Syllabus for
Master of Computer Applications
Academic Year  (2023)

Department of Computer Science of CHRIST (Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation’s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field. 

VISION

The Department of Computational Sciences endeavours to imbibe the vision of the University “Excellence and Service”. The department is committed to this philosophy which pervades every aspect and functioning of the department.

MISSION

“To develop a computational scientist with ethical and human values”. To accomplish our mission, the department encourages students to apply their acquired knowledge and skills towards professional achievements in their career. The department also moulds the students to be socially responsible and ethically sound.

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 Objective

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: Project management and finance: 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.

The Department of Computational Sciences at CHRIST (Deemed to be University) Delhi- NCR has created a niche in the realm of higher education in India through its programmes. Currently, the Department offers a wide array of undergraduate courses with multiple specializations in the disciplines of Computer Science, Statistics & Mathematics. A dedicated research block with all the latest research facilities boosts the morale of the faculty and research scholars alike. This is an ideal place for students with a research blend of mind to explore his/her passion. Apart from academics, students are moulded holistically through various co-curricular and extracurricular activities.

To promote the holistic development of the students and to sustain the academic creativity and inventiveness of the faculty the department engages in numerous workshops, seminars, industrial interfaces, faculty development programmes and many such endeavours. It is equipped with a highly committed team of instructors having versatile experience in teaching and research. The department also provides opportunities to work on collaborative projects with industry and international universities.

Course Objectives

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-Subgroups-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.

1. John C Martin, "Introduction to Languages and the Theory of Computation", Tata McGraw Hill, 2015.

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

1. Y.N Singh, “Mathematical Foundation of computer science”, New Age International Publishers, New Delhi,2005

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

ESES-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

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.

ESE 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.

ESE 50%

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

ESE 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.

ESE 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.

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.

OVERVIEW OF WEB TECHNOLOGIES AND HTML5

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 AND AJAX

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

CLIENT-SIDE SCRIPTING

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

React JS

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 ReactJs.

Node JS and MYSQL

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 Mysql with Node.JS.

[1] HTML 5 Black Book (Covers CSS3, JavaScript, XML, XHTML, AJAX, PHP, jQuery), DT Editorial Services, Dreamtech Press, 2nd Edition, 2016.

[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.

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

ESE 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

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.

3. Calculate descriptive statistics

4. 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

5. Build simple linear regression model

6. Perform a one-way analysis of variance

7. 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

8. Demonstrate conditional probability

9. Demonstrate Bayes' rule

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

Healthcare - Finance - Digital Marketing- Environment-Sports

[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

5. https://www.isibang.ac.in/~athreya/psweur/#about

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

ESE 50%

1. 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.

2. Ability to translate end-user requirements into system and software requirements

3. Able to identify and formulate research problem, conduct critical research review based on the domain

Description

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.

1. Domain Identification, Problem Identification, Requirement Analysis for the specific Problem - 15 Hours

2. Preparation of SRS Document, DFD, Design the Modules - 15 Hours

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

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

3. Application of Stack (convert an infix expression to the postfix form)

4. 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

5. Implementation of Linear and Binary Search

6. 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

7. Implementation of Tree Traversal

8. 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:

9. Implementation of Graph Traversal

10. 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.

Web Resources:

[1] https://www.programiz.com/c-programming

[2] https://www.hackerrank.com/domains/data-structures

ESE 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.

2. https://www.geeksforgeeks.org/java/

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.

1. Stallings William, “Data and Computer Communications”, PHI, 9th Edition, 2011.

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

50% ESE

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

Basic Concepts – Decision Tree Induction – Bayes Classification Methods – Rule-Based Classification – Model Evaluation and Selection – Techniques to Improve Classification Accuracy – Classification by Backpropagation – Support Vector Machines – Learning from Your 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.

1. Data Mining Concept and Techniques, Jiawei Han, Micheline Kamber, Jian Pie, Morgan and Kaufmann Publisher, Third Edition,2012

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

1. Data Mining and Predictive Analytics Daniel T. Larose, Chantal D. Larose (Wiley Series on Methods and Applications in Data Mining), Wiley Publications.

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

50% ESE

The course covers a wide range of topics, including financial reporting, financial analysis, budgeting, internal control, and financial decision-making. The course begins by introducing students to the fundamental principles of accounting and the preparation and interpretation of financial statements.

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

https://in.coursera.org/learn/financial-accounting-polimi 

https://www.edx.org/course/accounting-and-finance 

1. To inculcate project development skills and various software engineering principles and methods 

2. Ability to use recent technologies for the software development

3. To acquire research skills and able to publish research articles

1. Develop the Modules, Implementation, Testing - 15 Hours

2. Review of the Modules, Report Preparation - 15 Hours

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

• 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.

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

1. Dawn Griffiths and David Griffiths, Head First Android Development: A Brain-Friendly guide, O’Reilly, 2nd edition, 2019. 

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

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

1. Python Data Visualization Cook Book, Igor Mialovanovic, PACKT publications, First Edition, 2013

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

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.

1. Martin Kleppmann, Designing Data-Intensive Applications - The Big Ideas Behind Reliable, Scalable,and Maintainable Systems,  first edition, O’Reilly ,2017

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

1. Ted Malaska , Rebuilding Reliable Data Pipelines Through Modern Tools , first edition,  O’Reilly, 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

ESE 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-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

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.

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

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.

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

1. Installation of Android Studio and Hello World

2. Layout Editors

3. Input Controls.

4. Activity and Intents- Implicit and Explicit and camera

5. Input controls

6. Menu and pickers

7. User navigation – Recyclerview

8. MediaController

9. Fragments

10. AsyncTask and AsyncTaskloader

11. Notifications

12. BroadcastReceiver

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.

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.

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

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.

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

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.

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.

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

1. Data Exploration using Parametric Methods and Non-Parametric Methods

2. Dimensionality Reduction using PCA

3. K Means Clustering

4. Hierarchical Clustering

5. Classification using Decision tree

6. Logistic Discrimination

7. Classification using Kernel Machines

8. Classification using MLP

9. Temporal reinforcement Learning

[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.

ESE 50%

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.

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

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

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

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

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.

Graphical Models for Sequence Labelling in NLP

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

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

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

5. Write a program for stemming Non-English words

6. Write a program for lemmatizing words using WordNet

7. Write a program to differentiate stemming and lemmatizing words

8. Write a program for POS Tagging.

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

The course is designed to enhance the soft skills and technical understanding of the students.

Students takesseminar in advanced concepts in computer science.

This course gives an overview of the field of Cloud computing and an in-depth study into its enabling technologies and main building blocks. Students will gain hands-on experience solving relevant problems through projects that will utilize existing public cloud tools. The students will develop the skills needed to become a practitioner or carry out projects in this domain.

Definition of Cloud Computing - Characteristics of Cloud Computing - Cloud Models - Service Models - Deployment Models - Cloud Services Examples - IaaS: Amazon EC2, Google Compute Engine, Azure VMs - PaaS: Google App Engine - SaaS: Salesforce - Cloud-based Services & Applications.

Virtualization - Load Balancing - Scalability & Elasticity  Deployment –Replication – Monitoring - Software Defined Networks - Service Level Agreements – Security - Billing.

Identity and Access Management Services - User, Groups, Roles - Compute Services - Amazon Elastic Compute Cloud - Google Compute Engine - Windows Azure Virtual Machines - Storage Services - Amazon Simple Storage Service - Google Cloud Storage - Windows Azure Storage

Amazon Relational Data Store - Amazon DynamoDB -Google Cloud SQL - Google Cloud Datastore - Windows Azure SQL Database - Amazon Virtual Private Network - Windows Azure

Table Service. Application Services - Content Delivery Services - Amazon CloudFront - Windows Azure Content Delivery Network

PaaS - Google AppEngine - Amazon Elastic Beanstalk - SaaS - Salesfore - Open source Private Cloud Softwares - Openstack - CloudStack - Eucalyptus – OwnCloud

[2] Google Cloud Platform Associated Qwiklabs, 2020.

[3] Arshdeep Bahga and Vijay Madisetti, Cloud computing - A Hands-On Approach, CreateSpace Independent Publishing Platform, Reprint 2018.

[2] Zaigham Mahmood, Ricardo Puttini and Thomas Erl, Cloud Computing: Concepts, Technology & Architecture, Pearson Publications, 2013.

Web Resources:

[1] https://www.w3schools.in/cloud-computing/cloud-computing/

[2] https://docs.aws.amazon.com

[3] https://cloud.google.com › docs

To work on a project were student are specialized in.

NA

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

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