparing for Publishing, Deploying APK Files.
 
Text Books And Reference Books: [1] WeiMeng Lee, “Beginning android 4 application Development, John Wiley & sons, Inc, 2012.  
Essential Reading / Recommended Reading [1] Paul DeitelHarvey DeitelAbbey DeitelMichael Morgano,” Android for Programmers An AppDriven Approach”, Pearson Education Inc., 2012. [2] Jerome (J.F) DiMarzio, "Android  A programmer's Guide", Tata McGraw Hill,2010, ISBN: 9780071070591  
Evaluation Pattern
 
MCS241A  DIGITAL IMAGE PROCESSING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The main objective of this course is to give strong foundation on concepts and algorithms widely used in Digital image processing. It provides handson experience for processing images using various techniques. 

Learning Outcome 

Upon successful completion of the course the student would: CO1: Comprehend the knowledge of image processing and image analysis techniques 
Unit1 
Teaching Hours:12 

Introduction and Digital Image Fundamentals


The origins of Digital Image Processing, Fundamental Steps in Image Processing, Elements of Digital Image Processing System, Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.  
Unit2 
Teaching Hours:12 

Image Enhancement in Spatial Domain & Frequency Domain


Image Enhancement in Spatial Domain Gray Level Transformations, Histogram Processing, Histogram equalization, Histogram specification, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters. Image Enhancement in Frequency Domain Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters. Self Learning Homomorphic Filtering  
Unit3 
Teaching Hours:12 

Image Restoration and Image Compression


A model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering. Image Compression models: Huffman coding, Run length coding, LZW coding.  
Unit4 
Teaching Hours:12 

Image Segmentation and Representation


Point, Line and Edge detection. Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method. Region Based Segmentation – Region Growing and Region Splitting and Merging. Representation – Chain codes, Self Learning Polygonal approximations using minimum perimeter polygons.  
Unit5 
Teaching Hours:12 

Description and Object Recognition


Boundary descriptors – Fourier descriptors. Regional descriptors –Topological descriptors and Moment invariants. Introduction to Patterns and Pattern Classes. DecisionTheoretic Methods – Minimum distance classifier, KNN classifier and Bayes’ Self Learning classifier  
Text Books And Reference Books: [1] R. C. Gonzalez & R. E. Woods, Digital Image Processing, 3rd Edition.Pearson Education, 2009. [2] A.K. Jain, Fundamental of Digital Image Processing, 4th Edition.P HI, 2011. [3] Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, Digital Image Processing Using MATLAB, 2nd Edition. PHI, 2009.  
Essential Reading / Recommended Reading [1] M. A. Joshi, Digital Image Processing: An algorithmic approach, 2nd Edition. PHI 2009. [2] B. Chanda, D. Dutta Majumdar, Digital Image Processing and analysis, 1st Edition, PHI,2011.  
Evaluation Pattern
 
MCS241B  SOFTWARE DEFINED NETWORKS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The purpose of this course is to introduce the students with basics of computer networks. The concepts covered throughout the semester includes topics related to SDN (Software Defined Networking), a new era in networking that is influencing the modern network as well as changing the future of networks and services. During the study of this course, student will learn how SDN made a paradigm shift in telecommunications and networking that is one of the rare tectonic shifts in an industry in the last 30 years. One of the goals of this subject is to prepare attendees for a market that is going to demand computer scientists and software engineers to deliver the next generation of software centric networks. 

Learning Outcome 

CO1: Understand the fundamentals of SDN and the characteristics of SDN controllers and their implications. CO2: Apply SDN for security and scalability. CO3: Dvelop SDN for real time applications. 
Unit1 
Teaching Hours:12 

Introduction to Computer Networks, Protocols and Network Programming


Introduction to the OSI stack and network protocols. Understand the mechanisms of network elements (switches, routers). How do legacy networks work? Introduction to VPN and MPLS. Identify the shortcomings of today’s legacy networks. Birth of network programmability and SDN.  
Unit2 
Teaching Hours:12 

Introduction to SDN Networks


SDN Architecture. OpenFlow switch. Traffic matching with traditional networks. OpenFlow. Ports. Control and data plane separation. Centralization of control. Introduction to controllers. Southbound interfaces (e.g. OpenFlow). Virtualization. NBI. SBI. Abstraction. PCEP. What is NFV? Spanning Tree.  
Unit3 
Teaching Hours:12 

SDN Tools


VMware, Controllers, Ryu, OpenDaylight, Floodlight, ONOX,POX. Analyzing tool: Wireshark and Gnuplot. Topology creation using Mininet. Customizing topology with own python script to run your own network topology and connect to the controller. Capture OpenFlow message format and message sequence. Testing communication between the hosts. IPerf and Gnuplot.  
Unit4 
Teaching Hours:12 

Large scale Wireless SDNs:


Running OFM on top of ODL. ACL with help of OFM. Large topology generation using GNS3, Docker container and VM. Multiple controllers on single network. Using WiFiMininet generate wireless SDN Networks. SDWAN  
Unit5 
Teaching Hours:12 

SDN Applications


Real World SDN –Google, Real World SDN – Microsoft, Real World SDN – NSA, Real World SDN – Facebook, Linux on switches, Analyze which SDN Controller should be used in which scenario.  
Text Books And Reference Books: 1. Software Defined Networking, Network Function Virtualization, and Quality of Experience: Foundations of Modern Networking, William Stallings, AddisonWesley, (2015). 2. Software Defined Networks: A Comprehensive Approach, Paul Goransson, Chuck Black, Morgan Kaufmann; 1 edition (June 6, 2014).  
Essential Reading / Recommended Reading 1. Network Programmability and Automation, Volume 1 (Networking Technology), by Khaled Abuelenain and Karim Okasha, (Jan,2019). 2. SDN a Complete Guide, by Gerardus Blokdyk, 2019 edition (Dec 21, 2018).
Web Resources: 1.https://www.opennetworking.org/sdndefinition/ 2.https://www.sdxcentral.com/sdn/definitions/whatthedefinitionofsoftwaredefinednetworkingsdn/  
Evaluation Pattern
 
MCS241C  SOFTWARE QUALITY AND TESTING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

This Course helps the student to understand the need for Software Quality, Tools Used and Metrics involved. It focus on the learning of software testing principles and methods to detect the ever changing software technological changes.


Learning Outcome 

Student will be able to : CO1: Understand the concepts of Software Quality framework,standards and Measurement theory. CO 2: Apply the different Software Quality Tools. CO 3: Analyze the advantages of Extreme Testing and High Order Testing CO 4: Create effective test plan and test cases.

Unit1 
Teaching Hours:12 
Introduction to Software Quality, Framework and Quality Standards


Quality: popular view, Quality: professional view, software quality, total quality management, The defect prevention process, process maturity framework and quality standards (CMM, SPR Assessment, Malcolm Bridge, ISO9000)  
Unit2 
Teaching Hours:12 
Fundamentals in Measurement Theory and Metrics


Fundamentals in Measurement Theory Levels of measurement some basic measures, reliability and validity Software quality metrics Product Quality Metrics, inprocess quality process, example of Metrics Program – Motorola, HP  
Unit3 
Teaching Hours:12 
Basic Quality Tools and Defect Removal


Seven Basic Quality Tools Ishikawas’ seven basic tools, checklist, pareto diagram, histogram, runchart, scatter diagram control chart cause and effect diagram. Defect Removal Effectiveness Literature review, a close look at DRE, defect removal effectiveness and quality planning
 
Unit4 
Teaching Hours:13 
Fundamentals of Software Testing


Software Testing Principles, Economics of Testing Inspection and walkthrough, code inspection, an error checklist for Inspection, Walkthroughs, Desk Checking, Peer Rating Module Testing Self Study Two testing tools  
Unit5 
Teaching Hours:12 
High Order Testing, Debugging and Extreme Testing


High Order Testing  Debugging by Brute Force, Induction, Deduction, Backtracking Extreme Programming basics, Extreme Testing, Extreme Testing Applied Java Unit Testing, Automatic testing  
Text Books And Reference Books: [1] Stepen H Kan, Metrics and Models in Software Quality Engineering, 2nd Edition, reprint 2006 [2] S A Kelkar, Software Quality and Testing, PHI, 1st Edition, 2012.
 
Essential Reading / Recommended Reading [1] GlenfordJ.Myers, The Art of Software Testing” John Wiley and Sons publications, 2004
 
Evaluation Pattern CIA50% ESE50%  
MCS241D  DATA WAREHOUSING AND DATA MINING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course is designed to introduce the core concepts of data mining, techniques, implementation, benefits, and outcome expectations from this new technology and to understand the concepts of Data Warehouse system. 

Learning Outcome 

Upon successful completion of the course, the Students will able to CO1:Demonstrate basic data mining algorithms, methods, tools and OLAP. CO3:Build the Data warehouse for real time applications. 
Unit1 
Teaching Hours:12 
Introduction to data Mining


Data Mining – Process and architecture  Kinds of Data to be mined  Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Major Issues in Data Mining.  
Unit1 
Teaching Hours:12 
Data Preprocessing


Preprocessing  Descriptive Data Summarization – Measuring the central tendency Measuring the dispersion of data.  
Unit2 
Teaching Hours:12 
Data Mining Algorithms


Association Rule Mining: Basic Concepts, Efficient and Scalable Frequent Item set Mining Methods – Apriori algorithm.  
Unit2 
Teaching Hours:12 
Data Preprocessing (cont.,)


Data Cleaning  Missing Values – Noisy Data  Data Cleaning as a Process  Data Integration and Transformation  Data ReductionData Cube AggregationAttribute Subset Selection. Demo: Preprocessing can be done using WEKA tool.  
Unit3 
Teaching Hours:12 
Generating Rules


Generating Rules – Improving efficiency – Mining frequent item set without candidate generation. Classification and Prediction: Issues Regarding Classification and Prediction, Accuracy and Error Measures.  
Unit3 
Teaching Hours:12 
Cluster Analysis


Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – KMeans and KMedoids, Hierarchical Methods – Agglomerative and Divisive.  
Unit3 
Teaching Hours:12 
Demo


Classification and clustering analysis can be done using WEKA tool.  
Unit4 
Teaching Hours:12 
Building a data warehouse


Business considerations, Design considerations, technical considerations, implementation considerations, integrated solutions, benefits of data warehousing, Relational data base technology for data warehouse, database architectures for parallel processing, parallel RDBMS features, alternative technologies.  
Unit5 
Teaching Hours:12 
DBMS schemas for decision support


Data layout for best access, multidimensional data model, star schema, STARjoin and STARindex, bitmapped indexing, column local storage, complex data types, Data extraction, clean up and transformation toolstool requirements, vendor approaches, access to legacy data, vendor solutions, transformation engines  
Text Books And Reference Books: [1] Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, USA, 2^{nd} Edition, 2012. [2] Kimball, Ralph & et al, The Data Warehouse Lifecycle Toolkit, John Wiley &Sons, 2013.  
Essential Reading / Recommended Reading [1] Berson Alex, Stephen J Smith, “Data Warehousing, Data Mining and OLAP”, TATA McGrawHill, 13th reprint 2017.  
Evaluation Pattern CIA  50% ESE  50%  
MCS241E  COMPUTER GRAPHICS UISING OPEN GL (2019 Batch)  
Total Teaching Hours for Semester:100 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To familiarize the students with the concepts of computer graphics like line, circle drawing algorithms, transformations, clipping, projection, color models, curves. To make the students understand how to implement the computer graphics concepts using OpenGL. 

Learning Outcome 

At the conclusion of course, students are able to: CO 1: Demonstrate the concepts of Computer Graphics. CO 2:Analyze the different algorithms and models in Graphics.
CO 3: Implement the Graphics concepts using OpenGL. 
Unit1 
Teaching Hours:12 
Introduction to Computer Graphics


Applications, Overview of Graphics Systems – Video display devices, Rasterscan systems, Graphics software, Introduction to OpenGL. Graphics Output Primitives Coordinate Reference Frames, TwoDimensional frame in OpenGL, Point Functions, Line Functions, LineDrawing Algorithms – DDA, Bresenhams, Curve Functions, Midpoint Circle Algorithm, and Displaywindow reshape function.
SelfLearn: Area filling, Display lists, Basic colors, Attribute functions.
 
Unit2 
Teaching Hours:12 
Geometric Transformations


Basic twodimensional geometric transformations, Homogeneous Coordinates, Composite transformations, Geometric transformations in threedimensional space, Translation, Rotation, scaling, composite threedimensional transformations, OpenGL geometric transformation functions. SelfLearn: Reflection, shear.  
Unit3 
Teaching Hours:12 
Illumination and Color Models


Light sources, Basic illumination models, transparent surfaces, OpenGL illumination functions. Color Models, Standard primaries and chromaticity diagrams, RGB color model, HSV color model. OpenGL color functions. SelfLearn: Raytracing and Texture mapping.
 
Unit4 
Teaching Hours:12 
Viewing


Twodimensional viewing pipeline, clipping window, Normalization and viewport transformations, 2D viewing functions, Clipping Algorithms – Line clipping – Cohen Sutherland and LiangBarsky Line clipping, polygon clipping – SutherlandHodgman algorithm. Threedimensional viewing concepts – Projections, Threedimensional viewing pipeline, Projection transformation, Parallel and Perspective projection matrices. 3D viewing functions. SelfLearn: Other clipping algorithms, Text clipping, and Projection derivations.  
Unit5 
Teaching Hours:12 
Threedimensional Object Representations


Spline representations, Cubic spline interpolation methods, Bezier curves and BSpline curves. OpenGL approximationSpline functions.
 
Text Books And Reference Books: [1] D. Hearn, M. Pauline Baker, Computer Graphics with OpenGL. PHI, 3rd Edition, New Delhi, 2011.  
Essential Reading / Recommended Reading [1] Foley, Vandam&Feiner, Hughes, Computer Graphics Principles &Practice, in C, Pearson Education (Singapore Pvt Ltd, Indian Branch, Delhi), 6th Indian Reprint 2001. [2] Richard S Wright, Jr. Michael Sweet, Open GL Super Bible, 2nd Edition. [3] Woo, Mason and Neider, Jackie, Open GL Programming guide.  
Evaluation Pattern CIA  50% ESE  50%  
MCS241F  COMPUTER ARCHITECTURE (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To enable the students to learn the basic functions, principles and concepts of Computer Architecture, learn the fundamental aspects of Computer Architecture and design, focus on processor design, control unit design techniques and study on I/O interfacing.


Learning Outcome 

On successful completion of the course the students should have CO 1: Demonstrate the fundamentals of Computer Architecture. CO 2: Understand the working of number systems, I/O, Registers and Memory. CO 3: Analyze the concepts of processor design, control unit design and I/O interfacing.

Unit1 
Teaching Hours:13 
Computer system and Memory


Computer components – computer function – instruction fetch and execute – interrupts – I/O functions – interconnection structures – Bus interconnection – Bus structure – multiple bus hierarchies – elements of bus design. Memory Computer memory system overview – characteristics of memory system – memory hierarchy – cache memory principles – elements of cache design cache size – mapping function – replacement algorithms – write policy – internal memory semiconductor memory – organization – DRAM and SRAM – types of ROM – chip logic – external memory – magnetic disk magnetic read write mechanisms – data organization and formatting – physical characteristics – disk performance parameters – RAID – optical memory. Self Learning: Introduction to Magnetic disks, optical memory.
 
Unit2 
Teaching Hours:11 
Input/output organization


External devices – I/O modules – programmed I/O – interrupt driven I/O – DMA – I/O processor – interface circuits – serial port – parallel port – standard I/O interfaces – PCI bus, SCSI bus, USB bus.  
Unit3 
Teaching Hours:12 
Computer arithmetic


The arithmetic and logic unit – integer arithmetic – negation – addition – subtraction – multiplication and division – floating point representation – principles – IEEE standard for binary floating point representation – floating point arithmetic addition and subtraction – multiplication and division – precision considerations.  
Unit4 
Teaching Hours:12 
Central processing unit


Instruction sets characteristics – types of operands – types of operations – addressing modes – instruction formats  processor organization – register organization – instruction cycle – instruction pipelining reduced instruction set architecture – RISC verses CISC Case study: Pentium and power PC data types – operation types – addressing modes.
 
Unit5 
Teaching Hours:12 
Control unit


Control unit operations – micro operations – fetch cycle – indirect cycle – interrupt cycle – execute cycle – instruction cycle – control of the processor – functional requirements – control signals – hardwired implementation – control unit inputs and control unit logic – micro programmed control Basic concepts – Micro instructions – micro programmed control unit – micro instruction sequencing design considerations – sequencing techniques – address generation –micro instruction execution – micro instruction encoding.
Service learning Ewaste Management
 
Text Books And Reference Books: [1] William Stallings, Computer Architecture and Organization, Pearson Education, 7th Edition, 2010.  
Essential Reading / Recommended Reading [1] Carl Hamacher, ZvonkoVranesic and SafwatZaky, Computer Organization, 5th Edition, Tata McGraw Hill, 2011. [2] David A. Patterson and John L. Hennessy, Computer Organization and Design: The Hardware/Software Interface, Elsevier, 2008. [3] John P. Hayes, Computer Architecture and Organization, McGraw Hill, 3^{rd} Edition, 2002. [4] Vincent P. Heuring and Harry F. Jordan, Computer Systems Design and Architecture, Pearson Education, 2nd Edition, 2004. [5] M. Morrris Mano, Computer system architecture, Pearson Education, 3rd Edition, 2005.
 
Evaluation Pattern CIA ESE 50% 50%  
MCS242A  CYBER LAW AND IT SECURITY (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To understand the dynamics of cyber security and cyber law matrix, Creating technolegal professionals with the blended skills of law and technology. 

Learning Outcome 

The Course will assist students in CO 1: Understand the principles of cyber security and cyber law matrix. CO 2: Analyzing the legal issues involved in various problems and conflicts commonly encountered online CO 3: Identify the Egovernance and thedigital signature concepts 
Unit1 
Teaching Hours:12 
Cryptography


Object and Scope of the IT Act, Genesis, Object, Scope of the Act, Encryption Symmetric Cryptography, Asymmetric Cryptography, RSA Algorithm, Public Key, Encryption.  
Unit2 
Teaching Hours:12 
Digital Signature


Digital Signature: Technology behind Digital Signature, Creating a Digital Signature, Verifying a Digital Signature, Digital Signature and PKI, Digital Signature and the Law  
Unit3 
Teaching Hours:12 
EGovernance


EGovernance and IT Act 2000: Legal recognition of electronic records, Legal recognition of digital signature, Use of electronic records and digital, signatures in Government and its agencies. Certifying Authorities: Need of Certifying Authority and Power, Appointment, function of Controller, Who can be a Certifying Authority? Digital Signature Certifications, Generation, Suspension and Revocation of Digital Signature Certificate.  
Unit4 
Teaching Hours:12 
Domain Name Disputes and Trademark Law


Concept of Domain Names, New Concepts in Trademark, Jurisprudence, Cyber squatting, Reverse Hijacking, Meta tags, Framing, Spamming, Jurisdiction in Trademark Dispute. Cyber Regulations Appellate Tribunal, Establishment & Composition of Appellate Tribunal, Powers of Adjudicating officer to Award Compensation, Powers of Adjudicating officer to Impose Penalty.  
Unit5 
Teaching Hours:12 
The Cyber Crimes


Tampering with Computer Source Documents, Hacking with Computer System, Publishing of Information Which is Obscene in Electronic Form, Offences: Breach of Confidentiality & Privacy, Offences: Related to Digital Signature Certificate.  
Text Books And Reference Books: [1] David Baumer, J.Poindexter, Cyberlaw and ECommerce: A Primer, McGrawHill Publishing Co, 2005, ISBN13: 9780071123006 [2] Cyber Law in India by Farooq Ahmad – Pioneer Books,2017.  
Essential Reading / Recommended Reading [1] Law relating to computers, Internet and ecommerce: A guide to cyber laws / Nandan Kamath. Delhi: Universal Law Publishing Co. Pvt. Ltd., 2000. [2] Cyber laws: For every netizen in India (With Information Technology Bill 1999) / NA Vijayashankar.  Bangalore: Ujvala Consultants Pvt. Ltd., 1999. [3] Researching the legal web: A guide to legal resources on the Internet Nick Holmes and Dalia Venables.  2nd Ed.  London: Butterworths, 1999.  
Evaluation Pattern CIA  50% ESE  50%  
MCS242B  OOAD USING UML (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Object Oriented Analysis and Design Using UML course provides instruction and practical experience focusing on the effective use of objectoriented technologies and the judicious use of software modeling as applied to a software development process. 

Learning Outcome 

Upon the completion of the course,the student will able to CO 1: Understand the objectoriented life cycle. CO 2: Identify the objects, relationships, services and attributes through UML. CO 3: Apply the usecase diagrams. CO 4: Implement software quality and usability. 
Unit1 
Teaching Hours:12 
Complexity


The inherent complexity of software, The Structure of complex systems, Bringing order to chaos, on designing complex systems, Categories of analysis and Design methods.  
Unit1 
Teaching Hours:12 
The Object Model


The evolution of object model, Elements of object model, applying the object model, Foundations of the object model.  
Unit2 
Teaching Hours:13 
Classification


The importance of proper classification, Identifying classes and objects, Key abstraction and mechanisms, A problem of classification.  
Unit2 
Teaching Hours:13 
Classes and Objects


The nature of an object, Relationship among objects, the nature of a class, Relationship among classes, The interplay of classes and objects, On building quality classes and objects, invoking a method.  
Unit3 
Teaching Hours:12 
Notation


Basic Behavioural Modelling, Basic elements, class diagram, object, state Transition diagram, Interactions, Use Case Diagrams, Activity, module and process diagrams.  
Unit4 
Teaching Hours:10 
Process


Principles, Micro and macro development process, Pragmatics Management and planning, staffing, Release management, Reuse, Quality Assurance Metrics, Documentation, Tools, The benefits and risks and Objectoriented development.  
Unit5 
Teaching Hours:13 
Architectural Modeling


Components, Deployment, Collaborations, Pattern and Frameworks, Component Diagram, Deployment Diagrams, Systems and Models. Case Study: A domain based analysis and design using rational rose can be made.  
Text Books And Reference Books: [1] Mahesh P. Matha, ObjectOriented analysis and Design Using UML, PHI,3^{rd} reprint, 2012. [2] Grady Booch, ObjectOriented Analysis And Design With Applications, Pearson Education, 3rd edition, 2009.  
Essential Reading / Recommended Reading [1] Grady Booch, James Rumbaugh and Ivar Jacobson, The Unified Modeling Languages User Guide, Addison Wesley, 4th Edition, Reprint 2000. [2] Mike O’Docherty, Object Oriented Analysis and Design Understanding system development with UML2.0, John Wiley and Sons, 1st Edition, 2005.  
Evaluation Pattern CIA  50% ESE  50%  
MCS242C  WEB ENGINEERING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The World Wide Web has become a major delivery platform for information resources. Many applications continue to be developed in an adhoc way, contributing to problems of usability, maintainability, quality and reliability. This Course Description examines systematic, disciplined and quantifiable approaches to developing of highquality, reliable and usable web applications.


Learning Outcome 

At the conclusion of course, students are expected to be able to: CO 1: Understand the concepts, principles and methods of Web engineering. CO 2: Apply the concepts, principles, and methods of Web engineering to Web applications development. CO 3: Implement the Web application development software tools and environments currently available on the market. 
Unit1 
Teaching Hours:12 
Requirements Engineering and Modeling


RE Fundamentals and Specifics  Principles for RE  Adapting RE Methods  Modeling Fundamentals and Specifics  Modeling Requirements  Content Modeling  Hypertext Modeling  Presentation Modeling  Customization Modeling  
Unit2 
Teaching Hours:12 
Web Application Architectures and Design


Fundamentals and Specifics – Components  Layered Architectures  Dataaspect Architectures  Evolutionary Perspective  Presentation Design  Interaction Design  Functional Design  Outlook
 
Unit3 
Teaching Hours:12 
Testing, Operation and Maintenance


Fundamentals and Specifics of Testing  Test Approaches and Schemes  Test Methods and Techniques  Test Automation – Challenges in Launch of a Web Application  Promoting a Web Application  Content Management  Usage Analysis  
Unit4 
Teaching Hours:12 
Performance and Security


Characteristics of Performance  Definition and Indicators – Workload  Analytical Techniques  Representing and Interpreting Results  Performance Optimization Methods  Aspects of Security  Encryption, Digital Signatures and Certificates  Secure Client/ServerInteraction  Client Security Issues  Service Provider Security Issues
 
Unit5 
Teaching Hours:12 
Technologies for Web Applications and Semantic Web


Fundamentals  Client/Server Communication  Clientside Technologies  Ajax  Documentspecific Technologies  Serverside Technologies  Fundamentals and Specifics of Semantic Web  Technological Concepts and Tools.
 
Text Books And Reference Books: [1].Web Engineering: The Discipline of Systematic Development of Web Applications by GertiKappel, 2012.
 
Essential Reading / Recommended Reading [1]. Unleashing Web 2.0: From Concepts to Creativity by Diane Cerra, 2010.
 
Evaluation Pattern CIA50% ESE50%  
MCS242D  COMPILER DESIGN (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course is to learn the theory and practice behind building automatic translators and compilers for higher level programming languages and to engineer and build key phases of a compiler.


Learning Outcome 

Upon completion,the student will able to CO1: Develop and implement compilers. CO2: Design LL and LR parsers. CO3: Perform code optimization. CO4: Design algorithms to generate machine code.

Unit1 
Teaching Hours:12 
Introduction to Compilers


Structure of a compiler  Lexical Analysis: Role of Lexical Analyzer  Input Buffering  Specification of Tokens  Recognition of Tokens  The LexicalAnalyzer Generator Lex  Finite Automata  From Regular Expressions to Automata  Design of a LexicalAnalyzer Generator  Optimization of DFABased Pattern Matchers.
 
Unit2 
Teaching Hours:12 
Syntax Analysis


Role of Parser  Grammars  Error Handling  Contextfree grammars  Writing a grammar  TopDown Parsing  BottomUp Parsing  LR Parsing  General Strategies Recursive Descent Parser Predictive Parser  Parser Generators.  
Unit3 
Teaching Hours:12 
Intermediate Code Generation


Syntax Directed Definitions  Evaluation Orders for Syntax Directed Definitions  Evaluation Orders for SDD’s  Applications of SyntaxDirected Translation  SyntaxDirected Translation Schemes  Intermediate Languages: Syntax Tree  ThreeAddress Code  Types and Declarations  Translation of Expressions  Type Checking.  
Unit4 
Teaching Hours:12 
RunTime Environment and Code Generation


Storage Organization  Stack Allocation Space  Access to Nonlocal Data on the Stack  Heap Management  Garbage Collection and TraceBased Collection. Issues in Design of Code Generator  The Target Language  Addresses in the Target Code  Basic Blocks and Flow Graphs  Optimization of Basic Blocks  A Simple Code Generator  Register Allocation and Assignment  Dynamic Programming CodeGeneration.
 
Unit5 
Teaching Hours:12 
Machine Independent Code Optimization


Principal Sources of Optimization  Introduction to DataFlow Analysis  Foundations of DataFlow Analysis  Constant Propagation  Partial Redundancy Elimination  Loops in Flow Graphs  RegionBased and Symbolic Analysis.
 
Text Books And Reference Books: 1. Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman, Compilers: Pearson New International Edition: Principles, Techniques, and Tools, Second Edition, Pearson Education Limited, 2013.
 
Essential Reading / Recommended Reading [1]. Keith D Cooper and Linda Torczon, Engineering a Compiler, Morgan Kaufmann Publishers Elsevier Science, 2004. [2].V. Raghavan, Principles of Compiler Design‖, Tata McGraw Hill Education Publishers, 2010. [3].Terence Halsey, Compiler Design: Principles, Techniques and Tools, Larsen & Keller education, 2018.
Web Resources: 1.http://www.cs.cmu.edu/~fp/courses/15411f14/resources.html 2.https://www.tutorialspoint.com/compiler_design/  
Evaluation Pattern CIA50% ESE50%  
MCS242E  WIRELESS AND MOBILE NETWORKS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course aims at providing a comprehensive overview of the important issues related to Wireless communication and Mobile Computing. They are grouped into four main areas: basic issues and problems, mobile communications, cellular networks and protocols. Different mobile communications methods and latest communication technologies are discussed. Protocols such as Mobile IP, medium access control and mobility management strategies that are needed to support mobile devices are covered under the specified topic.


Learning Outcome 

Upon completion of the course the students will be able to
CO 1: Understand the basic issues and problems in mobile computing. CO 2: Assess the transmission mechanisms and characteristics of different mobile/wireless communication. CO 3: Analyse the strengths and limitations of different types of mobile/wireless networks. CO 4: Evaluate different mechanisms for supporting mobility. 
Unit1 
Teaching Hours:12 
Wireless Systems and its Evolution


Wireless Telecommunications Systems and Networks History and Evolution of Wireless Radio Systems, Development of Modern Telecommunications Infrastructure, Overview of Existing Network Infrastructure, Wireless Network Applications: Wireless Markets. Evolution and Deployment of Cellular Telephone Systems Different Generations of Wireless Cellular Networks, 1G Cellular Systems, 2G Cellular Systems, 2.5G Cellular Systems, 3G Cellular Systems, 4G Cellular Systems and Beyond, Wireless Standards Organizations.  
Unit2 
Teaching Hours:12 
Cellular System and Architecture


Common Cellular System Components Common Cellular Network Components, Hardware and Software Views of the Cellular Network, 3G Cellular System Components, Cellular Component Identification, Cell establishment. Wireless Network Architecture and Operation The Cellular Concept, Cell Fundamentals, Capacity Expansion Techniques, Mobility Management, Wireless Network Security.
 
Unit3 
Teaching Hours:12 
GSM and TDMA Technology


Introduction to GSM and TDMA, GSM Network and System Architecture, GSM Channel Concept, GSM Identities, GSM System Operations, GSM Infrastructure Communications.  
Unit4 
Teaching Hours:12 
CDMA and CDPD


CDMA Technology Introduction to CDMA, CDMA Network and System Architecture, CDMA Channel Concept, CDMA System Operations. CDPD and Edge Data Networks CDPD, GPRS, GPRS Networks, GPRS Network Details, GPRS Network Layout and Operation, GPRS Packet Data Transfer, GPRS Protocol Reference Model, GPRS Logical Channels, GPRS Physical Channels, GSM/GPRS/Edge Technology.  
Unit5 
Teaching Hours:12 
Wireless LAN/Wireless PANs/IEEE 802.15x


Introduction to wireless LAN 802.11X technologies, Evolution of Wireless LAN, Introduction to IEEE 802.15x Technologies, Wireless PAN Applications and Architecture, Bluetooth, Introduction to Broadband wireless MAN,802.16 technologies.
 
Text Books And Reference Books: [1] Mullett, Wireless Telecommunications Systems and Networks, Cengage Learning, 2013.
 
Essential Reading / Recommended Reading [1] Raj Kamal, Mobile Computing, Oxford University Press, 2012. [2] Stallings William, Wireless Communications and Networks, Pearson Education Asia, 2nd Edition, 2009. [3] Theodore S Rappaport, Wireless Communications: Principles and Practice, Pearson Education Asia, 2nd Edition, 2009. [4] Jochen Schiller, Mobile Communication, AddisonWesley, 2nd Edition, 2011.  
Evaluation Pattern CIA50% ESE50%  
MCS242F  THEORY OF COMPUTATION (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course serves as an introduction to the basic theory of Computer Science, formal methods of computation and indepth understanding of the compilation process. Topics include finite automata, regular expressions and languages; push‐down automata, context‐free languages; selected advanced language theoretical topics; emphasis on technique and compiler theory as well. 

Learning Outcome 

Upon successful completion of this course students should be able to: CO 1: Analyze and design finite automata, pushdown automata, Turing machines, formal languages, and grammars. CO 2: Demonstrate their understanding of key notions, such as algorithm, computability, decidability, and complexity through problem solving. CO 3: Assess the basic results of the Theory of Computation. CO 4: Explain the relevance of the ChurchTuring thesis.

Unit1 
Teaching Hours:12 
Finite Automata and Regular Expressions


Alphabets, Strings and Languages, Deterministic and NonDeterministic Finite Automata, Finite Automata with ε moves, regular expressions equivalence of NFA and DFA, twoway finite automata, Moore and Mealy machines, applications of finite automata.  
Unit2 
Teaching Hours:12 
Push Down Automata Theory


ContextFree Languages and Derivation Trees Ambiguity in ContextFree Grammars Chomsky Normal Form Greibach Normal Form, Push Down Automata Definition, Acceptance by Push Down Automata, Push Down Automata and Context Free Languages, properties of CFL.  
Unit3 
Teaching Hours:12 
Introduction to Compiler


Compilers Analysis of the source program Phases of a compiler, Compiler construction tools, Role of Lexical Analyzer Input Buffering Specification of Tokens.
 
Unit4 
Teaching Hours:12 
Basic Parsing Techniques


Shift reduce parsing operator precedence parsing, Recursive descend parsing, predictive parsing, LR parsing, Simple LR parsing, canonical LR parsing, LALR parsing.  
Unit5 
Teaching Hours:12 
Intermediate Code Generation


Intermediate languages Declarations, Assignment Statements, Boolean Expressions, Case Statements, Back patching Procedure calls. Code Optimization: Principle Sources of optimization, Loop Optimization, DAG Representation of basic blocks, Global Data Flow Analysis, Code Generation, Problems in code generation Register allocation and assignment, Code Generation from DAG s, PeepholeOptimization.  
Text Books And Reference Books: [1] John E. Hopcroft and Jeffrey D. Ullman, Introduction to Automata Theory, Languages and Computation, Narosa Publishers, 2011.
 
Essential Reading / Recommended Reading [1] Alfred Aho, Ravi Sethi, Jeffrey D Ullman, Compilers Principles, Techniques and Tools, Pearson Education Asia, 2008. [4] Mishra&Chandrashekharan: Theory of Computer Science, Automata Languages & computation, 2nd Ed PHI, New Delhi.
 
Evaluation Pattern
CIA ESE 50% 50%  
MCS242G  DATA ANALYTICS (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Data Science is the latest buzz word in the modern era of cloud and big data in academic research and corporate world. Data Science experts must acquire analytical skill set for pursuing research and generating new knowledge in the business process. Data Analytics course delivers various techniques to discover new and hidden knowledge from the data set. This course provides insight into the complete research process in phases as research methodology, data exploration, modeling, evaluation and visualization. R programming, MATLAB and Excel are the suggestive tools for implementation. 

Learning Outcome 

Upon successful completion of the course students will be able to : CO1: Identify appropriate preprocessing techniques to clean the data CO2: Apply various supervised and unsupervised algorithms to real world problems CO3: Interpret the results of developed models using different visualization techniques 
Unit1 
Teaching Hours:12 
Introduction to Data Analytics


Introduction to data, types of data, Knowledge discovery process, Big Data Platform – Challenges of conventional systems – Web data – Evolution of Analytic scalability, Types of analytics: Confirmatory analytics, Descriptive analytics, exploratory analytics and Predictive analytics, analytic processes and tools, Statistical concepts: Sampling distributions, resampling, statistical inference, prediction error.  
Unit2 
Teaching Hours:12 
Classification Techniques


Bayesian modeling, inference and Bayesian networks, Support vector and kernel methods, nonlinear dynamics – Rule induction – Neural networks, competitive learning, neural networks and deep learning.
 
Unit3 
Teaching Hours:12 
Association Rule Mining and Clustering


Mining Frequent itemsets – Market based model – Apriori Algorithm – FP growth algorithm, Handling large data sets in Main memory – Limited Pass algorithm – Counting frequent itemsets in a stream – Clustering Techniques – Hierarchical – K Means – DBSCAN.  
Unit4 
Teaching Hours:12 
Mining Data Streams


Introduction to Streams Concepts – Stream data model and architecture – Stream Computing, Sampling data in a stream – Filtering streams – Counting distinct elements in a stream –linear systems analysis, stock market predictions.  
Unit5 
Teaching Hours:12 
Framework and Visualizations


MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases – S3 – Hadoop Distributed file system for big data – Visualizations – Visual data analysis techniques, interaction techniques; Systems and applications.
 
Text Books And Reference Books: [1] Runkler, Thomas. A, Data Analytics: Models and Algorithms for Intelligent Data Analysis, Springer, 2012. [2] Lillean Pearson, Data Science For Dummies, John Wiley and Sons, 2015.
 
Essential Reading / Recommended Reading [1] Jain P and Sharma P, Behind Every Good Decision: How Anyone Can Use Business Analytics to Turn Data into Profitable Insight, Amacom, 2014. [2] John W Foreman, Data Smart: Using Data Science to Transform Information into Insight, Wiley, 2013.  
Evaluation Pattern CIA 50% ESE 50%  
MCS243A  MATLAB PROGRAMMING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course introduces students to basic Matlab programming concepts. At the end of the course, students should be able to use Matlab in their own work and be prepared to develop the skills necessary to handle research related projects in the area of Science effectively.


Learning Outcome 

On completion of the course the student will acquire the following skills in Matlab: CO 1: Understand the use of procedural statementsassignments, conditional statements, loops, function calls, arrays and files. CO 2: Design, code, and test small programs using GUI. CO 3: Develop competency in using graphics tools.

Unit1 
Teaching Hours:12 
An Introduction to Matlab


An Introduction to Matlab – Matlab Installation, Features of Matlab  Some basics of using Matlab  The order of precedence, Algebraic functions, special characters, functions, Variables, Different types of variables Matrices, vectors and scalars  Creating matrices, Addressing parts of matrices, Changing parts of a matrix, Some special commands for handling matrices, More about matrices
 
Unit2 
Teaching Hours:12 
Mathematical operations with matrices &Importing and exporting data


Mathematical operations with matrices  Functions that operate elementbyelement, Elementary mathematical functions that operate columnwise, Matrix algebra, Solving systems of linear equations, Finding linear regression coefficients. Importing and exporting data  Preparing data to import, Copyandpaste importing, Importing using the Import Wizard, Importing using commands, Exporting to Excel files with commands
 
Unit3 
Teaching Hours:12 
Graphics and Programming


Graphics Useful commands for twodimensional plotting, Time series plotting, Plotting a function, Several graphs in one window and other types of graphs, Other twodimensional graphs, Plotting tools. Programming in Matlab – Scripts, The Editor, Writing a script, The search path, User interaction with the script User defined functions  About the differences between scripts and user defined functions, More about functions.
 
Unit4 
Teaching Hours:12 
Flow control and Numerical analysis


Flow control – Loops, Relational and logical operators, Conditional statements, More about flow control. Numerical analysis and curve fitting  Solving equations, Finding a function minimum point, Numerical integration, Curve fitting, More about numerical analysis
 
Unit5 
Teaching Hours:12 
File Handling and GUI


File Handling  fopen, fclose functions, Reading and Writing Text files and Binary Files. Graphical User Interface GUI Development Environment, GUI Components, Dialog Boxes, File Dialog Box, Creating Simple GUI.  
Text Books And Reference Books: [1] Krister Ahlersten, An Introduction to Matlab, Book Boon, 2^{nd} Edition, 2015. (ebook) [2] Y. Kirani Singh & BB Chaudhuri, Matlab Programming, PHI Publications, 2007.  
Essential Reading / Recommended Reading [1] Cesar Perez Lopez, Matlab Programming for Numerical Analysis, Springer publications, 2014 [2] Delores M Etter & David C Kuncicky, Introduction to Matlab, 2^{nd} Ed, Pearson Publications, 2004. [3] RudraPratap, Getting Started with MATLAB 7, A Quick introduction for Scientist and Engineers”, Oxford University Press (2006).
 
Evaluation Pattern CIA50% ESE50%  
MCS243B  R PROGRAMMING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This course is used to provide an introduction to R, statistical language and environment that also provides more flexible graphics capabilities than other popular statistical packages. The course also covers the basics of R for statistical programming, computation, graphics, and modeling.


Learning Outcome 

Students will able to: CO 1: Understand about usage of R for statistical programming, computation, graphics and modeling, CO 2: Apply R in an efficient way, fit some basic types of statistical models. CO 3: Implement R in their own research work.

Unit1 
Teaching Hours:12 
RIntroduction & Installation


Introduction to RBenefits of using RUnique features of RExploring RInstallPackages Working with code editorFirst R sessionnavigating the workspace RobjectsAtomic Vectors Attributes MatricesArraysClassListsData FramesLoading and Saving data.
 
Unit2 
Teaching Hours:12 
Control Structures


IfelseFor LoopsWhile LoopsRepeatNext, Break, Functions, Symbol BindingR Scoping RulesOptimizationCoding StandardsDates and Time Loop Functionslapply(), apply(), mapply(), tapply(),splitDebbugingProblem diagnosisReading Errors and WarningsReading error messagesCaring about warningsGoing Bug HuntingCalculating the logit()Knowing where an error comes fromLooking inside a function  
Unit3 
Teaching Hours:12 
Graphics


Basic plottingManipulating the plotting windowAdvanced plotting using lattice library Saving plots.
 
Unit4 
Teaching Hours:12 
Models


Central TendencyMeasuring VariabilityCovariance and CorrelationMeasuring SymmetryPCA.Model formulae and model optionsOutput and extraction from fitted models. Models considered: Linear regression: lm () – Logistic regression: glm () – Poisson regression: glm () – Survival analysis: Surv (), coxph () – Linear mixed models: lme ()
 
Unit5 
Teaching Hours:12 
Data Processing using R


Entering data in the R text editorUsing the Clipboard to copy and pasteReading data in CSV files, Reading data from ExcelWorking with other data types. Manipulating and Processing DataDeciding on the Most Appropriate Data Structure, Creating Subsets of Data, Adding Calculated Fields to Data, Combining and Merging Data Sets, Sorting and Ordering Data, Traversing Data with the Apply Functions, Getting to Know the Formula Interface, Working with Tables.  
Text Books And Reference Books: [1] Andrie De Vries, JorisMeys, R Programming for Dummies. ISBN 9781119962847. John Wiley & Sons, 2012 [2] Grolemund, Forword, Hadley Wickham, Garrett, HandsOn Programming with R,OREILLY Publishers. June 2014.
 
Essential Reading / Recommended Reading [1] Robert I. Kabacoff, R in Action, Data Analysis and Graphics with R, ISBN: 9781935182399, August 2011. [2]ViswaViswanathan, ShanthiViswanathan, R Data Analysis Cookbook. ISBN 10: 1783989068, 2015.
 
Evaluation Pattern CIA50% ESE50%  
MCS243C  NETWORK SIMULATION USING NS3 (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To study about basics of computer networks and to understand the NS3 Simulator architecture to simulate network components, topologies, network models, protocols and algorithms. 

Learning Outcome 

Upon completion,the student will be able to CO 1: Understand the complex structure of networks protocol hierarchy CO 2: Apply NS3 for communication network performance evaluation. CO 3: Develop simulations for network components. 
Unit1 
Teaching Hours:12 
Basics of Computer Networks


Reference Models (OSI, TCP/IP); Data Link protocols: Sliding Window protocols: Go back N and Selective repeat sliding window protocol, Channel Allocation; Network Layer Protocols: Routing Algorithms, Congestion Control Algorithms; Transport layer protocols: Connection Establishment, Connection Release, Flow Control and Buffering.  
Unit2 
Teaching Hours:12 
NS3 Introduction & Installation


NS3 introduction, NS3 installation (Linux OS), Fundamental aspects of Network simulator3, Working with netanim, working with gnuplot Logging level details, Debugging using gdb tool, Tracing and Monitoring of packet loss, delay and drop, Flow Monitoring.  
Unit3 
Teaching Hours:12 
Network Dynamics


Packet analysis using Wireshark, Building Topologies, Mesh Network, Simple Wireless Scenario, Status of Energy Model, WIFI and Adhoc Network, Broadcast Communication, Network Dynamics and mixed network management.  
Unit4 
Teaching Hours:12 
NS3 Simulation


Implementation of OLSR, AODV, DSDV, DSR, TORA, Worm Model, Simulating pointtopoint and CSMA networks in ns3, Using PyViz for visualizations, Simulating BSS and IBSS in ns3, Using Wireshark for tracing.  
Unit5 
Teaching Hours:12 
NS3 Emulation and Encryption


Simulating a hidden node problem in ns3, Using NetAnim for visualizations, Network Emulation with NS3 over Testbed and Grid Network, Simulating EncryptionDecryption in wireless networks.  
Text Books And Reference Books: [1] Jack L. Burbank, "Introduction to Network Simulator NS3", WileyBlackwell ,2016 [2] Andrew S Tanenbaum, Computer Networks, PHI publications, 5th Edition, 2012.  
Essential Reading / Recommended Reading [1] Kevin Fall & KannanVaradhan, "The NS Manual", VINT Project – 2011.  
Evaluation Pattern CIA  50% ESE  50%  
MCS243D  HADOOP (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

The subject is intended to give the knowledge of Big Data evolving in every realtime application and how they are manipulated using the emerging technologies. This course breaks down the walls of complexity in processing Big Data by providing a practical approach to developing Java applications on top of the Hadoop platform. It describes the Hadoop architecture and how to work with the Hadoop Distributed File System (HDFS) and HBase in Ubuntu platform. 

Learning Outcome 

Upon completion of the course the students will be able to
CO 1: Understand the Big Data concepts in real time scenario. CO 2: Develop a map reduce program and to implement the program in cloud. CO 3: Analyze the Hadoop Distributed File System (HDFS) and HBase. CO 4: Create an application involving Big Data Analytics. 
Unit1 
Teaching Hours:12 
Introduction


Distributed file system – Big Data and its importance, Four Vs, Drivers for Big data, Big data analytics, Big data applications. Algorithms using map reduce, MatrixVector Multiplication by Map Reduce.  
Unit1 
Teaching Hours:12 
Apache Hadoop


Moving Data in and out of Hadoop – Understanding inputs and outputs of MapReduce  Data Serialization. Problems with traditional largescale systemsRequirements for a new approach Hadoop – Scaling  Distributed Framework  Hadoop v/s RDBMS  Brief history of Hadoop.  
Unit2 
Teaching Hours:12 
Developing MapReduce Programs


Using languages other than Java with Hadoop, Analyzing a large dataset.  
Unit2 
Teaching Hours:12 
Understanding MapReduce


Key/value pairs, The Hadoop Java API for MapReduce, Writing MapReduce programs, Hadoopspecific data types, Input/output.  
Unit2 
Teaching Hours:12 
Setting up Hadoop on a local Ubuntu host


Prerequisites, downloading Hadoop, setting up SSH, configuring the pseudodistributed mode, HDFS directory, NameNode, Examples of MapReduce, Using Elastic MapReduce, Comparison of local versus EMR Hadoop.  
Unit2 
Teaching Hours:12 
Configurations of Hadoop


Hadoop Processes (NN, SNN, JT, DN, TT)  Temporary directoryUICommon errors when running Hadoop cluster, solutions.  
Unit3 
Teaching Hours:12 
Hadoop Streaming


How Streaming Works  Streaming Command Options  Specifying a Java Class as the Mapper/Reducer  Packaging Files with Job Submissions  Specifying Other Plugins for Jobs.  
Unit3 
Teaching Hours:12 
Hadoop configuration properties


Setting up a cluster, Cluster access control, managing the NameNode, Managing HDFS, MapReduce management, Scaling.  
Unit3 
Teaching Hours:12 
Advanced MapReduce Techniques


Simple, advanced, and inbetween Joins, Graph algorithms, using languageindependent data structures.  
Unit4 
Teaching Hours:12 
HIVE & PIG


Architecture, Installation, Configuration, Hive vs RDBMS, Tables, DDL & DML, Partitioning & Bucketing, Hive Web Interface, Pig, Use case of Pig, Pig Components, Data Model, Pig Latin.  
Unit5 
Teaching Hours:12 
Hands On


Single Node Hadoop Cluster Set up in any cloud service provider  How to create instance. How to connect that Instance Using putty. Installing Hadoop framework on this instance. Run sample programs which come with Hadoop framework.  
Unit5 
Teaching Hours:12 
HBase


RDBMS Vs NoSQL, HBasics, Installation, Building an online query application – Schema design, Loading Data, Online Queries, Successful service.  
Text Books And Reference Books: [1] Boris lublinsky, Kevin t. Smith, Alexey Yakubovich, “Professional Hadoop Solutions”, Wiley, ISBN: 9788126551071, 2015. [2] Tom White, “Hadoop: The Definitive Guide”, Publisher: O’Reilly Media, Inc., 2015 Edn. [3] Garry Turkington, Hadoop Beginner's Guide, Packt Publishing, 2013 edn.  
Essential Reading / Recommended Reading [1] Pethuru Raj, Anupama Raman, Dhivya Nagaraj, Siddhartha Duggirala, “HighPerformance BigData Analytics: Computing Systems and Approaches”, Springer, 2015. [2] Jonathan R. Owens, Jon Lentz, Brian Femiano, “Hadoop RealWorld Solutions Cookbook”, Packt Publishing, 2013 edn. [3] Tom White, “HADOOP: The definitive Guide”, O Reilly 2012.  
Evaluation Pattern CIA  50% ESE  50%  
MCS243E  PYTHON PROGRAMMING (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

This Course helps in understanding the programming paradigms brought in by Python with a focus on Regular Expressions, List and Dictionaries.It enables learning Python case studies on Data Mining, Image and Data Processing to appreciate the fastchanging landscape of programming. 

Learning Outcome 

Upon completion of the course,the student will able to CO 1: Understand the Python Programming Paradigm. CO 2: Implement the concepts of Regular Expression ,Text Processing and File handling. CO 3 Apply Python for Data and Image processing. CO 4: Develop hands on experience in Cluster Analysis using Python. 
Unit1 
Teaching Hours:12 
Programming Fundamentals


Introduction, Python Objects, Builtin Functions, Numbers and Strings, Conditionals and Loops, Functions, Passing Arguments, String Functions  
Unit2 
Teaching Hours:12 
Lists, Tuples, Files


Operators, Builtin Functions, List Type Builtin Methods, Special Features of Lists, Tuples, Tuple Operators and Builtin Functions, Special Features of Tuples File Objects, File Builtin Function, File Builtin Methods, File Builtin Attributes, Standard Files, Commandline Arguments, File System, File Execution, Persistent Storage Modules  
Unit3 
Teaching Hours:12 
Regular Expressions, Dictionaries


Introduction/Motivation, Special Symbols and Characters for REs, REs and Python Introduction to Dictionaries, Operators, Builtin Functions, Builtin Methods, Dictionary Keys.  
Unit4 
Teaching Hours:12 
Data Processing: Case Study


Storing in List and Strings, Dispersion, Central Tendency, Mean Median Mode, Frequency Distribution, Standard Deviation. Using Files for large dataset, statistics with real data, reading data from internet, Accessing Stock Market Data, Correlating Stock data.  
Unit5 
Teaching Hours:12 
Image Processing and Data Mining: Case Study


Introduction, RGB Color Model, Object for Image Processing, Image Processing (Negative Images, Gray Scale, Resizing, Stretching, Flipping, Edge Detection) What is Data Mining? Implementing Cluster Analysis on Simple Data, Distance between two points, Clusters and Centroids, KMeans cluster Analysis, File Processing, Visualization  
Text Books And Reference Books: [1] Chun, J Wesley, Core Python Programming, Second Edition, Pearson, 2007 Reprint 2010 [2] Bradley N Miller, David L Ranum, Python Programming in Context, Second Edition, 2014  
Essential Reading / Recommended Reading [1] Barry, Paul, Head First Python, 2nd Edition, O Rielly, 2010 [2] Lutz, Mark, Learning Python, 4th Edition, O Rielly, 2009  
Evaluation Pattern CIA  50% ESE  50%  
MCS251  DATA STRUCTURES LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

This Course helps the students to implement the theoretical concepts of Data Structures and also compliment the same with handson experience effectively. 

Learning Outcome 

The successful completion of this course will enable the students to: CO 1: Understand the need for Data Structures when building an application using an optimized algorithm CO 2: Apply Code efficiency and Optimization techniques. CO 3: Implement algorithms like Huffman, Quick Sort, Shortest Path etc CO 4: Develop programming skills 
Unit1 
Teaching Hours:60 

List of programs


1. Implement sequential search and binary search techniques. 2. Implement Selection sort. 3. Implement Insertion sort. 4. Implement Stacks. 5. Implement Queues. 6. Implement linked lists and some operations on linked lists. 7. Write a program to convert an infix expression to the postfix form. 8. Write a program to evaluate a postfix expression. 9. Implement Quick sort. 10. Implement Merge sort for array. 11. Merge Sort a file contents (without loading the content into an internal data structure). 12. Implement TwoWay linked lists. 13. Implement Circular linked lists. 14. Implement Binary Search Tree. 15. Implement Shell sort. 16. Implement Heap sort. 17. Implement Radix sort. 18. Implement Depth First Search for Graphs. 19. Implement Breadth First Search for Graphs.  
Text Books And Reference Books: [1] Gilberg, F Richard &Forouzan, A Behrouz, Data Structures A Pseudocode approach with C,2nd Edition, Cengage, 2008.  
Essential Reading / Recommended Reading [1] Horowitz Sahni AndersonFreed, Fundamental of Data Structures in C, Universities Press, Reprint 2008. [2] Richard Johnsonbaugh, Algorithms, Pearson Education, 2nd Edition, 2008 [3] Robert Sedgwick, Algorithm in C++, AddisonWesley Publishing Company. [4] Knuth, Donald E, Art of Computer Programming, Sorting & Searching, AddisonWesley, 2005.  
Evaluation Pattern
 
MCS252  MOBILE APPLICATION LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:02 

Course Objectives/Course Description 

Aimed at helping learners create applications using Google's Android™ open source platform. Learn, Understand, create and deploy advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS. 

Learning Outcome 

Upon completion of the Course,student will be able to CO1:Build your own Android apps. CO2:Explain the differences between Android and other mobile development environments. CO3: Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus. CO4:Secure, tune, package and deploy Android Applications 
Unit1 
Teaching Hours:60 
Program List


1. Installation – Android Studio 2.Develop an application that uses GUI components, Font and Colors. 3.Develop an application that uses Layout Managers and event listeners. 4.Develop a native calculator application 5. Develop an Application that used the camera in phone and allows to click the photo. 6.Develop an application that uses that demonstrates the use of Fragments 7.Develop a Music player – Basic controls to play, pause and stop the MP3 file 8.Implement an application that writes data to the SD card. 9.Write a mobile application that creates alarm clock. 10.Develop an application that makes use of database. 11. Creating application using App Inventor 12. Packaging and Deploying the APK file in Google Play store  
Text Books And Reference Books: [1] WeiMeng Lee, “Beginning android 4 application Development, John Wiley & sons, Inc, 2012.  
Essential Reading / Recommended Reading [1] Paul DeitelHarvey DeitelAbbey DeitelMichael Morgano,”Android for Programmers An AppDriven Approach”,Pearson Education Inc., 2012. [2] Jerome (J.F) DiMarzio , "Android  A programmer's Guide", TataMcgraw Hill,2010, ISBN: 9780071070591  
Evaluation Pattern CIA50% ESE50%  
MCS271  SEMINAR (2019 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:1 
Course Objectives/Course Description 

Students will be giving presentations on any advanced concepts and technologies in computer science.


Learning Outcome 

Upon completion of Course,student will be able to CO1: Understand new and latest trends in computer science CO2: Identify advanced concepts in IT CO3: Apply the acquired knowledge in their Research.

Unit1 
Teaching Hours:30 
Seminar


Identification of Topic. Presentation of Topic. Submission of Seminar Report.  
Text Books And Reference Books:   
Essential Reading / Recommended Reading   
Evaluation Pattern CIA50% ESE50%  
MCS281  ADBMS PROJECT LAB (2019 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

The main aim of the course is to develop the practical knowledge of the students on building a project using ADBMS concepts. The student identifies a real world problem, design, and creates the model to solve it. At the end of the semester the students should develop the working project using ADBMS concepts. 

Learning Outcome 

The following outcomes are expected from the students: CO1: Understand the practical concepts and the technical issues related to the development of ADBMS project and identify the problem. CO2: Analyze the problem, identify the solution, various front end and backend tools required for the project and apply them as per the requirement. CO3: Create a working project that satisfies the need of the end user. CO4: Develop communication skills, ethics and leadership qualities as an individual and as a leader. 
Unit1 
Teaching Hours:60 
ADBMS Project Lab


1. DBMS Lab includes an application project. The backend of the project may be any one of the following: • MSSQL Server • Oracle • DB2 • MySql 2. User interface could be made with any one of the front end tools available. 3. Students should have indepth knowledge of the front and backend tool, which they are using. 4. Database tables are required to be normalized, at least to the second level. 5. There need to be independent forms for data entry operations. 6. All the forms in the project need to have similar look and feel in terms of background/foreground color, arrangement of controls, spacing and sizing of the controls, size of forms, etc. 7. There could be separate forms for searching purposes. 8. Master table data entry forms may include navigational buttons along with Add, Save, Delete etc. 9. Reports should be generated dynamically.
Note: Project should be developed by following software engineering process.
Solicitation, Source Selection, Contract Administration.  
Text Books And Reference Books: [1] Bob Hughes, Mike Cotterell, Software Project Management, Tata McGrawHill, 5th Ed.,2011 [2] Pankaj Jalote, Software Project Management in Practice, Pearson Education, 3rd Ed., 2010. [3] Kathy Schwalbe, Information Technology Project Management, THOMSON Course Technology, International Student Edition, 2003. [4] Elaine Marmel, Microsoft Office Project 2003 Bible, Wiley Publishing Inc.  
Essential Reading / Recommended Reading [1] Maylor, H., Project Management, PHI, 3rd Ed., 2002. [2] Robert T. Futrell, Quality Software Project Management, Pearson, 2010. [3] Bentley C., PRINCE2: A Practical Handbook, NCC Blackwell, 2002. [4] Robert T. Futrell, Quality Software Project Management, Pearson, 2010. [5] S.A. Kelkar, Software Project Management  A Concise Study, PHI, Revised Edition.  
Evaluation Pattern CIA  50% ESE  50%  
MCS331  DIGITAL IMAGE PROCESSING (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

To provide strong foundation on concepts and algorithms widely used in Digital image processing. It also focuses on compression and restoration of images using various techniques. 

Learning Outcome 

Upon successful completion of the course the student would:Co1: Comprehend the knowledge of image processing and image analysis techniques. 
Unit1 
Teaching Hours:12 

Introduction


The origins of Digital Image Processing, Fundamental Steps in Image Processing, Elements of Digital Image Processing System  
Unit1 
Teaching Hours:12 

Digital Image Fundamentals


Image Sampling and Quantization, Basic relationships: Neighbors, Connectivity, Distance Measures between pixels, Linear and Non Linear Operations.  
Unit2 
Teaching Hours:12 

Image Enhancement in Spatial Domain


Gray Level Transformations, Histogram Processing, Histogram equalization, Histogram specification, Basics of Spatial Filters, Smoothening and Sharpening Spatial Filters.  
Unit2 
Teaching Hours:12 

Image Enhancement in Frequency Domain


Introduction to Fourier Transform and the frequency Domain, Smoothing and Sharpening, Frequency Domain Filters.
Self Learning topic : Homomorphic Filtering  
Unit3 
Teaching Hours:12 

Image Compression


Image Compression models: Huffman coding, Run length coding, LZW coding.  
Unit3 
Teaching Hours:12 

Image Restoration


A model of The Image Degradation / Restoration Process, Noise Models, Restoration in the presence of Noise, Periodic Noise Reduction by Frequency Domain Filtering.  
Unit4 
Teaching Hours:12 

Region based Segmentation and Representation


Region Based Segmentation – Region Growing and Region Splitting and Merging. Representation – Chain codes
Self Learning topic: Polygonal approximations using minimum perimeter polygons.  
Unit4 
Teaching Hours:12 

Image Segmentation


Point, Line and Edge detection.Thresholding – Basic global thresholding, optimum global thresholding using Otsu’s Method.  
Unit5 
Teaching Hours:12 

Object Recognition


Introduction to Patterns and Pattern Classes. DecisionTheoretic Methods – Minimum distance classifier, KNN classifier and Bayes. Self Learning topic : Classification  
Unit5 
Teaching Hours:12 

Descriptiors


Boundary descriptors – Fourier descriptors.Regional descriptors –Topological descriptors and Moment invariants.  
Text Books And Reference Books:
[1] R. C. Gonzalez & R. E. Woods, Digital Image Processing, 3rd Edition.PearsonEducation, 2009. [2] A.K. Jain, Fundamental of Digital Image Processing, 4th Edition.PHI, 2011. [3] Rafael C. Gonzalez, Richard E. Woods and Steven L Eddins, Digital Image Processing Using MATLAB, 2nd Edition. PHI, 2009.  
Essential Reading / Recommended Reading
[1] M. A. Joshi, Digital Image Processing: An algorithmic approach, 2nd Edition. PHI 2009. [2] B.Chanda, D. DuttaMajumdar, Digital Image Processing and analysis, 1st Edition, PHI,2011.  
Evaluation Pattern
 
MCS332  MOBILE APPLICATIONS (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:04 

Course Objectives/Course Description 

This Course Description aimed at helping learners create applications using Google's Android™ open source platform. The Course Description explains what Android™ is and how it compares to other mobile environments, the setup of the Android™ Eclipsebased development tools, the Android™ SDK, all essential features, as well as the advanced capabilities and APIs such as background services, accelerometers, graphics, and GPS. 

Learning Outcome 

Upon successful completion of the course, the student will be able to
CO1: Build your own Android apps.
CO2: Explain the differences between Android and other mobile development environments.
CO3: Design and develop useful Android applications with compelling user interfaces by using extending and creating your own layouts and views and using menus.
CO4: Deploy Android Applications and package. 
Unit1 
Teaching Hours:12 

Introduction


Brief History of Embedded Device Programming, Introduction to Android, Get to know the required tools,Creating your first Android application, Anatomy of android Application. Understanding Activities, linking Activities using intents, fragments, calling Builtin Applications using Intents, Displaying Notifications.  
Unit2 
Teaching Hours:12 

User Interface and Designing with views


Understanding the components of a screen, adapting to display orientation, managing changes to screen orientation, Utilizing the Action Bar, Creating the user Interface programmatically, Listening for UI Notifications. Using Basic Views, Using Picker views, Using List views to display lists, Understanding specialized fragments.  
Unit3 
Teaching Hours:12 

Displaying with views, Data persistence and Content Providers


Using Image Views to display pictures, using menus with views, some additional views. Saving and loading user preferences, persisting Data Files, Creating and using Databases. Sharing Data in Android, using content provider, creating your own content providers, using content providers.  
Unit4 
Teaching Hours:12 

Messaging and Location based services


SMS Messaging, Sending Email, Displaying Maps, Getting Location Data, Monitoring a Location. Hands on project: Building a Location Tracker.  
Unit5 
Teaching Hours:12 

Services


Creating your own services, Establishing Communications between a service and an activity, binding activities to services
Self Learning
understanding Threads, Preparing for Publishing, Deploying APK Files.
 
Text Books And Reference Books: [1] WeiMeng Lee, “Beginning android 4 application Development, John Wiley & sons, Inc, 2012.  
Essential Reading / Recommended Reading [1] Paul DeitelHarvey DeitelAbbey DeitelMichael Morgano,”Android for Programmers An AppDriven Approach”,Pearson Education Inc., 2012. [2] Jerome (J.F) DiMarzio , "Android  A programmer's Guide", TataMcgraw Hill,2010, ISBN: 9780071070591.  
Evaluation Pattern
 
MCS333  DESIGN AND ANALYSIS OF ALGORITHMS (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:04 

Course Objectives/Course Description 

The objective of the course to teach various algorithm design techniques for effective problem solving in computing. Students will be able to analyze the efficiency of algorithms in terms of its complexity and also to design effectivly. 

Learning Outcome 

Upon successful completion of the course student will be able to CO1: Apply appropriate algorithm design techniques to solve computing problems CO2:Analyze the running time of algorithms for problems in various domains. CO3: Design efficient algorithms to solve real world problems. 
Unit1 
Teaching Hours:12 

Introduction


The role of Algorithms in Computing – Algorithms, Algorithms as a technology. Getting Started – Insertion sort, Analyzing algorithms, Designing Algorithms.Growth of Functions – Asymptotic Notations. Recurrences – The Substitution method, Recursion Tree method and Master method.
 
Unit2 
Teaching Hours:12 

Greedy Method


Knap Sack Problem, Minimum Spanning Trees , Prims algorithm and Kruskal’s algorithm.  
Unit2 
Teaching Hours:12 

Divide and Conquer


General Method, Binary Search, Finding the Maximum and Minimum, Merge Sort, Quick Sort, Selection sort, Strassens Matrix Multiplication.  
Unit3 
Teaching Hours:12 

Dynamic programming Method


Optimal Binary Search Trees, Traveling Salesman Problem, Longest Common Subsequence  
Unit3 
Teaching Hours:12 

Back Tracking


Introduction  The 8queens problem, Sum of Subsets  
Unit3 
Teaching Hours:12 

Branch n Bound


General Method Traveling Salesman Problem  
Unit4 
Teaching Hours:12 

Graph Algorithms


Representation of Graph, Depth First Search, Breadth first search.Single Source shortest path – Dijkstra’s Algorithm and Bellman Ford Algorithm. All Pair Shortest Path – FloydWarshall Algorithm.  
Unit4 
Teaching Hours:12 

Lower Bound Theory


Comparison trees for sorting and searching.
Self Learning Representation of graphs (from discrete mathematics)  
Unit5 
Teaching Hours:12 

NPHard and NPComplete problems


Basic Concepts, NP_Hard graph problems, NPHard Scheduling problems, NP Hard code generation problems, some simplified NPHard problems.
Amortized AnalysisCase studies.
 
Text Books And Reference Books: [1] Coremen T H, Leiserson C E, Rivest R L and Stein, Clifford, Introduction to algorithms, PHI, 2nd Edition, 2009. [2] Horowitz E and Sahni S. Fundamentals of Computer Algorithms, Computer SciencePress,2008.  
Essential Reading / Recommended Reading [1] Gelder Van Allen and Baase Sara, Computer Algorithms – Introduction to Design and Analysis, Addison Wesley, 3rd Edition, 2002. [2] Aho A V, Hopcroft J E and Ullman J D., The Design and Analysis of Computer Algorithms, Addison Wesley Publishing House, 1983. [3] Dromey, R.G., How to solve it by Computer, PrenticeHall International, 2006.  
Evaluation Pattern
 
MCS341A  DATA WAREHOUSING AND DATA MINING (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The main objective of the course is designed to introduce the core concepts of data mining and data warehousing techniques and implementation. 

Learning Outcome 

Upon successful completion of the course, the Students will able to CO1:Demonstrate basic data mining algorithms, methods, tools and OLAP. CO3:Build the Data warehouse for real time applications. 
Unit1 
Teaching Hours:12 

Introduction to Data Warehouse and OLAP


Basic elements of the Data Warehouse: Source systemData staging AreaPresentation ServerDimensional ModelBusiness processData MartData warehouseOperational Data StoreOLAP: ROLAP, MOLAP and HOLAP.  
Unit1 
Teaching Hours:12 

Data Warehouse Design


The case for dimensional modeling – Putting Dimensional modeling together: the data warehouse bus architecture – Basic dimensional modeling techniques.  
Unit2 
Teaching Hours:12 

Data Staging


Data staging overview – Plan effectively – Dimension Table staging – Fact Table loads and warehouse operations – Data quality and cleansing – issues.  
Unit2 
Teaching Hours:12 

Data Warehouse Architecture


The value of architecture – An architectural framework and approach – Technical architecture overview – Back room data stores – Back room services. Back Room Services.  
Unit3 
Teaching Hours:12 

Introduction to data Mining


Data Mining – Process and architecture  Kinds of Data to be mined  Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Major Issues in Data Mining.  
Unit3 
Teaching Hours:12 

Data Preprocessing


Preprocessing  Descriptive Data Summarization – Measuring the central tendency Measuring the dispersion of data  Data Cleaning  Missing Values – Noisy Data  Data Cleaning as a Process  Data Integration and Transformation  Data ReductionData Cube AggregationAttribute Subset Selection. Demo: Preprocessing can be done using WEKA tool.  
Unit4 
Teaching Hours:12 

Cluster Analysis


Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – KMeans and KMedoids, Hierarchical Methods Agglomerative and Divisive Demo: Classification and clustering analysis can be done using WEKA tool.  
Unit4 
Teaching Hours:12 

Data Mining Algorithms


Association Rule Mining: Basic Concepts, Efficient and Scalable Frequent Item set Mining Methods – Apriori algorithm, Generating Rules – Improving efficiency – Mining frequent item set without candidate generation. Classification and Prediction: Issues Regarding Classification and Prediction, Accuracy and Error Measures.  
Unit5 
Teaching Hours:12 

Mining TimeSeries and Spatial Data


Mining TimeSeries Data – Trend analysis – Similarity search, Spatial Data MiningSpatial Data Cube Construction and Spatial OLAP Mining Spatial Association and Colocation PatternsSpatial Clustering, Classification MethodsMining Raster Databases.  
Unit5 
Teaching Hours:12 

Applications and Trends in Data Mining


Data Mining Applications, Data Mining System Products and Research Prototypes, Social Impacts of Data Mining.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
MCS341B  MACHINE LEARNING (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

To acquire basic knowledge in machine learning techniques and learn to apply the techniques in the area of pattern recognition and data analytics. 

Learning Outcome 

CO1: Understand the basic principles of machine learning. CO2: Differentiate between predictive and descriptive machine learning techniques and their applications. CO3: Apply machine learning techniques to solve real time problems. CO4: Analyze machine learning algorithms with respect to evolving data types and analysis paradigms 
Unit1 
Teaching Hours:12 
Introduction


Machine Learning, types of machine learning, examples. Supervised Learning: Learning class from examples, VC dimension, PAC learning, noise, learning multiple classes, regression, model selection and generalization, dimensions of a supervised learning algorithm. Parametric Methods: Introduction, maximum likelihood estimation, evaluating estimator, Bayes’ estimator, parametric classification.
 
Unit2 
Teaching Hours:12 
Dimensionality reduction


Introduction, subset selection, principal component analysis, factor analysis, multidimensional scaling, linear discriminant analysis.
Clustering: Introduction, mixture densities, kmeans clustering, expectationmaximization algorithm, hierarchical clustering, choosing the number of clusters. Nonparametric: Introduction, nonparametric density estimation, nonparametric classification.  
Unit3 
Teaching Hours:10 
Decision Trees


Introduction, univariate trees, pruning, rule extraction from trees, learning rules from data.
Multilayer perceptron: Introduction, training a perceptron, learning Boolean functions, multilayer perceptron, backpropogation algorithm, training procedures.  
Unit4 
Teaching Hours:14 
Kernel Machines


Introduction, optical separating hyperplane, vSVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, oneclass kernel machines. Bayesian Estimation: Introduction, estimating the parameter of a distribution, Bayesian estimation, Gaussian processes.
Hidden Markov Models: Introduction, discrete Markov processes, hidden Markov models, basic problems of HMM, evaluation problem, finding the state sequence, learning model parameters, continuous observations, HMM with inputs, model selection with HMM.  
Unit5 
Teaching Hours:12 
Graphical Models


Introduction, canonical cases for conditional independence, dseparation, Belief propagation, undirected graph: Markov random field. Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partiIntroduction, canonical cases for conditional independence, dseparation, Belief propagation, undirected graph: Markov random field.
Reinforcement Learning: Introduction, single state case, elements of reinforcement learning, temporal difference learning, generalization, partially observed state.ally observed state. Self Learning
Clustering , Decision tree  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern CIA  50% ESE  50%
 
MCS341C  PARALLEL COMPUTING WITH OPEN CL (2018 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 
