Syllabus for
Master of Computer Applications
Academic Year  (2023)

Master of Computer Applications is a Two year post graduate programme spread over six Trimesters. This programme strives to shape the students into outstanding computer professionals for the challenging opportunities in IT industry. It enables students to evolve from the stereo type thinking to better achievers and prepares them to scale the global standards. Curriculum incorporates the state of the art areas of IT industry to provide opportunity for extended study in an area of specialization.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Problem Analysis: Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines.

PO3: Design/Development of Solutions: Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.

PO4: Conduct Investigations of Complex Computing Problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.

PO5: Modern Tool Usage: Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.

PO6: Professional Ethics: Understand and commit to professional ethics and cyber regulations, responsibilities, and norms of professional computing practices.

PO7: Life-long Learning: Recognise the need, and have the ability, to engage in independent learning for continual development as a computing professional.

PO8: Demonstrate knowledge and understanding of the computing and management principles and apply these to one?s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.

PO9: Communication Efficacy: Communicate effectively with the computing community, and with society at large, about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions.

PO10: Societal and Environmental Concern: Understand and assess societal, environmental, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practices.

PO11: Individual and Team Work: Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary environments.

Continuous Internal Assessment: 50% Weightage

End Semester Examination: 50% Weightage

This course aims to provide fundamental knowledge of mathematical foundations for Computer Science.

Introduction -Elementary theory of sets-Set rules and Set Combinations-Relations-Functions- Discrete Numeric Functions-Addition of Numeric Functions-Multiplication of numeric functions-Multiplication with Scalar Factor to Numeric Function.

Introduction to Logic-Symbolization of Statements-Equivalence of Formula-Propositional Logic-Theory of Inference-Predicate Logic-Inference Theory of Predicate Logic

Introduction-Groups-Semi Groups-Complexes-Product Semi Groups-Permutation Groups-Order of a Group-Sub Groups-Cyclic Groups

Basic Concepts of Automata Theory-Deterministic Finite State Automata (DFA) - Non-deterministic Finite State Automata (NDFA) - Conversion of NDFA to DFA

Introduction-Basic Features of a Turing Machine-Language of a Turing Machine-General Problems of a Turing Machine.

[2] Kenneth H Rosen, “Discrete Mathematics and its Applications”, Tata McGraw Hill, 2016.

[2] Donald F. Stanat and David F. McAllister, “Discrete mathematics in Computer Science”.

50%        50%

To provide extensive knowledge of C programming language to the students. It helps in developing the ability to solve computational problems through programs. Lab component is included to give hands-on experience to the students

Tokens in C, data types and keywords - Decision control structures - Loop control structure.

Functions - Library functions - Function definitions - Prototype - Scope - Storage classes -Call by value - Pointers variable - Definition and initialization - Pointer operators - Calling function by reference - const qualifier with pointers - sizeof operator - Pointer arithmetic - Pointers to functions - Recursion - Recursion and stack.

Arrays - Definition - Initialization - 2D arrays - Memory map of 2D arrays - Pointers and 2D arrays -  Passing Arrays to functions - Strings - Characters - Character handling library - String I/O - Pointers and strings

Structure definitions - Initializing structures - Accessing structure members - Array of structures - Pointers to structures - Using structures with functions - Self referential structures -  typedef – Unions, enums

File processing - Data hierarchy - File and streams - File operations - Sequential-Access file - Random-Access file  - Preprocessors - symbolic constants and macros - File inclusion - Conditional compilation

Lab Exercises:

1.Implement a sample case study: e.g., Bank transaction processing system, Hospital appointment system, Hotel booking system, etc

[2] Byron Gottfried, Programming with C, McGraw Hill, 4th Edition, 2018.

[2] Brian W. Kernighan, Dennis M. Ritchie, The C Programming Language, Pearson, 2nd Edition, 2012.

50%       50%

This course starts with an introduction to the basic concepts in research and leads through the various methodologies involved in the research process. It focuses on finding out the research gap from the literature and encourages lateral, strategic, and creative thinking. This course also introduces computer technology and basic statistics required for conducting research and reporting the research outcomes scientifically, with emphasis on research ethics.

Defining research problem:  Selecting the problem- Necessity of defining the problem- Techniques involved in defining a problem- Ethics in Research.

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- On-line Searching: Database-SCI Finder- Scopus- Science Direct-Searching research articles- Citation Index -Impact Factor -H-index.

Measurement of Scaling: Quantitative-Qualitative,-Classification of Measure scales- Data Collection- Data Preparation.

Scientific Writing: Significance- Steps- Layout- Types- Mechanics and Precautions- Paper writing for international journals- Writing scientific report.

Latex: Introduction-Text-Tables- Figures- Equations- Citations- Referencing and Templates (IEEE style).

[2] Zina O’Leary, The Essential Guide of Doing Research, 3rd Edition, SAGE Publications Ltd, 2017.

[2] Kumar, Research Methodology: A Step by Step Guide for Beginners, 4th Edition, SAGE Publications Ltd, 2014.

This course begins with an introduction to organizational Basic building block diagram of a digital computer system. As the course progresses each major block ranging from Processor to I/O will be discussed in their full architectural detail. The course talks primarily about Computer Organization and Architecture issues, Architecture of a typical Processor, Memory Organization, I/O devices and their interface and System Bus organization etc.

Basics Of Digital Electronics: Multiplexers and De multiplexers, Decoder and Encoder, Registers., shift registers, Introduction to combinational circuit, introduction to sequential circuits

Register Transfer and Micro Operations: Register Transfer Language and Register Transfer, Bus and Memory Transfer, Logic Micro Operations, Shift Micro Operations, Design of arithmetic logic unit., arithmetic microoperations

Data representation: signed number representation, fixed and floating point representations, character representation. Computer arithmetic - integer addition and subtraction, ripple carry adder, carry look-ahead adder, etc. multiplication - shift-and-add, Booth multiplier, carry save multiplier, etc. Division - non-restoring and restoring techniques, floating point arithmetic.

Fundamental concepts – Execution of a complete instruction – Multiple bus organization – Hardwired control – Micro programmed control -Basic concepts – Data hazards – Instruction hazards – Influence on Instruction sets – Data path and control consideration – Superscalar operation

Memory Hierarchy and Processor Vs Memory Speed– Semiconductor RAMs – ROMs – Speed – size and cost – Cache memories – Performance consideration – Virtual memory- Memory Management requirements – Secondary storage

Introduction to Parallel Processing : Pipelining, Characteristics of multiprocessors, Interconnection Structures, parallel processing

Latest technology and trends in computer architecture : multi-cores processor., next generation processors architecture, microarchitecture, latest processor for smartphone or tablet and desktop

Multiprocessors : Categorization of multiprocessors(SISD,MIMD,SIMD.SPMD), Introduction to GPU

2. Computer Systems Architecture – M.Moris Mano, IIIrd Edition, Pearson/PHI,2017

2. Structured Computer Organization – Andrew S. Tanenbaum, 4th Edition PHI/Pearson, 2006

3. Fundamentals or Computer Organization and Design, - Sivaraama Dandamudi Springer Int.  V Edition, 2006

4. Computer Architecture a quantitative approach, John L. Hennessy and David A. Patterson, Fourth Edition Elsevier, 3RD Edition 2012

50%       50%

To provide a strong foundation for database design and application development and understand the underlying core database concepts and emerging technologies

Using High-Level 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 -  Enhanced Entity Relationship Model - Relational Database Design by ER- and EER-to-Relational Mapping  

Design Guidelines for Relation Schemas - Functional Dependencies - Normal Forms Based on Primary Keys - Second and Third Normal Forms - Boyce-Codd Normal Form - Multivalued Dependency and Fourth Normal Form - Join Dependencies and Fifth Normal Form - - File Organization - Organization of Records in Files - Ordered Indices - B+ Tree Index Files - Static Hashing - Bitmap Indices

Transaction - Introduction to transaction processing- transaction and system concept- Desirable properties of transaction- Transaction support in SQL- concurrency control techniques – Two phase Locking techniques for concurrency- Concurrency Control Based on Timestamp Ordering. Recovery Concepts.Distributed databases: Distributed Database concepts- Types - Data Fragmentation- Replication- Allocation Techniques. Overview of Transaction Management - Overview of Concurrency Control and Recovery

Definition and Introduction-Sorted Ordered Column-Oriented Stores- Key/Value Stores. Interacting with NoSQL, NoSQL Storage Architecture: Working with Column-Oriented Databases-HBase Distributed Storage Architecture, NoSQL Stores: Accessing Data from Column-Oriented Databases Like HBase-Querying Redis Data Stores- Querying in Neo4J

DDL commands, DML commands, TCL commands,  NoSQL CRUD operations,  NoSQL aggregate functions, Data manipulation using CASSANDRA

[2] Shashank Tiwari, Professional NoSQL, Wrox Press, Wiley, 2021,

[2] O’neil Patric, O’neil Elizabeth, Database Principles, Programming and Performance, Argon Kaufmann Publishers, 2nd Edition, 2002.

[3] Ramakrishnan and Gehrke, Database Management System, McGraw-Hill, 3rd Edition, 2003.

50%          50%

This course covers programming paradigms brought in by Python with a focus on Regular Expressions, List and Dictionaries. It explores the various modules and libraries to cover the landscape of Python programming

Underlying mechanism of Module Execution- Sequences, Mapping and Sets- Dictionaries- Functions - Lists and Mutability - Custom and built-in modules.

Lab Exercises:

1. Demonstrate use of Python data structures

2. Demonstrate Lists  and Dictionary comprehension

3. Demonstrate Custom modules with functions

Classes: Classes and Instances-Inheritance—Polymorphism- Abstract Classes-Exceptional Handling- Regular Expressions using “re” module.

Lab Exercises:

4. Demonstrate use of object- oriented programming concepts

5. Demonstrate exceptional handling

6. Implement ‘re’ module

Computation on NumPy-Aggregations-Computation on Arrays-Comparisons, Masks and Boolean Arrays-Fancy Indexing-Sorting Arrays-Structured Data: NumPy’s Structured Array. Introduction to Pandas Objects-Data indexing and Selection-Operating on Data in Pandas-Handling Missing Data-Hierarchical Indexing.

Lab Exercises:

7. Implement NumPy features

8. Demonstrate Pandas with its operations  

9. Apply different types of indexing methods using NumPy and Pandas 

Basic functions of Matplotlib-Simple Line Plot, Scatter Plot. Introduction to Tkiner module-Root Window-Widgets-Button-Label-Message-Text-Menu-Listboxes-Spinbox-Creating tables.

Lab Exercises:

10. Apply regular expression for form validation.

11. Demonstrate the use of “Matplotlib” modules to plot line and scatter plot

Introduction-Web framework-creating model to add database service- Django administration application.

Basic Database Operations and SQL, Databases and Python, The Python DB-API, Connection Objects Databases and Python: Adapters Examples of Using Database Adapters, A Database Adapter Example Application.

Lab Exercises:

12. Create a web application using Django framework.

13. Establish database connectivity for a GUI application using all the appropriate widgets and demonstrate data manipulation and visualization

[2]   Python Tutorial,  Guido Rossum, CreateSpace Independent Publishing Platform, 2018

[3]   Python Programming Fundamentals, Kent D. Lee, Springer Publications, 2nd  Edition, 2015

[2]   Programming with Python, T. R. Padmanabhan, Springer Publications, 2019.

[3]   Murach's Python Programming (2nd Edition),  Joel Murach, Michael Urban, Mike Murach & Associates, Incorporated, 2021

Web Resources:

1.     https://cfm.ehu.es/ricardo/docs/python/Learning_Python.pdf

2.     Python Tutorial

50%       50%

On completion of this course, a student will be familiar with full stack and able to develop a web application using advanced technologies and cultivate good web programming style and discipline by solving the real-world scenarios

Internet - Client/Server model -Web Search Engine-Web Crawling-Web Indexing-Search Engine Optimization and Limitations-Web Services –Collective Intelligence – Mobile Web –Features of Web 3.0-HTML vs HTML5-Exploring Editors and Browsers Supported by HTML5-New Elements-HTML5 Semantics-Canvas-HTML Media.

Git-commit-rollback-remote repository- GitHub-merge conflict-CSS specificity rule-Pseudo selectors-media queries-flexbox-responsive web design-transition-Bootstrap 5 responsive grid-Components ( Navbar, tables, heroes, carousel, modal etc.,) - font awesome icons

Lab Exercises:

1. Identify a domain of your choice, list out ten entities in the domain. For each entity, identify minimum 10 attributes and assign the data type for each attribute with proper justification.

2. Develop static pages for a given scenario using HTML

3. Demonstrate Geolocation and Canvas using HTML5

XML-Documents and Vocabularies -Versions and Declaration -Namespaces JavaScript and XML: Ajax-DOM based XML processing Event-Transforming XML Documents -Selecting XML Data:XPATH - Template based Transformations: XSLT - Displaying XML Documents in Browsers - Evolution of AJAX - Web applications with AJAX - AJAX Framework.

Lab Exercises:

4. Write an XML file and validate the file using XSD

5. Demonstrate XSL with XSD

JavaScript Implementation - Use Javascript to interact with some of the new HTML5 apis -Create and modify Javascript objects- JS Forms - Events and Event handling-Async await-JS Navigator-JS Cookies - Introduction to JSON-JSON vs XML-JSON Objects-fetch API

Lab Exercises:

6. Write a JavaScript program to demonstrate Form Validation and Event Handling

7. Implement web application using AJAX with JSON

8. Demonstrate to fetch the information from an XML file (or) JSON with AJAX

Package Manager (NPM) - ES6- Introduction to React.js - Create React App & React file structure - JSX and Components -passing and destructuring props - React Hooks - Axios - Images and Forms - Conditional Rendering - Routes - Redux

Lab Exercises:

9. Create a web application using React Js with Forms.

10. Develop SPA ( Single Page Application)  with React JS

11. Implement CRUD Operation using React JS.

Introduction to Node.js - Express JS - Node mailer - NODE JS WITH MYSQL  - Introduction to MySQL - Performing basic database operation(DML) (Insert, Delete, Update, Select)-Prepared Statement- Uploading Image or File to MySQL- Retrieve Image or File from MySQL - bcrypt hashing

Lab Exercises:

12. Demonstrate Node.js file system module.

13. Implement CRUD operation with MySQL using Node.JS

[2] Modern Full-Stack Development: Using TypeScript, React, Node.js, Webpack, and Docker,  Frank Zammetti,  APRES, 1st Edition, 2020

[2] Laura Lemay, Rafe Colburn & Jennifer Kyrnin, Mastering HTML, CSS & Javascript Web Publishing, BPB Publications, 1st Edition, 2016.

Web Resources:

[1] www.w3cschools.com

[2] https://fullstackopen.com/en/part1/introduction_to_react

50%       50%

The Course provides solid fundamental knowledge of software engineering concepts to the students and it prepares them to develop the skills necessary to handle software projects. It also enables the students to apply software engineering principles to develop quality software applications.

A generic process model – Defining a framework activity, identifying a Task Set, Process - Prescriptive Process Models-Specialized Process Models. Requirements Engineering- Developing use cases, Elements of the requirements Model, Analysis pattern, negotiating requirements, validating requirements-Latest Methodology-RAD, DevOps, Fish Model, SCRUM Agile Modeling- Practicing with Rational Rose / other Open Source for all the phases of SDLC

The design process-Design concepts – Abstraction, Architecture, Patterns, Separation of concerns, Modularity, information hiding, Functional Independence, refinement, Aspects, Refactoring, Design classes, The design Model – Data Design elements, Architectural Design elements, Interface Design Elements, Component-Level Design elements, Deployment’s level Design elements

Basic Design Principles, Component-level Design guidelines, Cohesion, Coupling, Functional design at the Component level, Designing traditional components–Component based development-Domain Engineering, Component qualification, Adaptation, and Composition, Analysis and Design for reuse. User Interface Analysis and Design models, COCOMO II Model.

Software Quality- Software testing fundamentals- internal and external view of testing, White-box testing, Basic path testing - control structure testing - Black- box testing- Strategic Approach to Software Testing-verification and validation, unit testing-Integration Testing-Unit testing in OO context, Integration testing in OO context, validation testing, system testing.

Metrics in the process and project domains-Metrics for software quality, The project planning process, Software scope and Feasibility, Resources, software project estimation, Decomposition techniques- DevOps- Empirical estimation models, Software equation.

[2]  Sommerville, Ian, Software Engineering, Addison Wesley, 9th Edition, 2011.

[2]  Stephen R. Schach, Software Engineering, Tata McGraw-Hill Publishing Company Limited, 2007.

Web Resources:

[1] www.nptel.ac.in  

50%      50%

This course covers the concept of applied statistics,  probability and R tool in computational perspective. It explore the practical experience of statistics and probability using R programming

Basic calculation - Getting Help - Installing Packages - Data and programming : Data Types, Data Structures, programming Basics

Lab Exercises:

1. Perform basic calculations using R data structures(Vector, Matrices, List, Data Frames)
2. Reshape data structures

 Introduction to Statistics and Data, Types of Data -Quantitative Data, Qualitative Data, Data, Multivariate Data etc. Features of Data distributions - Center, Spread, Shape, Symmetry, Skewness and Kurtosis, Stem and Leaf Diagrams, Frequency Distributions and Histogram, Measures of Center - Mean, Median, Mode, Measures of Spread - Range, Variance, Standard Deviation, Interquartile range, Measures of Relative Position: Quartiles, Percentiles.

Plotting - Histogram, Bar plot, Box plot, Scatter Plot, Pie chart.

Lab Exercises:

1. Calculate descriptive statistics
2. Visualize Data using plots(Bar, histogram, pie, scatter, Box)

Hypothesis Tests in R - One sample t-Test Review and example, Two sample t-Test Review - and example  - Simulation, Simple Linear Regression - Modeling, Least square approach, The lm function - Maximum likelihood Estimation(MLE) Approach, Simulating SLR, Analysis of Varience - One-Way ANOVA, Two-Way ANOVA

Lab Exercises:

1. Build simple linear regression model
2. Perform a one-way analysis of variance
3. Perform a Two-way analysis of variance

Sample Spaces - Events - Model Assignments - Properties of Probability - Counting Methods - Conditional probability - Independent Events - Bayes' Rule - Random Variables

Lab Exercises:

1. Demonstrate conditional probability
2. Demonstrate Bayes' rule

Healthcare - Finance - Digital Marketing- Environment-Sports

Lab Exercises:

1.  Explore all learned statistical concepts using dataset of any domain.

[2]   Introduction to Probability and Statistics Using R, G. Jay Kerns,  Lulu.com, 2016.

[2]   Applied Statistics with R- A Practical Guide for the Life Sciences,Justin C. Touchon, Oxford university press, 2021.

[3]   SimpleR – Using R for Introductory Statistics, John Verzani

[4]   A Handbook of Statistical Analyses Using R, Brian S. Everitt and Torsten Hothorn

[5]   Probability and Statistics with Examples using R,  Siva Athreya, Deepayan Sarkar, and Steve Tanner

Web Resources:

1.      https://book.stat420.org/applied_statistics.pdf

2.      https://eleuven.github.io/statthink/ChapCaseStudies.html#physical-strength-and-job-performance

3.      https://wwwuser.gwdg.de/~cscherb1/content/Statistics%20Course%20files/R-Manual%20Goettingen.pdf

4. https://cbb.sjtu.edu.cn/~mywu/bi217/usingR.pdf

50%      50%

To understand and appreciate the different functions of Operating Systems

Concepts - Operating System Definition – Operating System operations – Kernel Data Structures - Operating System Services - System Calls - Linkers and Loaders – Process Management – Concepts - Process Concept – Kernel Level Data Structures for Process Management - Operations on Process IPC Basics – IPC in Shared-Memory Systems – IPC in Message-Passing Systems – Examples of IPC Systems – Pipe, FIFO, Message Queue

File-System Interface - File Concept – File Operations - Kernel Level Data Structures for File Management - Operations on Files File-System Implementation – File System Structure - File System Operations - Directory Allocation - Allocation Methods – Free Space Management – Kernel Level Data Structures for handing open files.

Multi-Threading – Overview – Multi-Threading Models – Thread Libraries Thread Synchronization – Critical Section – Synchronization Objects

Main Memory – Conceptual background – Contiguous Memory Allocation – Paging – Swapping Virtual Memory – Background – Demand Paging – Page Replacement – Thrashing

Process Related commands – Debugging Commands – process synchronization - shell scripting – file related commands – system calls - Socket Programming

[2]   Andrew S Tanenbaum & Herbert Bos, Modern Operating Systems, Pearson, 4th Edition, 2014

[2]   Mention the Book Title, Author Name(s), Publisher Name, Edition, Year

[3]   Digital Computer Fundamentals, Floyd, Thomas L, Pearson International, 11th Edition, 2015

 Web Resources:

1.      www.w3cschools.com

2.      https://archive.ics.uci.edu

50%      50%

To have hands on experience in developing a software project by using various software engineering principles and methods in each of the phases of software development. Ability to translate end-user requirements into system and software requirements  Able to identify and formulate research problem, conduct critical research review based on the domain

Each student will be encouraged to develop a project based on the societal and institutional needs.    At the end of the Course the students will be submitting design document / literature review document in the IEEE format.

Option – I :Software Development

1. Domain Identification, Problem Identification, Requirement Analysis for the specific Problem  - 15 Hours
2. Preparation of SRS Document, DFD, Design the Modules - 15 Hours

Option – II :Research Project

1. Domain Identification, Conduct the Critical review on the selected research problem, Identify the Research Gap - 15 Hours
2. Formulate research questions, Collect the data based on the research questions, Propose Novel methodology to solve the research issues - 15 Hours

50%      50%

To provide extensive knowledge of data structures and algorithms using C language to the students. It helps in developing the ability to solve computational problems through programs. Lab component is included to give hands-on experience to the students. It includes linked lists, stacks, queues, trees, heaps, hash tables, and graphs

Abstract Data Types - Arrays, Limitation of the Array, Records & Pointers-About   Arrays, Records & Pointers; Their   Implementation   in Memory, Using One Dimensional Array& Two Dimensional, About Record & Pointers. Linked List - Concept of Singly Linked List, Operations on Linked List, Inserting and   Removing Nodes from a List, Array Implementation   of Lists, Implementation Over Linked List, Doubly Linked List, Generalized List.

Lab Programs

1. Implement Matrix manipulation on Arrays
2. Implement linked list and its operations

Stacks- Definition   and Example, Primitive Operations, Stack as an ADT, Implementation   of Stacks as An Array and Linked List, Operations on Stacks, Stack Stored as A Linked List, Arithmetic   Expression, Converting an Expression   from Infix to Postfix.

Queues - Definition   And examples Of Queues, Queues   as An Abstract Data Type, Queues Stored   as a Linked List, Circular Queue, Implementation of Queues as An   Array and Linked List, Operations on Queues, Priority   Queue & Dequeue.

Lab Programs

1. Application of Stack (convert an infix expression to the postfix form)
2. Queue Operations using Linked List

Searching - Linear Search, Binary Search, Hashing: hash tables, hash functions, collision resolution‐separate chaining, open addressing‐linear probing, quadratic probing, double hashing – Patter matching: Naïve / KMP

Sorting: Bubble Sort, Insertion Sort, Selection Sort, Merge and Quick sort along with time complexity

Lab Programs

1. Implementation of Linear and Binary Search
2. Implementation of Quick / Merge Sort

Trees- Definition of Trees, Basic Terminology of Trees, Binary Tree, Binary Tree Representation as An Array & Linked List, Application of Trees, Binary Tree Traversal: In-Order, Pre-Order, Post-Order - Threaded Binary Tree, Height Balance Tree, B-Trees, Binary Search Trees, Construction of BST Operations‐ Searching, Insertion and Deletion, AVL Trees, Height of an AVL Tree, Operations – Insertion, Deletion and Searching.

Lab Programs

1. Implementation of Tree Traversal
2. Construction of BST and operations

Graphs: Basic Terminology of Graphs, Implementation    of Graphs as An Arrays& Linked List, Operation on Graphs, Graphs Traversals: Breadth First Search, Depth First Search – Topological Sort – Minimum Spanning Tree: Prims and Kruskals

Lab Programs:

1. Implementation of Graph Traversal
2. Program to construct Minimum Spanning Tree

[2] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, Introduction to Algorithms, MIT Press, 3rd Edition, 2009

[2] Horowitz Sahni Anderson-Freed, Fundamental of Data Structures in C, Universities Press, Reprint, 2008.

[3] Yashavant Kanetkar , Data Structures Through C, BPB Publications, 2019.

50%      50%

This course will help the learner to gain sound knowledge in object-oriented principles, GUI application design with database, and enterprise application design with Servlets

Introduction to Object Oriented Programming (OOP)

Object-Oriented Programming (OOP) Principles- Class Fundamentals - Declaring Objects - Introducing Methods - Overloading methods – Constructors - Parameterized Constructors - this Keyword.

Class Features

Garbage Collection - the finalize () Method - Introducing Access Control - Understanding static - Introducing nested and inner classes - String class - String Buffer Class - Command Line Arguments.

 Lab Exercises:

1. Identify a domain of your choice, list out ten entities in the domain. For each entity, identify minimum 10 attributes and assign the data type for each attribute with proper justification.

2. Implement the concept of class, data members, member functions and access specifiers.

3. Implement the concept of function overloading & Constructor overloading.

Inheritance in Java

Inheritance Basics - Multilevel Hierarchy- Using super - Method overriding - Dynamic Method Dispatch- Abstract keyword- 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.

Multithreading Java

Thread Model - Life cycle of a Thread - Java Thread Priorities - Runnable interface and Thread Class- Thread Synchronization – Inter Thread Communication.

Lab Exercises:

4. Implement String and String Buffer classes.

5.Implement the concept of inheritance, super, abstract and final keywords.

6.Implement the concept of package and interface.

7.Implement the concept of multithreading.

Generics

Generics Concept - General Form of a Generic Class – Bounded Types – Generic Class Hierarchy - Generic Interfaces – Restrictions in Generics.

Lambda Expression

Introduction to Lambda expression- Block Lambda Expressions - Generic Functional Interfaces - Passing lambda expressions as arguments - Lambda expressions and exceptions- Lambda expressions and variable capture.

The Collections Framework

The Collections Overview – Collection Interface – List Interface – Set Interface – SortedSet Interface – Queue Interface - ArrayList Class – LinkedList Class – HashSet Class – Using an Iterator – The For Each Statement. Working with maps – The map interfaces, the map classes. Comparators- the collection algorithms

Lab Exercises:

8. Implement the concept of Generics

9. Implement the concept of the lambda expression

10. Implement the concept of a collection framework

JDBC

Introduction to JDBC- Connecting to the database- Basic JDBC Operations – Essential JDBC Classes – JDBC Drivers – JDBC-ODBC Bridge – Connecting to a database with driver manager – JDBC database URL.

JAVA BEANS

Java beans - Advantages of Beans – Introspection- Bound and Constrained Properties – Persistence – Customizers - The JavaBeans API.

JAVA SWING

Swing Basics – Components and Containers – JLabel and ImageIcons- JTextField – Swing Buttons – JTabbedPane – JScrollPane – JList – JComboBox – JTable – Swing Menus.

Lab Exercises:

 11. Implement the concept of JDBC

12. Implement the concept of java beans

13. 13. Implement the concept of java swing

JAVA SERVLETS

Servlets Basics – Life Cycle of a Servlet –A Simple Servlet - The Servlet API – Servlet Interfaces – Generic Servlet Class- HttpServletRequest Interface – HttpServeltResponse

JSP

The JSP development model – component of jsp page – Page directive – Action – scriptlet – JSP expression, JSP Syntax and semantics, JSP in XML.

Lab Exercises:

14. Implement the concept of java servlets

15. Implement the concept of JSP

[2] The complete reference JSP 2.0, Tata McGraw- Hill, 2nd Edition, Phil Hanna

[3] Cay S Horstmann, Core Java Volume 1 Fundamentals, Prentice Hall, 11th Edition, 2018

50%      50%

This course aims to set the foundation for computer networks and introduce the cryptographic approaches. The course covers the communication process between devices with a standard set of protocols based on the Internet model (TCP/IP). The last two units present the cryptographic approaches used for network security

Data Communications - Data Transmission: Concepts and Terminology - Analog and Digital Data Transmission - Transmission Impairments - Transmission Media - Guided Transmission Media - Wireless 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 - Circuit Switching Networks - Circuit Switching Concepts - Packet-Switching Principles

Congestion Control in Data Networks - Congestion Control - Traffic Management - Congestion Control in Packet - Switching Networks - High-Speed LANs: The Emergence of High-Speed LANs - Ethernet - 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

Introduction to Cryptography and Data Security - Stream Ciphers - Block Cipher - The Data Encryption Standard (DES) and Alternatives - The Advanced Encryption Standard (AES) - Introduction to Public-Key Cryptography - The RSA Cryptosystem - Public-Key Cryptosystems Based on the Discrete Logarithm Problem - Elliptic Curve Cryptosystems

Digital Signatures - The Digital Signature Algorithm (DSA) - Hash Functions - Message Authentication Codes (MACs) - Principles of Message Authentication Codes - MACs from Hash Functions: HMAC - Key Establishment

[2].  Bart Preneel, “Understanding Cryptography”, Springer Heidelberg Dordrecht London New York, 2010.

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

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

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

Web Resources:

1. https://studyopedia.com/computer-networks/introduction-computer-networks/
2. https://www.johannes-bauer.com/compsci/ecc/
3. https://www.tutorialspoint.com/dsl/dsl_adsl_fundamentals.htm

50%      50%

This course helps to preprocess and analyze data, choose relevant models and algorithms for respective applications and to develop research interest towards advances in data mining.

Data Mining Introduction: An overview of Data Mining – Kinds of data and pattern to be mined –Technologies – Targeted Applications - Major Issues in Data Mining – Data Objects and Attribute Types – Measuring Data Similarity and Dissimilarity

Data Preprocessing: Data Cleaning –Data Integration–Data Reduction–Data Transformation – Data Discretization

Basic Concepts – Frequent Itemset Mining Methods – Apriori Algorithm-Generating Association Rules from Frequent Itemsets – Pattern Evaluation Methods – Pattern Mining in Multilevel, Multidimensional space – Constraint-Based Frequent Pattern Mining – Mining Compressed or Approximate Patterns – Pattern Exploration and Application

Classification – Model Evaluation and Selection – Techniques to Improve Classification Accuracy – Classification by Backpropagation – Support Vector Machines – Learning from Neighbors.

Cluster Analysis – Definition – Types of Data in Cluster Analysis, Clustering methods– Partitioning Methods – k-Means– k-Medoids– Hierarchical Methods –Agglomerative versus Divisive Hierarchical Clustering –BIRCH–Density-Based Methods–DBSCAN

Outliers and Outlier Analysis – Clustering-Based Approach – Classification-Based Approach – Mining Complex Data Types – Data Mining Applications.

[2]   Data Mining Techniques, Arun K Pujari, Second Edition, Universities Press India Pvt. Ltd.2010

[2]    Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark A. Hall, Morgan and Kaufmann Publisher, Third Edition,2014

 Web Resources:

https://data-flair.training/blogs/data-mining-tutorial/

50%      50%

The main objective of this course is to introduce the basics of the accounting and financial management for the domain specific application development.

Introduction to financial accounting - The accounting equation and financial statements - The accounting cycle and adjusting entries - Cash flow statement and financial analysis

Introduction to managerial accounting - Cost behavior and cost-volume-profit analysis – Job - costing and process costing-Budgeting and variance analysis

Introduction to financial management - Time value of money and discounted cash flows - Risk and return, portfolio theory, and capital asset pricing model (CAPM) - Capital budgeting and financing decisions

Financial markets and intermediaries - Stocks, bonds, and other securities - Derivatives and  - hedging - Investment banking and mergers and acquisitions

Financial statement analysis - Ratio analysis and benchmarking - Forecasting and valuation models - Corporate governance and ethical considerations

50%     50%

The main objective of this course is to introduce the basics of the econometrics for the domain specific software application development.

Overview of econometrics as a field - Theoretical concepts and empirical methods - Types of data and variables in econometrics - Probability and statistical inference

Simple linear regression model - Multiple linear regression model - Estimation and inference in linear regression - Assumptions and diagnostics in linear regression

Nonlinear regression models - Panel data models and fixed effects models - Instrumental variable estimation - Time series models and forecasting

Counterfactual analysis and causality - Experimental and quasi-experimental designs - Regression discontinuity and difference-in-differences - Propensity score matching and sensitivity analysis

Microeconometric applications (e.g., labor, education, health) - Macroeconometric models and forecasting - Program evaluation and policy impact analysis - Ethics and communication in econometrics research

50%          50%

The main objective of this course is to introduce the basics of the social science domain for social application development.

Introduction to computational social science and relation to computer science - Theoretical and methodological foundations of CSS - Data types and sources in CSS (e.g., text, network, geospatial data) - Research design and ethical considerations in CSS research

Data acquisition and scraping using web APIs and libraries (e.g., Requests, BeautifulSoup) - Data cleaning and validation using regular expressions and string manipulation – Data transformation and normalization using Pandas and Numpy libraries - Exploratory data analysis and visualization using Matplotlib and Seaborn libraries

Machine learning algorithms for social data (e.g., classification, clustering, dimensionality reduction) - Model selection and evaluation using cross-validation and hyperparameter tuning - Deep learning models for natural language processing (e.g., word embeddings, Convolutional neural networks) - Social network analysis using graph algorithms (e.g., centrality measures, community detection)

Collecting and processing social media data using APIs and web scraping – Sentiment - analysis and opinion mining using natural language processing techniques (e.g., lexicons, machine learning) - Topic modeling and clustering using Latent Dirichlet Allocation (LDA) and K-means algorithms - Social media network analysis and visualization using NetworkX and Gephi libraries

Social computing and crowdsourcing applications (e.g., recommendation systems, human computation) -Algorithmic fairness and ethical issues in CSS - Human-Computer Interaction (HCI) research using CSS techniques - Future directions and emerging topics in CSS and computer science

50%          50%

This course provides an introduction to cognitive psychology, covering its history, theories, and research methods, as well as exploring attention, perception, memory models, encoding, storage, retrieval, language acquisition, comprehension, non-verbal communication, problem solving, decision-making, creativity, and cognitive tools. It also examines the intersection of cognitive psychology and AI, including cognitive models, the role of cognitive psychology in AI development, human-AI interaction, and emerging topics in the field.

Overview of cognitive psychology as a field  (History, Theory and Research) - Theoretical approaches to studying cognition - Basic concepts and methods in cognitive psychology - Attention and perception

Models of memory and forgetting - Encoding, storage, and retrieval processes - Long-term memory structures and organization - Factors influencing memory performance

Language acquisition and development - Language processing and comprehension - Speech perception and production - Non-verbal communication and gestures

Decision making and reasoning - Heuristics and biases in judgment - Creativity and innovation - Problem solving strategies and cognitive tools

Cognitive architectures and models (e.g., ACT-R, SOAR, CLARION) - Cognitive psychology in the development of AI and machine learning algorithms - Human-AI interaction and explainability - Future directions and emerging topics in cognitive psychology and computer science

CIA 50%

• To inculcate project development skills and various software engineering principles and methods
• Ability to use recent technologies for the software development
• To acquire research skills and able to publish research articles

• In continuation with Semester II Software Development Lab Phase – I, Students are asked to continue their Software Development / Research Project Development
• Students are expected to prepare and submit final report on the project in the IEEE format.

 Option – I : Software Development

1. Develop the Modules, Implementation, Testing - 15 Hours
2. Review of the Modules, Report Preparation - 15 Hours

 Option – II : Research Project

1. Implementation of Research Problem, Formulate Research Article - 15 Hours
2. Present Article in National / International Conference and Publish Article in UGC CARE / WoS/ Scopus / International Journals  - 15 Hours

50%      50%

This course will enable students to learn to setup Android Application development environment, create user friendly User Interfaces, handle multiple activity, persistent application development, handle data in cloud, test and deploy the App in the market

History of Mobile Apps, Trends in Market-Web App Vs Mobile App-Mobile OS.Introduction to  Android and Kotlin: Kotlin Basics – Classes and Objects- Inheritance- Functions – Extension Functions – First Android App – Anatomy of an Android App - Deploying the app: Running and Debugging app in Android Emulator.

Lab Exercises:

1. Form Creation

2. Activity and Layout demonstration

Layouts in Android ConstraintLayout - Displaying lists with RecyclerView Multiple activities and intents - App bar, navigation drawer, and menus Fragments - Navigation in an app - Navigation UI.

 Lab Exercises:

3. Intents

4. User navigation

Introduction to Activity-Activity Lifecycle – Logging. Fragment: Introduction - Lifecycle- Task and Back Stack. Android App Architecture - View Model -Data Binding – Live Data- Transform Live Data.

 Lab Exercises:

1. Activity Lifecycle

2. Fragment Lifecycle

Store Data-Room Persistency Library-Asynchronous program-Coroutines-Testing Databases. Introduction to Advanced Binding – Multiple Item View types-Headers -GridLayouts.

 Lab Exercises:

1. Sharedpreference

2. Recyclerview

Connect to the Internet-Android Permissions-connect to and from Network Resources – Connect to the Web Services-Display Images. Repository pattern – Work Manager – Work Input/Output – Work Request Constraints. App UI Design: Android Styling – Typography-Material Design- Material Components- Localization.

 Lab Exercises:

1. Work  Manager

2. Material Design

[2] Gardner, B., Sills, B., Stewart, C., Marsicano, K. Android Programming: The Big Nerd Ranch Guide. United Kingdom: Addison Wesley Professional, 4th edition,2022

[2]   Mark Wickham, Practical Android: 14 Complete Projects on Advanced Techniques and Approaches, APRESS.

Web Resources:

1. https://developer.android.com/
2. https://kotlinlang.org/education/

50%      50%

This course inculcates the theoretical and practical approaches which focus on advanced programming concepts in Python. This course explores data analysis, text analysis, gaming, and web development using python.

Making 3D visualizations: Creating 3D bars- Creating 3D histograms – Animating in Matplotlib – Plotting Charts with Images and Maps: Processing images with PIL – Plotting with Images – Plotting data on a map using Basemap

Lab Exercises:

1. Demonstrate Plots with Images and Maps

2. Apply 3D visualization concepts

Basic Application Structure: Initialization – Routes and View functions – Server Startup – The request – response cycle. Templates: The Jinja2 Template Engine – Links – Static Files. 

Web Forms: Form Classes – HTML rendering forms – Form Handling.

Lab Exercises:

3. Design a website using FLASK and perform CRUD operations

4. Demonstrate views and templates in FLASK

Image and its Properties-Image types – Data structures for Image analysis -  Filtering – Image Enhancement -Segmentation.

Lab Exercises:

5. Apply Image transformation and Manipulations

6. Use Image Enhancement techniques

Processing and understanding text: Text processing and wrangling – Text classifications: Automated Text classifications – Data retrieval – Classification models.

Lab Exercises

7.Find text similarity using Information Retrieval

8. Demonstrate the text analytics process in Social Media like Twitter / Facebook / Instagram

Introducing Pygame- Installing Pygame – Using Pygame – Understanding Events – Opening a display – Using the font module.

Lab Exercises:

9. Build a game using Cocos2D

10. Design a game object with different movements

[2]   Flask Web Development, Miguel Grinberg , O’Reilly Publications, First Edition, 2014          

[3]   Image Processing and Acquisition using Python, Ravishankar Chityala, ‎Sridevi Pudipeddi, CRC Press, Taylor & Francis Group, 2014

[4]   Text Analytics with Python, Dipanjan Sarkar, Apress publications, Second Edition, 2019

[5]   Beginning Game with Python and Pygame, Will McGugan, Apress publications, 2007

[2]   Python Web Development with Django, Jeff Forcier, Paul Bissex, Wesley Chun,

 Web Resources:

1.      Python Data Visualization Cookbook (blaqueyard.com)

2.      Flask Web Development (coddyschool.com)

ESE   50%

This course is designed to provide the knowledge of .NET Frameworks along with C# programming.

.NET Architecture – Common Language Runtime, MSIL, Support of different Languages. Language Interoperability, .NET Framework Classes. Advantages of Managed Code – Strong Data Type Check, Garbage Collection, Security, Performance Improvement. C# as a programming language, Features of C# – Data types, Flow Control – the Main method, Program Structure, Methods, Arrays, Namespaces

Lab Exercises:

1.     Program to find the largest element from an integer array in C#.

2.     Design a window based application with common controls (Text box, Label, button, List box, check box, radio button, combo box, Link label, groupbox, panel, rich textbox, form, picture box, message box)

3.     Program to implement date time picker, month calender and numeric updown.

Understanding Windows Forms Architecture, Windows controls: Common, controls, Containers, Menus and Tool strips, Dialog controls, Data, Reporting. Adding and using windows controls to the form, working of window-based application with database.

Lab Exercises:

1.     Design window-based application with containers, menu strip, status strip and tool strip.

2.     Program to design Dialog controls-font dialog, openfile dialog, save file dialog.

3.     Design a windows-based application to perform CRUD operation into database.

Windows Presentation Foundation Application Fundamentals, Navigation applications / XAML Browser Applications, Binding to a WPF element, Transformations- Render, Skew, Rotate.

Lab Exercises:

1. Program to implement Transformations Scale and Skew.
2. Program to implement Transformations Translate and Rotate

Introduction to Visual Studio .NET – ASP .NET. Difference between ASP and ASP.NET. Creating a Web application using ASP.NET. Components of an ASP.NET User Control, Custom Control, Deploying ASP .NET applications. Master Pages, Themes. Assemblies, Features of Assemblies, Application Domains, Assembly Structure, Assembly manifests, Assemblies and Components.

Lab Exercises:

1. Program to implement user control and custom control.
2. Design a web-based application to perform CRUD operation into database

ADO.NET overview. Various data access objects – Connection, Command and DataSet Objects. Binding data to ASP .NET server controls. Accessing data from a database using ADO.NET.

Lab Exercises:

1.     Program to implement various data access objects.

2.     Design a web based application to access data from database using ADO.net.

[2]   Mastering C# and .Net Framework , Marino Posadas, Packt Publishing 2016.

[3]   Asp.net MVC 1.0 website programming: problem - design – solution, Bernadi andnick,2009.

[4]   ASP.NET 4, Unleashed – Stephen Walther, Kevin Hoffman, Nate Dudek, Pearson,2016.

50%         50%

This course will enable students to Familiarize basic architecture of 8086 microprocessor and Programming 8086 Microprocessor using Assembly Level Language Use Macros and Procedures. The functionalities of stack and interrupts will be addressed including timing and delay.

Salient features of 8086 Microprocessor, architecture of 8086 (Block diagram, signal description), register

organization, concepts of pipelining.  memory segmentation and memory address generation from segment offset address.  Minimum and Maximum Mode operation and diagram

Lab Exercises:

1. Addition, subtraction, multiplication and division of 8 bit signed and unsigned numbers.

2.  Addition, subtraction, multiplication and division of 16 bit numbers.

3.  Addition, subtraction, multiplication and division of 32-bit numbers.

Assembly Language Programming Tools Editors -Assembler, Linker, Debugger.  Assembler directives, model of 8086 assembly language programming, programming using assembler.

 Lab Exercises:

 1.     ASCII/ BCD arithmetic and conversion of numbers.

2.     Find valid 2 out of 5 code of a given number.

3.      Copy/exchange block of data (Array of 8 bit, 16 bit) from one location to another with and without overlap.

Concept of Machine Language, Instruction format, addressing modes.  Instruction set (Arithmetic, logical, data transfer, bit manipulation, string, program control transfer, process control)

 Lab Exercises:

1. Addition, subtraction, multiplication and division of two array (8 bit, 16 bit) and store result in third array.

2. Find the Maximum/Minimum from given 8/16-bit given array.

3.  Arrange given array in ascending and descending order.

Introduction to stack, Stack structure of 8086, Programming for Stack. Interrupts and Interrupt Service routines, Interrupt cycle of 8086, NMI, INTR, Interrupt programming, Timing and Delays.

 Lab Exercises:

 1. Find from the  given array/ byte is palindrome or not.

2. (i) copy string to another location/compare two strings (ii) Reverse string (iii) check palindrome or not (iv) searching a word from given string (vi) Find a character and replace with another character from given string. 

3. Generate Fibonacci series of 8 bit numbers

Defining Procedure (Directives used, FAR and NEAR, CALL and RET instructions) - Defining Macros.-Assembly Language Programs using Procedure and Macros-DOS interrupt services.

Introduction to 8051 microcontrollers

 Lab Exercises:

 1.     Compute factorial of given number using near procedure and far procedure.

2.      Copy string to another location using MACRO.

3.       Boolean expression simplification.

2. Microprocessors and Interfacing By Douglas V Hall Revised Second Edition, McGraw Hill Publication , 2021

3. The 8088 and 8086 Microprocessors, Programming, Interfacing, Software, Hardware and Applications, Fourth Edition, By Walter A Triebel and Avtar Singh, Pearson Education,2002

Web Resources:

1. www.tutorialpoint.com

2. https://www.javatpoint.com/8086-microprocessor

3.https://www.youtube.com/watch?v=clOFKQKdI3k&list=PLc21Sqj4D8SSRpPFZLL6XvS7aGFs3HQ4H

50%       50%

Go (or Golang) is an open-source programming language designed to build fast, reliable, and efficient software at scale. Google uses Go specifically for its large networks of servers, and Go also powers much of Google’s own cloud platform. Developers use Go in application development, web development, in operations and infrastructure teams, and much more. It is the language of Cloud Native infrastructure and software development.

Why Go? Variables, values & type – introduction to packages, short declaration operator, var keyword, exploring type, zero values, fmt package, creating your own type, conversion, not casting. Control flow – Understanding control flow, loop, conditional.

Lab Exercises:

1. Implement the concept of Variables, values and type.

2. Implement the concept of control flow.

Array. Slice - composite literal, for range, slicing a slice, append to a slice, delete from a slice, make, multi-dimensional slice. Map - introduction, add element & range, delete. Struct – introduction, embedded structs, anonymous structs.

 Lab Exercises:

1. Implement the concept of Array and Slice.

2. Implement the concept of Map and Structs

Introduction, variadic parameter, unfurling a slice, Defer, Panic, Methods, Interfaces & polymorphism, Anonymous function, function expression, returning a function, callback, closure, recursion. Error handling – introduction, checking errors, Printing and logging, Recover, Errors with info.

Lab Exercises:

1. Implement the concept of functions and error handling

2. Implement the concept of interface

Pointer – introduction, use, method sets, Passing and Returning Pointers from Functions, Passing by Value vs. Passing by Pointer. Application – JSON marshal and unmarshal, bycrypt. Testing and Benchmarking – introduction, table test, golint, benchmark, coverage. 

 Lab Exercises:

1. Implement the concept of Pointers, call by value and call by function.

2. Implement the concept of JSON marshal and unmarshal. Write its unit test case.

Concurrency vs parallelism, Wait-group, race condition, mutex, atomic. Goroutines, and Channels – introduction, Directional channels, using channels, range, select.

Lab Exercises:

1. Implement the concept of Concurrency.

2. Implement the concept Goroutines and Channels

[2]   The Go Programming Language, Alan A. A. Donovan, Brian W. Kernighan, 2016, Pearson Education;

[3]   Go in Action, William Kennedy, Brian Ketelsen, Erik St. Martin Manning; 2015

[2]   Get Programming with Go, Nathan Youngman, Roger Peppé, Manning; 2018

[3]   Hands-on Go Programming,  Sachchidanand Singh, Prithvipal Singh, BPB Publications 2021

 Web Resources:

1.     https://go.dev/doc/

2.     https://developers.google.com/learn/topics/go

50%         50%

The main aim of this course is to understand and apply a wide range of classification, clustering, estimation and prediction algorithms on Textual data and to perform social network analysis. They can also design a new solution to opinion extraction, sentiment classification and data summarization problems. 

Introduction to Text Mining - Text Representation- tokenization, stemming, stop words, TF-IDF, Feature Vector Representation, NER,N-gram modeling.

Text Clustering, Text Classification, Topic Modeling-LDA, HDP

Introduction To Web-Mining - inverted indices and boolean queries, plsi, query Optimization, page ranking.Web usage web content mining - essentials of social graphs, social networks, models, Information diffusion in social media.

Essentials of Social graphs, Social Networks, Models, Information Diffusion in social media. Analyzing social media:

Behavioral Analytics, Influence and Homophily, Recommendation in social media

Sentiment Classification, feature based opinion mining, comparative sentence and relational mining, Opinion spam

Recent Trends in Text, Web and Social Media Analytics

[2].RezaZafarani,MohammadAliAbbasiandHuanLiu,“SocialMediaMining-AnIntroduction”, Cambridge University Press, 2014.

[3].Bing Liu, “Sentiment Analysis and Opinion Mining”, Morgan & Claypool Publishers,2012.

[2].Matthew A. Russell, “Mining the social web”, 2nd edition- O'Reilly Media, 2013

50%      50%

To provide a foundational knowledge of data engineering and knowledge representation. To store, retrieve, analyze and design data for various applications. To represent different sorts of knowledge, such as uncertain or incomplete knowledge.

Data Engineering

Introduction to Data Engineering - Data Engineering versus Data Science – Data Engineering tools– Data Engineering Lifecycle.

Data Models

Data Systems – Reliability – Scalability – Maintainability -Data Models and Query Languages. - Relational Model Versus Document Model - Query Languages for Data -Query Languages for Data,Declarative Queries on the Web ,MapReduce Querying ,Graph-Like Data Models Property Graphs ,The Cypher Query Language ,Graph Queries in SQL ,Triple-Stores and SPARQL.

Introduction – Data Engineering ecosystem - Building data pipelines—Extract, Transform, Load -ETL Process – Data Structures related to  Database – Other data integration methods – Benefits and Challenges of ETL – ETL tools.

Data Warehousing - Stars and Snowflakes: Schemas for Analytics- Column-Oriented Storage - Column Compression -Sort Order in Column Storage - Writing to Column-Oriented Storage.

Data Storage and Retrieval Non Relational data

Non Relational data – NoSQL- Language-Specific Formats JSON, XML, and Binary Variants  - Modes of Dataflow Dataflow Through Databases.

DATA in Distributed systems

Data in distributed systems – Partitioning and Replication - Partitioning of Key-Value Data - Partitioning and Secondary Trouble with Distributed Systems- Faults and Partial Failures - Unreliable Networks - Unreliable Clocks.

Knowledge Representation - Ontological Engineering - Categories and Objects . Events - Mental Events and Mental Objects - Reasoning Systems for Categories -  Reasoning with Default Information Uncertain knowledge and reasoning- Quantifying Uncertainty - Acting under Uncertainty - Basic Probability Notation.

Probabilistic Reasoning-Representing Knowledge in an Uncertain Domain -The Semantics of Bayesian Networks -Efficient Representation of Conditional Distributions -Exact Inference in Bayesian Networks -Relational and First-Order Probability Models.

[2]  S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Pearson Education, 2019.

[2]  Paul Crickard, Data Engineering with Python,  first edition, Packt Publishing,2020

[3]  Ronald J. Brachman, Hector J. Levesque, KNOWLEDGE REPRESENTATION AND REASONING, Elsevier , 2004

[4]  S.L. Kendal and M. Creen An Introduction to Knowledge Engineering, Springer, 2007

It is intended to impart skills essential for the design and implementation of Embedded system and interfacing using appropriate hardware and software tools. To understand fundamentals of IoT and embedded system including essence, basic design strategy and process modeling.    The course eases hands-on experiences on sensor interfacing, blue tooth interfacing, LCD and motor interfacing.

Introduction: Embedded Systems and general-purpose computer systems, history, classifications, applications and purpose of embedded systems
Core of the embedded system, Memory, Sensors (resistive, optical, position, thermal) and Actuators (solenoid valves, relay/switch, opto-couplers), Communication Interface, Embedded firmware (RTOS, Drivers, Application programs), Power-supply (Battery technology, Solar), PCB and Passive components, Safety and reliability, environmental issues. Ethical practice.

Device and Communication Bus for Devices Network IO Types and examples, Serial communication devices, Parallel Device ports, Sophisticated Interfacing Feature in Devices Ports, Wireless Devices, Real time clock, Network Embedded Systems  interrupt sources, Programmed-I/O busy-wait approach without interrupt service mechanism- context and periods for context switching -Introduction to Basic Concept Device Drivers.

Custom single purpose processors: Hardware – Combination Sequence – Processor design – RT level design – optimizing software: Basic Architecture – Operation – Programmer’s view – Development Environment – ASIP – Processor Design –Peripherals – Timers and counters – UART – Pulse width modulator – LCD controllers – Key pad controllers– A/D converters – Real time clock.

Introduction to basic concepts of RTOS- Task, process & threads, interrupt routines in RTOS, Multiprocessing and Multitasking, Preemptive and non-preemptive scheduling, Task communication shared memory, message passing-, Interprocess Communication – synchronization between processes-semaphores, Mailbox, pipes, priority inversion, priority inheritance-comparison of commercial RTOS features.

Objectives, different Phases & Modeling of the Embedded product Development Life Cycle (EDLC)-Product specification – Hardware / Software partitioning – Detailed hardware and software design – Integration – Product testing – Selection Processes – Microprocessor Vs Micro Controller – Performance tools – Bench marking – RTOS Micro Controller – Performance tools – Bench marking

WEB ARCHITECTURAL FRAMEWORK FOR EMBEDDED SYSTEM        Embedded as Web Client - Embedded Web servers

Self-study: Case studies on Smart card- Adaptive Cruise control in a Car -Mobile Phone software for key inputs.

[2] Wayne Wolf, Computer as Components: Principles of Embedded Computing System Design, Morgan Kaufmann Publication, 2nd edition, 2008.

[3] Rajkamal, Embedded System: Architecture, Programming and Design, Tata McGraw Hill, 2nd edition, 2010.

[4] Sriram Iyer, Embedded Real time System Programming.

[2] Lyla B Das, Embedded Systems-An Integrated Approach, Pearson, 2013.

[3] Elicia White, Making Embedded Systems, O’Reilly Series, SPD, 2011.

[4] Bruce Powel Douglass, Real-Time UML Workshop for Embedded Systems, Elsevier, 2011.

[5] Simon Monk, Make: Action, Movement, Light and Sound with Arduino and Raspberry Pi, O’Reilly Series , SPD, 2016.

[6] Tammy Noergaard,  Embedded System Architecture, A comprehensive Guide for Engineers and Programmers, Elsevier, 2006.

[7] Jonathan W.Valvano, Embedded  Microcomputer Systems: Real Time Interfacing, Cengage Learning, 3rd edition, 2012.

[8] Michael Margolis, Arduino Cookbook, O’Reilly Series, SPD, 2013.

Web Resources:

[1] http://www.ti.com/ww/en/launchpad/launchpads-msp430-msp-exp430g2.html#project0

[2] http://coder-tronics.com/msp430-programming-tutorial-pt1/

[3] http://coder-tronics.com/msp430-programming-tutorial-pt2/

This course will enable students to learn to setup Android Application development environment, create user friendly User Interfaces, handle multiple activity, persistent application development, handle data in cloud, test and deploy the App in the market.

History of Mobile Apps, Trends in Market-Web App Vs Mobile App-Mobile OS-What is Android?-Why Develop apps for Android?-Most popular platform for mobile apps-best experience for app users Android version-the challenges of Android app development-Features of Android-Android Software Stack- Android System Architecture-Android Core building blocks- Introduction to Android Studio-Building first Android App-Layouts and resources for UI-Text and Scrolling views.   

LAB EXERCISES:

1. Installation of Android Studio and Hello World

2. Layout Editors

3. Input Controls.

Introduction to Activity and Intent-Activity life cycle and state-Implicit and Explicit Intents-The Android Studio debugger-App testing and Android support library-Understanding the views-components-understanding screen- screen orientation-Button-clickable images-Input controls-Menus and pickers- user navigation-RecyclerView-Drawables-styles and themes-material design resources for adoptive layouts and UI Testing.

LAB EXERCISES:

4. Activity and Intents- Implicit and Explicit and camera

5. Input controls

6. Menu and pickers

Background Tasks-AsyncTask and AsyncTaskloader, Internet Connection-Broadcast receiver- Services-Alarms and Schedulers -Notifications-Alarms- Delightful user experience.

LAB EXERCISES:

7. User navigation – Recyclerview

8. MediaController

9. Fragments

Preference and settings, Storage types, Data Storage, shared preference, App settings, SQLite Primer, Room, LiveData and ViewModel- introduction to Firebase – Firebase data handling CRUD operation.

LAB EXERCISES:

10. AsyncTask and AsyncTaskloader

11. Notifications

12. BroadcastReceiver

Fragments- Fragment lifecycle and communication- sensor basics-Introduction to API usage- using maps in your apps-Animation – Media Playback- video view. Phone calls – SMS Messages-Material design-design concepts-usage-user experience handling- deployment of App in Play store- security aspects of APP -Introduction to Kotlin, concepts of framework and Flutter.

LAB EXERCISES:

13. Sharedpreference

14. SQLite /Firebase

15. APK Deployment

[2] Bill Philips, Chris Stewart, Kristin Masrsicano, Android Programming: The Big Nerd ranch Guide, 4th edition, 2019.

[2] Mark Wickham, Practical Android: 14 Complete Projects on Advanced Techniques and Approaches, APRESS.

 Web Resources:

[1] https://developer.android.com/

[2] https://www.tutorialspoint.com/android/index.htm

[3] https://www.youtube.com/channel/UCVHFbqXqoYvEWM1Ddxl0QDg

The objective of this course is to provide introduction to the principles and design of machine learning algorithms. The course is aimed at providing foundations for conceptual aspects of machine learning algorithms along with their applications to solve real world problems. 

Data Mining – Kinds of data to be mined – Kinds of patterns to be mined – Technologies – Targeted Applications - Major Issues in Data Mining – Data Objects and Attribute Types – Measuring Data similarity and dissimilarity - Data Cleaning –Data Integration - Data Reduction – Data Transformation – Data Discretization

Machine Learning - Examples of Machine Learning Applications - Learning Associations - Classification -Regression -Unsupervised Learning - Reinforcement Learning.

 Lab Exercises:

1. Data cleaning and Exploration

2. Dimensionality Reduction using PCA

Supervised Learning: Learning class from examples - Noise - Learning Multiple classes. Regression-Model Selection and Generalization. Introduction to Parametric methods - Maximum Likelihood Estimation: Bernoulli Density -Multinomial Density - Gaussian Density, Nonparametric Density Estimation: Histogram Estimator - Kernel Estimator - K-Nearest Neighbour Estimator. Decision Tree – Introduction, Univariate Tree, tree Pruning, Rule Extraction from tree.

Linear Discrimination: Introduction - Generalizing the Linear Model-Geometry of the Linear Discriminant - Pairwise Separation - Gradient Descent - Logistic Discrimination.

 Lab Exercises:

1. Classification using Decision tree

2. Linear / Logistic Discrimination

Clustering - Introduction - Mixture Densities, K-Means Clustering - Mixtures of Latent Variable Models - Supervised Learning after Clustering - Spectral Clustering - Hierarchical Clustering - Clustering - Choosing the number of Clusters.

 Lab Exercises:

1.    K Means Clustering

2.    Hierarchical Clustering

Kernel Machines - Introduction - optical separating hyperplane kernel tricks - Vectorial Kernels

Multi-Layer Perceptron Introduction, training a perceptron - learning Boolean functions - multilayer perceptron – back propagation algorithm - training procedures.

LAB EXERCISES:

7. Classification using Kernel Machines

8. Classification using MLP

Introduction, Single state case, elements of reinforcement learning, Temporal difference learning, Generalization, partially observed state.

LAB EXERCISES:

9. Temporal reinforcement Learning

[2] E. Alpaydin, Introduction to Machine Learning, 3rd Edition, MIT Press, 2014.

[2] T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction, Springer, 2nd Edition, 2009.

[3] K.P.Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.

 Web Resources:

[1] https://data-flair.training/blogs/data-mining-tutorial/

[2] https://machinelearningmastery.com/

[3] https://towardsdatascience.com/

[4] https://scikit-learn.org/stable/

The student can understand the Big Data Platform and its Use cases and get an overview of Apache Hadoop. The course will provide HDFS Concepts and Interfacing with HDFS and the student can understand Map Reduce Jobs. It is to give hands on experience on Apache Hadoop architecture, ecosystem, and practices, and use related applications including HDFS, HBase, Spark, and MapReduce with Hive and Pig.

Big Data Overview: Data Structures - Analyst Perspective on Data Repositories - State of the Practice in Analytics - Current Analytical Architecture - Drivers of Big Data - Emerging Big Data Ecosystem and a New Approach to Analytics - Key Roles for the New Big Data Ecosystem - Examples of Big Data Analytics.

 LAB EXERCISES:

1. Installing and Configuring Hadoop

2. Installing and Configuring HIVE

Manipulation - NoSQL Data Architecture Patterns - Key-Value Store - Document Store - Tabular Data - Object Data Store - Graph Database - Variations of NoSQL Architectural Patterns - NoSQL to Manage Big Data - Shared-Nothing Architecture for Big Data Tasks - Choosing the Distribution Models - Ways of Handling Big Data Problems

LAB EXERCISES:

3.Demonstrate the CURD OPERATIONS AND AGGREGATIONS in NOSQL

4. Demonstrate the ARRAY MODIFIERS in NOSQL

Introduction to Hadoop and MapReduce Programming Hadoop Overview, HDFS (Hadoop Distributed File System), Processing– Data with Hadoop, Managing Resources and Applications with Hadoop YARN (Yet another Resource Negotiator). Introduction to MAPREDUCE Programming: Introduction, Mapper, Reducer, Combiner, Partitioner, Searching, Sorting, Compression.Key/value pairs, The Hadoop Java API for MapReduce, Writing MapReduce programs, Hadoop-specific data types, Input/output

 Lab Exercise

5. Finding max and min value in Hadoop.

6. Word count application in Hadoop

Introduction to Hive - Hive Architecture - Characteristics - Comparison with RDBMS (Traditional Database) – HIVE modes – HIVE Server2(HS2) - Hive Data Types and File Formats - Hive Data Model - Hive Integration and Workflow Steps -Hive Built-in Functions - HiveQL - HiveQL. Data Definition Language (DDL) - HiveQL. Data Manipulation Language (DML) - HiveQL for Querying the Data - Aggregation - Join - Group by Clause –PIG DATA TYPES, Built -in functions used with LOAD and STORE operators – PIG Evaluation functions.

LAB EXERCISES:

7. Demonstrate the HIVE QL for TABLE CREATION & MANIPULATION WITH TABLES

8. Demonstrate the use of HIVE QL for the the given dataset and queries

Introduction - Spark - Introduction to Big Data Tool-Spark - Introduction to Data Analysis with Spark - Spark SQL - Using Python Advanced Features with Spark SQL - Data Analysis Operations - Downloading Spark, and Programming using RDDs and MLIB

 LAB EXERCISES:

9.Configure Spark and implement collaborative filtering recommendation using spark.

10.Demonstrate use of Spark Machine learning library.

11. Different Data frame, Dataset and RDD operations

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

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

[2] Jonathan R. Owens, Jon Lentz and Brian Femiano, Hadoop Real-World Solutions Cookbook, Packt Publishing, 2013.

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

Web Resources:

[1] https://www.tutorialspoint.com/hadoop/index.htm

[2] https://www.javatpoint.com/hadoop-tutorial

This course is to make students familiar with the concepts of the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning concepts.

Introduction to NLP- Background and overview- NLP Applications -NLP hard Ambiguity- Algorithms and models, Knowledge Bottlenecks in NLP-Introduction to NLTK, Case study.

LAB EXERCISES:

1. (a) Import NLTK and download the data

   (b) Import and display one of the corpus

   (c) Import and display words from the corpus

   (d) Perform “Searching Text” from a corpus and display the results

   (e) Count and display how often a word occurs in a text and plot the same

2. Write a program to count word frequency and to remove stopwords

Word Level Analysis: Regular Expressions, Text Normalization, Edit Distance, Parsing and Syntax-Spelling, Error Detection and correction-Words and Word Classes-Part-of speech Tagging, Naive Bayes and Sentiment Classification: Case study.

LAB EXERCISES:

 3. Write a program to tokenize English and Non-English Languages

 4. Write a program to get synonyms and  Antonyms from WordNet

N-gram Language Models: N-Grams, Evaluating Language Models-The language modelling problem

SEMANTIC ANALYSIS AND DISCOURSE PROCESSING

Semantic Analysis: Meaning Representation-Lexical Semantics- Ambiguity-Word Sense Disambiguation. Discourse Processing: cohesion-Reference Resolution- Discourse Coherence and Structure.

LAB EXERCISES:

 5. Write a program for stemming Non-English words

 6. Write a program for lemmatizing words using WordNet

Architecture of NLG Systems, Applications

Machine Translation: Problems in Machine Translation- Machine Translation Approaches-Evaluation of Machine Translation systems. Case study: Characteristics of Indian Languages.

LAB EXERCISES:

 7. Write a program to differentiate stemming and lemmatizing words

 8. Write a program for POS Tagging

Information Retrieval: Design features of Information Retrieval Systems-Classical, Non- classical, Alternative Models of Information Retrieval. Lexical Resources: Word Embeddings - Word2vec- Glove.

Language models for information retrieval, Language modeling versus other approaches in IR.

UNSUPERVISED METHODS IN NLP: Graphical Models for Sequence Labelling in NLP.

LAB EXERCISES:

 9. Write a program for Word Embeddings.

 10. Case study-based program (IBM) or Sentiment analysis.

[2] Foundations of Statistical Natural Language Processing. Cambridge, MA: MIT Press, 1999.

[3] Introduction to Information Retrieval, Cambridge University Press. 2012.

[2] Steven Bird, Ewan Klein and Edward Loper Natural Language Processing with Python, O’Reilly Media; 1st edition, 2009.

 Web Resources:

[1] https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf

[2] https://nptel.ac.in/courses/106101007/

[3] NLTK – Natural Language Tool Kit-http://www.nltk.org

To enable students to learn the principles of IoT components, IoT programming, sensors and actuators, and modeling approaches to design and develop real-world applications on the Arduino and Raspberry Pi platforms.

Design principles of IoT - IoT architecture & components - OSI model for IoT Protocols - Organizational levels. Sensors: Sensors classification - Working principle of sensors - Criteria to choose a sensor - Generation of sensors. IoT Design Methodology: Design methodology - Challenges in IoT design - IoT system management - IoT servers.

LAB EXERCISES:

1. Interfacing Arduino boards and importing essential libraries with Arduino IDE

2. Realtime data acquisition and plotting with Arduino