Syllabus for
Master of Science (Computer Science and Applications)
Academic Year  (2019)

Evaluation Pattern: 60% CIA + 40% ESE 2. Tutorials / Assignments / Tests / Quiz / Seminar. 3. Attendance is part of the CIA component. 4. Minimum percentage to pass in each paper is 50% (CIA + ESE).

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

CO1: Recognize the principles and practice of object oriented programming in the construction of robust maintainable programs.

CO2: Show competence in the use of Java Programming language in the development of small to medium sized application programs that demonstrate professionally acceptable coding and performance standards.

CO3: Design real-time applications in various domains.

Review of the fundamentals of Java Programming, Class and Objects. Inheritance in Java - Inheritance in classes, Using super, Method overriding, Dynamic Method Dispatch. Abstract Classes, Using final with inheritance, the Object Class.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.Exception Handling in Java - try-catch-finally mechanism, throw statement, throws statement. Classes for Exception Handling

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 Multithreading - Life cycle of a thread, Java Thread priorities, Runnable interface and Thread Class. Sharing limited Resources, Shared Object with Synchronization. Applets -Life cycle of Applet, Applet Architecture, Applet restrictions, Creation and Execution of java Applets. Animation in Applets-Advantages of Applets.Applets Vs Applications.

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.Event Handling-Handling Keyboard Events and Mouse Events.Handling Sessions and Cookies.Servlet Model - Overview, Environment Setup, Life Cycle,  Examples - Client Request, Server Response.

Networking - Creating a server that sends data- Creating a client that receives data -two way communication between server and client. Difference between Server Socket and Socket.RMI. JDBC - Using MS-Sql Server Stages in a JDBC program- Registering the driver- Connecting to database - Transaction and Non-Transactional Events - Preparing SQL statements - various methods of statements and differences. Improving the performance of a JDBC program.

Java Server Pages - The Problem with Servlets, Life Cycle of JSP Page, JSP Processing, JSP Application Design with MVC, Setting Up the JSP. Environment - JSP Directives, JSP Action, JSP Implicit Objects, JSP Form Processing, JSP Session and Cookies Handling, JSP Session. Tracking - JSP Database Access, JSP Standard Tag Libraries, JSP Custom Tag, JSP Expression Language, JSP Exception Handling, JSP XML Processing.

[2] Kathy walrath, Black Book : Java server programming” J2EE, 1st ed., Dream Tech Publishers,  2015

[2] Rao Nageswara, Core Java: An Integrated Approach, Dreamtech press, 2nd Edition, 2010.

[3] James Keogh, Complete Reference J2EE, mcgraw publication, 2015.

ESE - 40%

To enable the students to learn the basic functions, principles and fundamental aspects of computer architecture and design in terms of digital logic elements and circuits, central processing unit and memory unit.

CO1: Understand different number system, binary codes and digital logic elements

CO2: Acquaint with elementary postulates of Boolean algebra and methods for simplifying Boolean expressions

CO3: Illustrate the procedures for the analysis and design of sequential and combinational circuits

CO4: Demonstrate the basic structure and operation of processing unit and get familiarize with different types of memory systems

Number system- Decimal number system- Binary number system- octal number system- hexadecimal number system- number system conversion- number representation- unsigned representation – signed number representation-1’s complement – 2’s complement- 9’s complement – 10’s complement- binary arithmetic operation- binary addition- binary subtraction- Binary multiplication- binary division- Binary codes- weighted codes and unweighted codes

Introduction- Boolean algebra- Boolean operators- truth table- laws of Boolean algebra- De Morgan’s Law- Logic gates- Description of logic gates- Universal properties- Simplification of logic functions- Simplification using NAND and NOR  gate- logic expression- minterm - maxterm- SOP - POS expression- minimization techniques- Karnaugh Map

Self learning: Implementation using simulator

Digital circuits- Combinational circuits- Half Adder – Full adder- Half subtractor-Full subtractor- Encoder- Decoder-BCD to seven segment display- Multiplexer- Demultiplexer

Sequential circuits-  Latches- SR- Latch- Flip Flop- SR flip flop- D flip flop- JK flip flop- master slave JK FF - Timing diagrams-Registers- Shift Register- SISO-SIPO-PISO-PIPO- Counters- Synchronous counters- Asynchronous counters- Decade counter- Mod counters- Timing diagrams

Basic Structure of Computers: Basic Operational Concepts- Bus Structures – Processor Clock - Clock Rate- Instruction set: CICS and RISC.

Basic Processing Unit: Some Fundamental Concepts, Multiple Bus Organization, Hard-wired Control, Micro programmed Control.

The memory system: Semiconductor RAM memories-Internal organization of memory chips- static memories-ROM-Cache memories

[2]. V.Carl Hamacher, Zvonko G. Varanesic and Safat G. Zaky, Computer Organisation, 6th  edition, Mc Graw-Hill Inc, 2013.

[2] Bartee, Thomas C, Digital Computer Fundamentals, Tata Mc Graw-Hill, 6th edition, 2013.

[3] William Stallings, Computer Architecture and Organization, PHI, Eigth  Edition, 2015.

[4] David A. Patterson and John L.Hennessey, Computer Organization and Design, Morgan Kauffman / Elsevier, Fifth edition, 2014.

ESE (Weightage): 40%

This course concentrates on introduction, principles, design and implementation of advanced database concepts.Objective of the course is to provide strong foundation of database concepts and develop skills for the design,   storage and retrieval in relational databases, XML and NoSQL databases.

CO1: Understand the fundamental and advanced concepts of relational databases.

CO2: Demonstrate storage and retrieval in XML and NoSQL.

CO3: Design Database Application using CRUD operations.

Database system applications, Purpose of database systems, View of data, Data models, Database languages, Database storage and querying, Transaction management, Database architecture, Database users and administrators.

Structure of relational databases, Database schema, Keys, Schema diagrams, Design process, ER model, Constraints, ER diagrams, Aspects of database design, Atomic domains and 1NF, Decomposition using functional dependencies, Functional dependency theory

File organization, Organization of records in files, Data dictionary storage, Basic indexing concepts, Ordered indexes, B+ tree index, Static hashing, Dynamic hashing, Bitmap index.

Motivation, Structure of XML Data, XML Document Schema, Querying and Transformation, Application Program Interfaces to XML, Storage of XML Data, XML Applications.

Definition and introduction, Document databases – MongoDB, Storing data and accessing data from MongoDB, Querying MongoDB, Document store internals, MongoDB reliability and durability, Horizontal scaling, CRUD operations in MongoDB, Creating and using indexes in MongoDB.

[2].      ShashankTiwari, “Professional NoSQL”, John-Wiley, 2011.

[2]   RamezElmasri, ShamkantNavathe, “Fundamentals of Database Systems”, Addison-Wesley, 6th Edition, 2011.

[3]   Kristina Chodorow, “MongoDB: The Definitive Guide”, O'Reilly, 2nd Edition, 2013.

ESE:  40%

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.

CO1: Collect data from various sources

CO2: Explore data using tools

CO3: Build analytical models

CO4: Interpret results based on the choice of domain

Introduction, Data and Relations-Matrix representation, variable measures, sequential relation, sampling and quantization. Data Pre-processing: Cleaning, Transformation, Basic Visualization-PCA, multidimensional scaling, Histograms, Correlation.

Linear and non-linear regression, Feature Selection. Forecasting - Recurrent Models, Classification-Rules, Trees, Naïve Bayes, SVM, Vector Quantization. Evaluation Metrics-Validation and Interpretation.

Optimization Methods – With derivatives, Gradient Descent. Clustering - Cluster Partition, Sequential, Prototype-Based, Relational, Cluster Validity and Self Organizing Map.

Introduction to Multi-criteria Decision Making, Using Numerical Methods in Data Science, Mathematical Modeling with Markov Chains. Modeling Spatial Data with Statistics- Getting predictive surfaces from special point data, Using trend surface analysis on spatial data.

Principles of Visualization-Understanding the type, Design Style, Data Graphic Type, Web-based Applications for Visualization Design, Best practices in dashboards, Making maps for Spatial Data.

Self Learning: Additional Exploration and Modeling Algorithms

Service based learning: Building models for social relevance issues

ESE (Weightage): 40%

The course builds the logical thinking in the students with the help of the programming. It also facilitates the students to build applications using java programming. The database concepts help the student to learn advance database connectivity and usage.

CO1: Demonstrate the skills for identifying logic in the problem

CO2: Analyze the given problem and write the algorithm, flowchart

CO3: Write structured java programs and implement the advance database concepts

1. Demonstrate objects and classes (constructor, access specifier, method overloading)

2. Demonstrate static block, static variables and static methods

3. Demonstrate inheritance in java

4. Demonstrate use of super and this

5. Demonstrate abstract class

6. Demonstrate interfaces in java

7. Demonstrate exception handling in java

8. Demonstrate multithreading in java

9. Demonstrate applets in java

10. Demonstrate two way communication between server and client

1. Select queries and DML

2. PL/SQL

3. Data manipulation with MongoDB

[2] Black Book “ Java server programming” J2EE, 1st ed., Dream Tech Publishers, Kathywalrath, 2015.

[2] Rao Nageswara, Core Java: An Integrated Approach, Dreamtech press, 2nd Edition, 2010.

[3] Complete Reference J2EE by James Keogh mcgraw publication, 2015.

This course provides a more comprehensive understanding of data structure and algorithm development.

Upon completion of the course, student will be able to 

CO1: Understand the need and working  of advanced search and sorting technique.

CO2: Calculate and measure efficiency of algorithm.

CO3: Appreciate some interesting algorithms like Huffman, Quick Sort, and Shortest Path etc.

Basic Operations, Implementation, Stack applications, Recursion- A Case Study, Queue operations, implementation, Applications of a queue.Linked Lists: Linked list algorithms- Processing a linked list, Linked list algorithms (Create, Traverse, Insert, Delete, Search, Destroy), doubly linked lists structures and its operations, Applications of linked lists. Algorithmic efficiency in terms of space and time complexity

Basic tree concepts, Binary trees, Binary tree traversals, Expression trees, General trees- Changing general tree to binary tree, General tree insertion, Search trees, Binary search trees, Operations, Traversals-BFS and DFS methods, Searching a BST, Algorithms for and traversing and searching. AVL trees, AVL Balance factor, Balancing trees, AVL Insert, AVL Delete

M-Way search trees, B Trees, B-Insertion, B-Tree Deletion, B –Tree Traversal-Tree Search, Simplified B-Trees - 2-3 Tree, 2-3-4 Tree, B-Tree Variations–B * Trees, B+ Trees

Terminology, operations, Graph storage structures – Adjacency Matrix, Adjacency lists, Graph algorithms- Create insert vertex, Delete vertex, Retrieve vertex, Depth first traversal and Breadth First Traversal, Networks- Minimum spanning tree, Shortest Path algorithm

General sort concepts, O Notation, Sort Algorithms-Quick Sort, Heap sort, Sorting using a Heap, Shell sort, Merge sort, radix sort, merging two sorted lists. Efficiency considerations, comparative study

[1]  Richard F. Gilberg, Behrouz A. Forouzan, “Data Structure. A Pseudocode Approach with C”, 3rd Edition, Thomson Publications, reprint 2006.

[2]  A M Tanenbaum, Y Langsam and M. J. Augenstein, “Data Structure using C”, 2nd Edition, Prentice- Hall, India, 2007.

[2]  U.A.Deshpande and O. G. Kakde, Data Structures and Algorithms, ISTE- learning.

ESE: 40%

This course aims to set the foundation for Data Communication and Network Security by introducing the network components, topologies, network models, layers, protocols, and some of the security aspect.

CO1: Comprehend knowledge about Network Architecture and its functionality. 

CO2: Evaluate network protocols for data transmission in various types of networks.

C03: Understand the working of Algorithm in Cryptography

CO4: Design solution to real time problems related to Network Security and compression. 

Data Communications- Data Transmission: Concepts and Terminology, Analog and Digital Data Transmission, Transmission Impairments, Channel Capacity; Transmission Media: Guided Transmission Media, Wireless Transmission, Wireless Propagation, Line-of-Sight Transmission; Signal Encoding Techniques: Digital Data, Digital Signals, Digital Data, Analog Signals, Analog Data, Digital Signals, Analog Data, Analog Signals.

Digital Data Communication Techniques- Asynchronous and Synchronous Transmission, Types of Errors, Error Detection, Error Correction, Line Configurations; Multiplexing: Frequency, Division Multiplexing, Synchronous Time-Division Multiplexing, Statistical Time-Division Multiplexing, Asymmetric Digital Subscriber Line, Switched Communications Networks, Circuit Switching Networks, Circuit Switching Concepts, Softswitch Architecture, Packet-Switching Principles.

Congestion Control in Data Networks- Effects of Congestion, Congestion Control, Traffic Management, Congestion Control in Packet-Switching Networks; Cellular Wireless Networks: Principles of Cellular Networks, First Generation Analog, Second Generation CDMA, Third Generation Systems; High-Speed LANs: The Emergence of High-Speed LANs, Ethernet, Fibre Channel; Wireless LANs: IEEE 802.11 Architecture and Services; Internetwork Protocols - Internetwork Protocols: Internet Protocol, IPv6; Transport Protocols: Connection-Oriented Transport Protocol Mechanisms, TCP, TCP Congestion Control, UDP.

The need for security, Security Approaches, Security Attacks, Security Services, Security Mechanisms, A Model for Network Security. Symmetric Cipher Models, Substitution Techniques, Transposition Techniques and other Symmetric key approaches. Data Encryption Standard, AES Cipher. Public Key Cryptosystems, RSA Algorithm and Diffie-Hellman Key Exchange

Application of Cryptographic Hash Function, Brute Force Attack, Secure Hash Algorithm (SHA-2 & SHA-3), Message authentication code, HMAC, Digital Signatures (DSS). User Authentication: Kerberos Federated Identity Management. E-Mail Security, Pretty Good Privacy, S/MIME, SSL, IP Security Overview.

[2]     William Stallings, “Cryptography and Network Security”, Prentice Hall, 6th Edition, 2014.

[2]      AtulKahate, “Cryptography and Network Security”, Tata McGraw-Hills, 2010.

[3]      Brijendra Singh, “Network Security and Management”, PHI, 3rd Edition, 2013.

ESE (Weightage): 40%

The course will expose few advanced topics in operating system and concepts related to recent developments in operating system.Objectives of the course are to understand the main concepts of parallel processing systems, distributed systems, real time systems etc., to have an insight into UNIX and MACH operating system and to know the components and management aspects of Real time, Mobile Operating Systems.

CO1: Analyse the requirements of Operating System.

CO2: Understand the concept of distributed operating system and concepts.

CO3: Demonstrate the advanced OS concepts of Real time OS and Mobile OS.

General Overview of the System - System Structure – Operating System Services – Introduction to kernel-architecture of unix operating system-introduction to system concepts kernel data structures. The Buffer cache - Buffer Headers – Structure of the buffer pool – Retrieval of a buffer – scenarios for retrieval of a Buffer-Reading and writing disk blocks - Advantages and disadvantages of the buffer cache. Internal Representation of files - Inodes, structure of a regular file, directories, conversion of a path to an inode, Super Block, inode assignment to a New file-Allocation of Disk Blocks-other file Types.

UNIX Process Management - The Structure of Processes: Process States and Transitions - Layout of system memory - Context of a process – Sleep – Implementation of System Calls. Process Control - Process Creation – Signals – Process Termination – Invoking other programs – PID & PPID – Changing the size of a process – The shell – System Boot and the init process - Implementation of System Calls.

Memory Management: Swapping – Segmentation – Demand Paging – A Hybrid System with Swapping and Demand Paging. The I/O Subsystem: Driver Interfaces – Disk Drivers – Terminal Drivers – Streams. Inter Process Communication (IPC): Process Tracing – System V IPC – Network Communications – Sockets. Multiprocessor Systems: Problem with Multiprocessor Systems – Master and Slave processors – Semaphores

Introduction to Distributed system- Remote Procedure Call – Logical clocks- Vector clocks -  Distributed mutual exclusion – Non token based algorithms – Token based algorithms – Deadlock algorithms – Election algorithms – Byzantine agreement problem – Load distributing algorithms  - Performance comparison. Distributed File system Design-the file service interface-the directory server interface-semantics of file sharing.Distributed file system implementation-file usage-system structure0-caching-replication-an example sun’s network file system

Real time and Mobile Operating Systems – Basic Model of Real Time Systems – Characteristics – Applications of Real Time Systems – Real time Task Scheduling –Handling resource sharing . Mobile Operating System – Micro Kernel Design – Client Server Resource Access – Processes and Threads – Memory Management - - File System.Case study MACH: Introduction to MACH - Process management in MACH-processes-thread scheduling- memory management in MACH-Virtual memory-memory sharing

[2].      Andrew S Tanenbaum, “Distributed Operating Systems”, PHI, reprint 2006.

[3]     Rajib Mall, “Real Time Systems: Theory and Practice”, Pearson Education, India, 2006

[2].      Arnold Robbins, “UNIX in a Nutshell”, In a Nutshell series, 3rd Edition, reprint 2007.

[3].      George Coulouris, Jean Dollimore and Tim Indberg, “Distributed Systems Concepts andDesign”, 3rd Edition, Pearson Education,      2002

[4].   Pradeep K Sinha, “Distributed Operating Systems – Concepts and Design”, PHI, 2006

2.      Tutorials / Assignments / Tests / Quiz / Seminar.

3.      Attendance is part of the CIA component.

4.      Minimum percentage to pass in each paper is 50% (CIA + ESE).

Business intelligence (BI) is a broad category of application programs and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications support the activities of decision support, query and reporting, online analytical processing (OLAP) and analysis.

CO1: Understand the Technical components of BI

O2: Analyze the process involved in BI

CO3: Visualize the data and Generate Reports using report builder and power pivot

Defining Business Requirements: Introduction, Uncovering Business Value, Prioritizing the Business Requirements. Designing the Business Process Dimensional Model: Concepts and Terminology, Additional Design Concepts and Techniques. The Toolset: Microsoft DW/BI Toolset, Architecture and Overview of the Toolset.

Creating the Relational Data Warehouse: Getting started, completing the physical design, Define storage and create constraints and supporting objects.

Master Data Services: Managing Master Reference Data, Introducing SQL Server MDS, Creating a Simple Application.

Design and Develop the ETL System: Developing the ETL Plan, Introducing SQL Server Integration Services, Extracting Data, Cleaning and Confirming Data, Delivering Data for Presentation.

Core Analysis Services OLAP Database: Overview, Design the OLAP structure-Planning, getting started, Data source view, Dimension design, Editing dimension, Editing Cube, Physical Design Consideration.

Building the BI Applications in Reporting Services: Overview, High Level Architecture for Reporting, System Design and Development Process, Building and Delivering Reports, Reporting Options.

Power Pivot and Excel: Using Excel for Analysis and Reporting, Architecture, Creating and using Power Pivot Databases, Power pivot Monitoring and Management.

Case study: Any Two Applications (eg. Healthcare, Retail Industry)

[2]. Mike Biere,“Business Intelligence for the Enterprise” , second edition, 2009

CIA 60%

ESE 40%

This course provides different ways to solve business intelligence problems using various data structures and writing programs for these solutions

CO1: Understand the concepts of data structure

CO2: Implement basic data structures like arrays and linked lists

CO3: Demonstrate optimal approaches to solve sorting and graph problems

CO4: Analyse the process involved in Business Intelligence

1. Implementation of insertion, selection and merge sorting Methods

2. Implementation of stacks

3. Implementation of queues

4. Implementation of Linked list

5. Implementation of two way linked list

6. Implementation of circular linked List

7. Implementation of Binary search tree

8. Implementation of radix and heap sort

9. Implementation of DFS for graphs

10. Implementation of BFS for graphs

Pre-Lab: Software Installation and Configuration

1. Create a Relational Data bases

2. Design an ETL System using SSIS

3. Design a dimensional model using SSAS

4. Design reports using SSRS

5. Design a dashboard using power pivot

2. Joy Mundy, Warren Thornthwaite and Ralph Kimball, “The Microsoft Data Warehouse Toolkit: With SQL Server 2008 R2 and the Microsoft Business Intelligence Toolset”, John Wiley & Sons, 2nd edition, 2011.

1. Robert Kruse, Tondo C L, Bruce Leung, Data Structures & program Design In C, Pearson Education, 2nd Edition, 2004.

2.Gert H. N. Laursen and Jesper Thorlund, “Business Analytics for Managers: Taking Business Intelligence beyond Reporting Paperback” , 2013

40% ESE

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.

CO1: Understand the basic concepts of Computer Graphics.

CO2: Apply geometric conversions on graphical objects.

CO3: Implement the Graphics concepts using OpenGL.

Applications, Overview of Graphics Systems – Video display devices, Raster-scan systems, Graphics software, Introduction to OpenGL. Graphics Output Primitives Coordinate Reference Frames, Two-Dimensional frame in OpenGL, Point Functions, Line Functions, Line-Drawing Algorithms – DDA, Bresenhams, Curve Functions, Midpoint Circle Algorithm, and Display-window reshape function.

Self-Learn: Area filling, Display lists, Basic colors, Attribute functions.

Basic two-dimensional geometric transformations, Homogeneous Coordinates, Composite transformations, Geometric transformations in three-dimensional space, Translation, Rotation, scaling, composite three-dimensional transformations, OpenGL geometric transformation functions

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.

Self-Learn: Ray-tracing and Texture mapping.

Two-dimensional viewing pipeline, clipping window, Normalization and viewport transformations, 2D viewing functions, Clipping Algorithms – Line clipping – Cohen- Sutherland and Liang-Barsky Line clipping, polygon clipping – Sutherland-Hodgman algorithm.

Three-dimensional viewing concepts – Projections, Three-dimensional viewing pipeline, Projection transformation, Parallel and Perspective projection matrices. 3D viewing functions.

Self-Learn: Other clipping algorithms, Text clipping, and Projection derivations.

Spline representations, Cubic spline interpolation methods, Bezier curves and B-Spline curves. OpenGL approximation-Spline functions.

2. Richard S Wright, Jr. Michael Sweet,Open GL Super Bible, 2nd Edition.

40% ESE

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

CO1: Understand the concepts, principles and methods of Web engineering.

CO2: Apply the concepts, principles, and methods of Web engineering to Web applications  development.

CO3: Correlate with current web technologies.

CO4: Collaborate with web application development software tools and environments currently available  on the market.

RE Fundamentals and Specifics - Principles for RE - Adapting RE Methods - Modeling Fundamentals and Specifics - Modeling Requirements - Content Modeling - Hypertext Modeling - Presentation Modeling - Customization Modeling

Fundamentals and Specifics – Components - Layered Architectures - Data-aspect Architectures - Evolutionary Perspective - Presentation Design - Interaction Design - Functional Design – Outlook

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

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/Server-Interaction - Client Security Issues - Service Provider Security Issues

Fundamentals - Client/Server Communication - Client-side Technologies  - Ajax - Document-specific Technologies - Server-side Technologies - Fundamentals and Specifics of Semantic Web - Technological Concepts and Tools

Software Project Management provides insight to the importance of careful project management. Topics are presented in the same order that they appear in the progression of actual projects 

CO1: Practice project planning activities that accurately forecast project costs, timelines and quality

CO2: Value software projects effectively

CO3: Assess real world projects with a strong knowledge on basic measurements of monitoring and controlling

Introduction to software project management and control whether software projects are different from other types of projects. Scope of project management.Management of project life cycle. Defining effective project objectives where there are multiple stakeholders. Software tools for project management. Project Planning: Creation of a project plan -step by step approach, The analysis of project characteristics in order to select the best general approach, Plan Execution, Scope Management, Use of Software (Microsoft Project) to Assist in Project Planning Activities.

Time Management, Project Network Diagram, Critical path Analysis, PERT, Use of Software (Microsoft Project) to Assist in Project Scheduling. Project Cost Management: Resource planning, Cost Estimation (Types, Expert Judgment, Estimation by Analogy, COCOMO).

Stages, Quality Planning, Quality Assurance, Quality Control, Quality Standards, Tools and Techniques for Quality Control.

Definition, Key to managing People, Organization Planning, Issues in Project Staff Acquisition and Team Development, Using Software to Assist in Human Resource Management, Communication Planning, Information Distribution, Performance Reporting.

Common Sources of Risk in IT projects, Risk Identification, Risk Quantification, Risk Response Development and Control. Project Procurement Management: Procurement Planning, Solicitation, Source Selection, Contract Administration.

[2]. PankajJalote, “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., 2003.

[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, 2012.

ESE (Weightage): 40%

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

CO1: Understand the basic principles of machine learning techniques.

CO2: Demonstrate supervised and unsupervised machine learning algorithms.

CO3: Apply appropriate techniques for real time problems.

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.

Introduction, subset selection, principal component analysis, factor analysis, multidimensional scaling, linear discriminant analysis. Clustering: Introduction, mixture densities, k-means clustering, expectation-maximization algorithm, hierarchical clustering, choosing the number of clusters. Non-parametric: Introduction, non-parametric density estimation, non-parametric classification.

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.

Introduction, optical separating hyperplane, v-SVM, kernel tricks, vertical kernel, defining kernel, multiclass kernel machines, one-class 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.

Introduction, canonical cases for conditional independence, d-separation, 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, d-separation, 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 
Service Learning: Introduction to machine learning applications developed for betterment of society through select case studies.

ESE - 40%

 To complete  a project based on previous semester’s course the student has taken or students Research specialization. The project need to be implemented using hardware and software. 

CO 2:To apply appropriate design/development methodology and Tools

CO3:Competence to work in a team

CO4:Ability to complete the solution as a product

Project will be based on any specialization papers which students are opted for.

ESE 40%

This research inclusive curriculum is designed with two main objectives:

1. Inculcating research culture among the post graduate students.

2.Enhancing employability skills of students by providing necessary research foundation

CO2 - Gather the datasets from various sources (like visiting websites, universities, person, creating individually, etc.

CO3 – Steps in pre-processing / Proposed model design / framework design

There is only CIA for this course. Students should carry out the following tasks:

Literature survey of existing data sets or any primary data sets in the respective area

·         Introduction to topic, existing scenario and applications

·         Literature review (Minimum 25 references)

·         Existing Model and Methodology

·         Concrete problem statement definition

Note: In case the research problem involves no dataset or minimal dataset, then the guide allots marks based on the research work that is done during the semester.

S.NoCriteria for Evaluation         Marks

1Submission of document        35 Marks

2Presentation                        10 Marks

3Attendance                         5 Marks

Cloud computing has become a great solution for providing a flexible, on-demand, and dynamically scalable computing infrastructure for many applications. Cloud computing presents a significant technology trend. The course aims at familiarizing with the basic concepts of cloud computing and its applications.

CO1: Understand the common terms and definitions of virtualization and cloud computing and be able to give examples.

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

CO3: Describe the landscape of different types of virtualization and understand the different types of clouds.

CO4: Illustrate how key application features can be delivered more easily on virtual infrastructures.

cloud computing Overview – Cloud components, Infrastructure, Services -, Applications – Storage, Database services -, Intranets and the cloud – components, Hypervisor applications - First Movers in the CloudYour Organization and Cloud Computing –  When you can use Cloud computing, Benefits, Limitations, Security Concerns, Regulatory Issues.

Cloud Computing with the Titans – Google,  EMC, NetApp, Microsoft, Amazon, Salesforce.com, IBM. The Business case for going to the Cloud - Cloud Computing services- Infrastructure as a Service, Platform as a Service, Software as a Service, Software plus services, How applications help your business, Deleting your data center.

Developing Applications-Google, Microsoft, Intuit QuickBase, Cast Iron cloud, Bungee connect,  Development, Trouble shooting, Application ManagementLocal clouds and Thin ClientsVirtualization in your Organization- why virtualize, How to virtualize, concerns, security-, Server solutions- Microsoft Hyper-V,VMware, VMware Infrastructure, Containers: Using and Managing Containers – Container Basics, Docker and the Hub, Container for Science, Creating your own Container, Secure your VMs and Containers.

Overview-The Basics, storage as a service, Providers, security, Reliability, advantages, cautions, Outages, Theft-,  Cloud storage providers, Standards- Application – Communication, Security -, Client – HTML, Dynamic HTML, JavaScript -, Infrastructure – Virtualization, OVF -, Service – Data, Web service

Developing Applications-Google, Microsoft, Intuit QuickBase, Cast Iron cloud, Bungee connect,  Development, Trouble shooting, Application ManagementLocal clouds and Thin ClientsVirtualization in your Organization- why virtualize, How to virtualize, concerns, security-, Server solutions- Microsoft Hyper-V,VMware, VMware Infrastructure, Containers: Using and Managing Containers – Container Basics, Docker and the Hub, Container for Science, Creating your own Container, Secure your VMs and Containers.

[2].      Ian Foster and Dennis B. Gannon, “Cloud Computing for Science and Engineering”, MIT Press, 2017. 

[2].      Judith Hurwitz, Robin Bloor, Marcia Kaufman and Fern Halper, “Cloud Computing for Dummies”. Wiley- India edition,2010

[3].      Ronald L. Krutz and Russell Dean Vines, “Cloud Security: A Comprehensive Guide to Secure Cloud Computing”. Wiley Publishing, Inc.,2012

[4].      Barrie Sosinky, “Cloud Computing : Bible”, 1st edition, Wiley Publishing, Inc.,2011

ESE (Weightage): 40%

The goal of computer vision is to develop the theoretical and algorithmic basis by which useful information about the world can be automatically extracted and analyzed from a single image or a set of images. This course will cover the fundamentals of ComputerVision.

CO1: Understand the Concepts of Computer Vision , Image Formation and Representation

CO2: Identify different image processing methods like Image Filtering (spatial domain), Mask-based (e.g., correlation, convolution), Smoothing (e.g., Gaussian), Sharpening (e.g., gradient), Edge Detection (e.g., Canny, Laplacian of Gaussian), Interest Point Detection (e.g., Moravec, Harris), Shape representation and Segmentation

CO3: Implement appropriate approach in real-time applications.

CO4: Design tools to process real-time graphic data for research.

Image representations (continuous and discrete)- Edge detection

Monocular imaging system, Orthographic & Perspective Projection, Camera model and Camera calibration, Binocular imaging systems.

Regularization theory, Optical computation, Stereo Vision, Motion estimation, Structure from motion.

Deformable curves and surfaces, Snakes and active contours , Level set representations, Fourier and wavelet descriptors, Medial representations,• Multi-resolution analysis.

Hough transforms and other simple object recognition methods, Shape correspondence and shape matching, Principal Component analysis, Shape priors for recognition.

Application: finding in digital Libraries, organizing collections of images, including what do users want, how well does the system work, Representations of parts of the picture, including segmentation, template matching, shape and correspondence, and clustering and organizing collections, searching and browsing, Images based rendering, Constructing 3D models from image sequences, including scene modeling from registered and unregistered images.

[2].      Richard Szeliski, “Computer Vision and Applications”, New Age Internations (P) Ltd., New Delhi, 2005.

[3].      S. Nagabhushana,“Computer Vision and Image Processing”, New Age Internations (P) Ltd., New Delhi, 2005.

The main objective of the courseis aimed at an entry level study of information retrieval and web mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and web mining are covered, the Course Description: is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations.

CO1: Understand the basic concepts and processes of information retrieval systems and data mining techniques. 

CO2: Demonstrate common algorithms and techniques for information retrieval (document indexing and retrieval, query processing, etc).

CO3: Analyze the quantitative evaluation methods for the IR systems and web mining techniques. 

CO4: Implement the popular probabilistic retrieval methods and ranking principle. 

Introduction to Data mining. Relationship to machine learning. Summarization and feature extraction.Data Preprocessing: Introduction to  preprocessing. Data summarization. Date cleaning. Data integration, Data transformation. Data cube aggregation, attribute subset selection, Dimensionality reduction, Numerosity reduction. Data Discretization, Concept Hierarchy generation.

Inverted indices and Boolean queries. Query optimization. The nature of unstructured and semi-structured text.The term vocabulary and posting lists. Text encoding: tokenization, stemming, lemmatization, stop words, phrases. Optimizing indices with skip lists. Proximity and phrase queries.Positionalindices.Dictionaries and tolerant retrieval.Dictionary data structures. Wild-card queries, permuterm indices, n-gram indices. Spelling correction and synonyms: edit distance, soundex, language detection.

Postings size estimation, sort-based indexing, dynamic indexing, positional indexes, n-gram indexes, distributed indexing

Term weighting, and the vector space model. Parametric or fielded search.Documentzones.The vector space retrieval model.tf.idf weighting. The cosienmeasure.Scoringdocuments.  Map Reduce: Distributed file systems, Map and reduce tasks. Algorithms that use map-reduce: Matrix vector multiplication, Relational algebra operations. Mining Frequent Patterns and Associations: Near-neighbor search, Collaborative filtering, Shingling. Min-hashing and locality  sensitive hashing.

The stream data model, examples of stream sources and queries, sampling data in a stream. Filtering streams, bloom filters, counting distinct elements in a stream. Market-Basket model, Association rules. A-priori algorithm.Classification: Introduction to text classification. Naïve Baye’s models. Spam filtering. K nearest neighbors, Decision boundaries, vector space classification using centroids.Comparative results. Support vector machine classifiers. Kernel function.Evaluation of classification.Micro-and macro-averaging.Learning rankings.

Introduction to the problem.Partitioning methods: K-means clustering; Hierarchical clustering.Latent semantic indexing (LSI).Applications to clustering and to information retrieval.Web Mining: Introduction to  web . Web search overview, web structure, the user, paid placement, search engine optimization/spam. Web measurement.Crawling and web indexes.Near-duplicate detection.Linkanalysis.Web as a graph.PageRank.Machine learning techniques for ranking.

[2].      AnandRajaraman and Jeffery D.ullman, “Mining the Massive”,Cambridge University Press, 2008.

[2].      K.P Soman, Shyamdiwakar and VAjay, “Insight into Data Mining – Theory and Practice”, 6th Ed  print, PHI India, 2012.

[3].      Jiawei Han and MichelineKamber, “Data Mining: Concepts and Techniques”, 2nd Edition,      2006, Morgan Kaufmann Publishers, San Francisco, USA.

The course is designed to provide a real-world project development and deployment environment for the students.

CO 2:To apply appropriate design/development methodology and Tools

CO3:Competence to work in a team

CO4:Ability to complete the solution as a product

It is a full time project to be taken up either in the industry or in an R&D organization.

Student can do project based on the specialization papers which students have opted for. 

Research in Computer Science : Research inclusive curriculum is an initiative by the Department of Computer Science through which regular curriculum of Post Graduate Computer Science courses is augmented with formal research.

(a) Inculcating research culture among the post graduate students

(b) Enhancing employability skills of students by providing necessary research foundation

CO1: Demonstrate their understanding of Research article publication process – correcting review comments.

CO2: Able to produce commercially valuable intellectual property. 

CO3: Able to produce new products/processes/methods/model/Framework.

There is only CIA for this paper. Research work carried out in this semester is divided in two parts.

Modelling and implementation of their research work. Students should perform the following tasks:

 ·         Methodology

 ·         Evaluation and Discussion of Results

 ·         Limitations, Conclusions and Scope for future enhancements

 ·         Plagiarism report

Evaluation rubric for Presentation :