Syllabus for
Bachelor of Technology (Computer Science and Engineering)
Academic Year  (2023)

The fundamental objective of the Department of Computer Science and Engineering of the CHRIST(Deemed to be University)is to develop a firm foundation in mathematics, science, and design methodology applied to the disciplines of Computer Science and Engineering. The various courses offered gives the fundamentals, working and expert subjects that provides enough learning environment where students understand and are able to apply the most contemporary and essential tools needed in the breadth and depth of Computer Science and Engineering. The Department strives to give skills essential to practicing engineering professionals; it is also an objective to provide experience in leadership, management, planning, and organization. The department understands its role in developing and evaluating methods that encourage students to continue to learn after leaving the institution. We believe that the student opportunities and experiences should lead to an appreciation of the holistic development of individual. We also try to pass to our students our passion for what we do, and to have the students comprehend that we also desire to continue to learn.

Programme Outcome/Programme Learning Goals/Programme Learning Outcome:

PO2: Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences

PO3: Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations

PO4: Conduct investigations of complex 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, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.

PO6: The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.

PO7: Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.

PO8: Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.

PO9: Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.

PO10: Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.

PO11: Project management and finance: Demonstrate knowledge and understanding of the engineering 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.

PO12: Lifelong learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

Programme Specific Outcome:

PSO2: Resource Management : Utilize Resource Management ideas to efficiently develop and deploy projects.

Continuos Internal Assesments CIA-I, CIA-II, CIA-III.

End Semester Examination-

To learn the fundamentals of data models and to conceptualize and depict a database system using ER diagram. To make a study of SQL and relational database design. To understand the internal storage structures using different file and indexing techniques which will help in physical DB design. To know the fundamental concepts of transaction processing- concurrency control techniques and recovery procedure. To have an introductory knowledge about the emerging trends in the area of distributed DBOO DB- Data mining and Data Warehousing and XML. To implement the design of the tables in DBMS. To write queries to get optimized outputs. To store, retrieve and view the contents. To generate report based on customized need

Introduction to File and Database systems- Database system structure – Data Models – Introduction to Network and Hierarchical Models – ER model – Relational Model – Relational Algebra.

SQL – Data definition- Queries in SQL- Updates- Views – Integrity and Security – Relational Database design – Functional dependencies and Normalization for Relational Databases (up to BCNF).

Introduction to NOSQL Systems ,The CAP Theorem, Document-Based NOSQL Systems and MongoDB, NOSQL Key-Value Stores, Column-Based or Wide Column NOSQL Systems, NOSQL Graph Databases and Neo4j 

Record storage and Primary file organization- Secondary storage Devices- Operations on FilesHeap File- Sorted Files- Hashing Techniques – Index Structure for files –Different types of Indexes- B-Tree - B+ Tree – Query Processing.

Transaction management -Transaction Processing – Introduction- Need for Concurrency control- Desirable properties of Transaction- Schedule and Recoverability- Serializability and Schedules – Concurrency Control – Types of Locks- Two Phases locking- Deadlock- Time stamp based concurrency control – Recovery Techniques – Concepts- Immediate Update- Deferred Update - Shadow Paging.

Raghu Ramakrishnan, “Database Management System”, Tata McGraw-Hill Publishing Company, 2003

Online Resources: W1.http://db-book.com/db6/slide-dir

ESE:30/100

To understand the basic concept of data structures for storage and retrieval of ordered or unordered data. Data structures include: arrays, linked lists, binary trees, heaps, and hash tables.

Definition- Classification of data structures: primitive and non-primitive- Operations on data structures- Algorithm Analysis: Introduction. The Stack ADT: Definition, Array representation of stack, Operations on stack: Infix, prefix and postfix notations Conversion of an arithmetic Expression from Infix to postfix. Applications of stacks.

The Queue ADT: Definition, Array representation of queue, Types of queues: Simple queue, circular queue, double ended queue (de-queue) priority queue, operations on all types of Queues. The List ADT: singly linked list implementation, insertion, deletion and searching operations on linear list, circular linked list implementation, Double linked list implementation, insertion, deletion and searching operations. Applications of linked lists.

Preliminaries – Binary Trees – The Search Tree ADT – Binary Search Trees – AVL Trees – Tree Traversals – Hashing – General Idea – Hash Function – Separate Chaining – Open Addressing –Linear Probing – Priority Queues (Heaps) – Model – Simple implementations – Binary Heap

Preliminaries – Insertion Sort, Selection sort – Shell sort – Heap sort – Merge sort – Quicksort – External Sorting

Introduction to Graphs, Definitions –DFS, BFS,  Minimum Spanning Tree – Prim’s and Kruskal's Algorithm.  Single-Source Shortest Paths – Bellman-Ford algorithm and Dijkstra’s Algorithm – Applications of Graphs

R2. Classic Data Structures , Debasis Samanta ,2nd Edition, PHI Learning PVT,2011

ESE-30/100

Course Description: Software engineering course provides: Different life cycle models, Requirement dictation process, Analysis modelling and specification, Architectural and detailed design methods, Implementation and testing strategies, Verification and validation techniques, Project planning and management and Use of CASE tools.

Course objectives:  To be aware of Different life cycle models; Requirement dictation process; Analysis modeling and specification; Architectural and detailed design methods; Implementation and testing strategies; Verification and validation techniques; Project planning and management and Use of CASE tools.

Introduction –S/W Engineering Paradigm – life cycle models (water fall, incremental, spiral, WINWIN spiral, evolutionary, prototyping, object oriented) - system engineering – computer based system – verification – validation – life cycle process – development process –system engineering hierarchy.

Functional and non-functional - user – System –requirement engineering process – feasibility studies – requirements – elicitation – validation and management – software prototyping – prototyping in the software process – rapid prototyping techniques – user interface prototyping -S/W document. Agile methods, Extreme Programming, SCRUM.

Design process and concepts – Modular design – design heuristic – design model and document. Architectural design – software architecture – data design – architectural design – transform and transaction mapping – user interface design – user interface design principles-Model representation- Real time systems - Real time software design – system design – real time executives – data acquisition system - monitoring and control system. SCM – Need for SCM – Version control – Introduction to SCM process – Software configuration items.

Taxonomy of software testing – levels – test activities – types of s/w test – black box testing – testing boundary conditions – structural testing – test coverage criteria based on data flow mechanisms – regression testing – testing in the large. S/W testing strategies – strategic approach and issues - unit testing – integration testing – validation testing – system testing and debugging.

Measures and measurements – S/W complexity and science measure – size measure – data and logic structure measure – information flow measure. Software cost estimation – function point models – COCOMO model- Delphi method.- Defining a Task Network – Scheduling – Earned Value Analysis – Error Tracking - Software changes – program evolution dynamics – software maintenance – Architectural evolution. Taxonomy of CASE tools – Agile based Case Study.

R2. Ian Sommerville, “Software engineering,” Pearson education Asia, 9th Edition, 2013.

R3. Pankaj Jalote- “An Integrated Approach to Software Engineering,” Narosa Publishing house, 2011.

R4.James F Peters and Witold Pedryez, “Software Engineering – An Engineering Approach”, John Wiley and Sons, New Delhi, 2010. 

 R5.Ali Behforooz and Frederick J Hudson, “Software Engineering Fundamentals”, OUP India 2012.

End Semester Exams ESE 50

Total 100

●      Discuss the core concepts Statistical Analytics and Data manipulation

●      Apply the basic principles, models, and algorithms supervised and unsupervised learning mechanisms.

●      Analyse the structures and algorithms of regression methods

●      Analyse the use of SVM in Data Science

Explain notions and theories associated to Convolutional Neural Networks

Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.

The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis.

Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, 0 Computational Considerations.

Introduction, The Support Vector Classifier, Support Vector Machines and Kernels, Generalizing Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis

Definition of Random Forests, Details of Random Forests- Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.

T2. Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.

R2. Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018.

R3. Wu, James, and Stephen Coggeshall. Foundations of predictive analytics. Chapman and Hall/CRC, 2012.

R4. Marcoulides, George A., and Scott L. Hershberger. Multivariate statistical methods: A first course. Psychology Press, 2014.

R5. Morgan, George A., et al. IBM SPSS for introductory statistics: Use and interpretation. Routledge, 2012

ESE 30 marks

After learning the course for a semester, the student will be aware of the important statistical information for addressing cryptography, error correction and coding, information theory and cryptanalysis. The student would also get a clear idea on some of the cases with their analytical studies in information coding and its related fields.

Probability Fundamentals, Bayes’ rule, Markov chains and application to pattern search algorithms, Classical statistical inference, Bayesian statistical inference, Regression techniques

Information coding, Pseudorandom number generators, discrete random variables, special distributions and mixed random variables, link and rank analysis , probability bounds, limiting theorem and convergence 

Risk M Basics of statistical learning: models, regression, curse of dimensionality, overfitting, etc. Optimization and convexity, Gradient descent, Newton’s method 

Classification and similarity analysis, linear discriminative analysis, regression analysis, iterative permutation analysis, Support vector machines, nearest neighbor and application of entropy

Clustering algorithms, graph analysis, pattern detection, Knowledge driven system design, learning with errors, Basics of neural networks

T2. Beichelt, Frank. Applied Probability and Stochastic Processes. Chapman and Hall/CRC, 2016.

T3. Li, X. Rong. Probability, random signals, and statistics. CRC press, 2017

R2. Papoulis, Athanasios, and S. Unnikrishna Pillai. Probability, random variables, and stochastic processes. Tata McGraw-Hill Education, 2002.

R3. Rozanov, Yu. Probability theory, random processes and mathematical statistics. Vol. 344. Springer Science & Business Media, 2012.

ESE 30 marks

●      Discuss the core concepts Statistical Analytics and Data manipulation

●      Apply the basic principles, models, and algorithms supervised and unsupervised learning mechanisms.

●      Analyse the structures and algorithms of regression methods

●      Analyse the use of SVM in Data Science

Explain notions and theories associated to Convolutional Neural Networks

Knowledge discovery: finding structure in data, Data quality versus data quantity, Statistical modeling versus statistical description. Data types, Data summarization, Means, medians, and central tendency, Summarizing variation, Summarizing (bivariate) correlation, Data diagnostics and data transformation, Outlier analysis, Entropy, Data transformation Simple smoothing techniques, Binning, Moving averages, Exponential smoothing. Introduction to SPSS (IBM’s) statistical tool.

The simple linear model, Multiple inferences and simultaneous confidence bands, Regression diagnostics, Weighted least squares (WLS) regression, Correlation analysis. Unsupervised versus supervised learning, Principal component analysis, Principal components, Implementing a PCA, Exploratory factor analysis.

Projection Pursuit Regression, Neural Networks, Fitting Neural Network, Some Issues in Training Neural Networks, Bayesian Neural Nets, 0 Computational Considerations.

Introduction, The Support Vector Classifier, Support Vector Machines and Kernels, Generalizing Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis

Definition of Random Forests, Details of Random Forests- Out of Bag Samples, Variable Importance, Proximity Plots; Analysis of Random Forests; Ensemble Learning, Boosting and Regularization Paths, Learning a Good Ensemble, Rule Ensembles.

T2. Russell, Stuart J., and Peter Norvig. Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited,, 2016.

R2. Ian Goodfellow and Yoshua Bengio and Aaron Courville,” Deep Learning ”, MIT Press, March 2018.

R3. Wu, James, and Stephen Coggeshall. Foundations of predictive analytics. Chapman and Hall/CRC, 2012.

R4. Marcoulides, George A., and Scott L. Hershberger. Multivariate statistical methods: A first course. Psychology Press, 2014.

R5. Morgan, George A., et al. IBM SPSS for introductory statistics: Use and interpretation. Routledge, 2012

ESE 30 marks

This mandatory course is aimed at providing a comprehensive overview of the different facets of Cyber Security.  In addition, the course will detail into specifics of Cyber Security with Cyber Laws both in Global and Indian Legal environments

Security Fundamentals-4 As Architecture Authentication Authorization Accountability, Social Media, Social Networking and Cyber Security.Cyber Laws, IT Act 2000-IT Act 2008-Laws for Cyber-Security, Comprehensive National Cyber-Security Initiative CNCI – Legalities

Cyber Attack and Cyber Services Computer Virus – Computer Worms – Trojan horse.Vulnerabilities -  Phishing -  Online Attacks – Pharming - Phoarging  –  Cyber Attacks  -  Cyber Threats -  Zombie- stuxnet - Denial of Service Vulnerabilities  - Server Hardening-TCP/IP attack-SYN Flood

Cyber Security Management Risk Management and Assessment - Risk Management Process - Threat Determination Process -Risk Assessment - Risk Management Lifecycle.Security Policy Management - Security Policies - Coverage Matrix Business Continuity Planning - DisasterTypes  -  Disaster Recovery Plan - Business Continuity Planning Process

Vulnerability - Assessment and Tools: Vulnerability Testing - Penetration Testing Black box- white box.Architectural Integration:  Security Zones - Devicesviz Routers, Firewalls, DMZ. Configuration Management - Certification and Accreditation for Cyber-Security.

Authentication and Cryptography: Authentication - Cryptosystems - Certificate Services, Securing Communications:  Securing Services -  Transport  –  Wireless  -  Steganography and NTFS Data Streams. Intrusion Detection and Prevention Systems:   Intrusion -  Defense in Depth  -  IDS/IPS  -IDS/IPS Weakness and Forensic AnalysisCyber Evolution: Cyber Organization – Cyber Future

R2. Thomas R, Justin Peltier, John, “Information Security Fundamentals”, Auerbach Publications.

R3. AtulKahate, “Cryptography and Network Security”,  2^nd Edition, Tata McGrawHill.2003

R4. Nina Godbole, SunitBelapure, “Cyber Security”, Wiley India 1^st Edition 2011

R5. Jennifer L. Bayuk and Jason Healey and Paul Rohmeyer and Marcus Sachs, “Cyber Security Policy Guidebook”, Wiley; 1 edition , 2012

R6. Dan Shoemaker and Wm. Arthur Conklin, “Cyber security: The Essential Body Of Knowledge”,   Delmar Cengage Learning; 1 edition, 2011

R7. Stallings, “Cryptography & Network Security - Principles & Practice”, Prentice Hall, 6th Edition 2014

Maximum Marks : 50

·

To study the fundamentals of digital circuits and concepts used in the analysis and design of various digital systems.

Switching Theory: Laws of Boolean algebra, Theorems of Boolean algebra, Switching functions, Methods for specification of switching functions - Truth tables and Algebraic forms, Realization of functions using logic gates. Digital Logic Elements: Electronic logic gates, Positive and negative logic, Logic families -TTL, ECL and CMOS, Realization of logic gates.

Simplification of Boolean Expressions and Functions: Algebraic methods,     Canonical forms of Boolean functions, Minimization of functions using Karnaugh     maps, Minimization of functions using Quine-McClusky method.

Design of Combinational Logic Circuits: Gate level design of Small Scale     Integration (SSI) circuits, Modular combinational logic elements - Decoders,     Encoders, Priority encoders, Multiplexers and Demultiplexers. Design of Integer     Arithmetic Circuits using Combinational Logic: Integer adders - Ripple carry adder     and Carry look ahead adder, Integer subtractors using adders, Unsigned integer     multipliers - Combinational array circuits, Signed integer multipliers - Booth's     coding, Bit-pair recoding, Carry save addition and Wallace tree multiplier, Signed     integer division circuits - Combinational array circuits, Complexity and propagation     delay analysis of circuits. Design of Combinational Circuits using Programmable     Logic Devices (PLDs): Programmable Read Only Memories (PROMs),     Programmable Logic Arrays (PLAs), Programmable Array Logic (PAL) devices,     Design of multiple output circuits using PLDs.

Sequential Circuit Elements: Latches -RS latch and JK latch, Flip-flops-RS, JK, T     and D flip flops, Master-slave flip flops, Edge-triggered flip-flops. Analysis and     Design of Synchronous Sequential Circuits: Models of sequential circuits - Moore     machine and Mealy machine, Flip-flops - Characteristic table, Characteristic     equation and Excitation table, Analysis of sequential circuits- Flipflop input     expressions, Next state equations, Next state maps, State table and State transition     diagram, Design of sequential circuits - State transition diagram, State table, Next     state maps, Output maps, Expressions for flip-flop inputs and Expressions for circuit     outputs, Modular sequential logic circuits- Shift registers, Registers, Counters and  Random access memories, Design using programmable logic sequencers (PLSs).     Design of Arithmetic Circuits using Sequential Logic : Serial adder for integers,     Unsigned integer multiplier, Unsigned integer division circuits, Signed integer     division, Floating-pint adder/subtractor - Design of control circuit, Floating - point multiplier.

Case study: Learn the Fundamentals of Digital Logic Design with VHDL Informal Laboratory:

·         Design and implementation of binary adder / subtractor using basic gates

·         Design and implementation of applications using multiplexers

·         Design and implementation of Synchronous & Asynchronous Counters

·         Design and implementation of Shift Registers

Coding Combinational Circuits using Hardware Description Language (HDL)

T1 - Donald P Leach, Albert Paul Malvino&GoutamSaha, “Digital Principles and Applications” , Tata McGraw Hill 7^th Edition, 2010.

R2- R D Sudhaker Samuel, “Illustrative Approach to Logic Design. Sanguine-Pearson”, 2010.

R3- Charles H. Roth, “Fundamentals of Logic Design”, Cengage Learning, 5th Edition, 2004.

R4- Ronald J. Tocci, Neal S. Widmer. Gregory L. Moss, “Digital Systems Principles and     Applications, ” 10^th  Edition. Pearson Education, 2007

R5- M Morris Mano, “Digital Logic and Computer Design”, Pearson Education, 10th Edition, 2008.

Assessment of each paper

·         Continuous Internal Assessment (CIA) for Theory papers: 50% (50 marks out

of 100 marks)

·         End Semester Examination(ESE) : 50% (50 marks out of 100 marks)

Components of the CIA

CIA I   :   Mid Semester Examination (Theory)                    : 25 marks                  

CIA II  :  Assignments                                                            : 10 marks

CIA III            : Quizzes/Seminar/Case Studies/Project Work     : 10 marks

Attendance                                                                             : 05 marks

            Total                                                                                         : 50 marks

For subjects having practical as part of the subject

Assessment of Practical paper

Conduct of experiments                                                       : 25 marks

Observations/Lab Record                                                   : 15 marks

Viva voce                                                                                : 10 marks

Total                                                                                        : 50 marks

(All the above assessments are carried for each experiment during regular lab classes and averaged to max 50 marks at the end of the semester)

Course Description:

Technical Writing Course consists of five units covering; Introduction to Technical Communication, Technical Writing, Soft Skills, Professional Presentation Skills and Professional Etiquettee. It aims to equip the students with the necessary technical communication and writing skills for professional success. 

Course objectives:

This course aims to equip engineering students with effective individual and collaborative technical writing and presentation skills which are necessary to be effective technical communicators in academic and professional contexts.

Communication Process, Flow, Barriers. Analyzing different kinds of technical documents, Reports/Engineering reports – Types, Importance and Structure of formal reports, information and document design.

Vocabulary for professional writing. Idioms and collocations, Writing drafts and revising, writing style and language. Writing Emails, Resumes, Video resume, Interviews, Types of interviews.

Self development process, Personality development, Types of personality, Perception and attitudes, Emotional intelligence, Time Management, Values and beliefs, Personal goal setting, Creativity, Conflict management, Career planning. 

Writing a speech, Formal presentations, Public speaking, Presentation aids, Group communication, Discussions, Organizational GD, Meetings & Conferences.

Email etiquettes, Telephone Etiquettes, Engineering ethics, Role and responsibility of engineer, Work culture in jobs.

T2: Diane Hacker, Pocket Style Manual, Bedford Publication, New York, 2003. (ISBN 0312406843)

T3: Raman Sharma, Technical Communications, Oxford Publication, London, 2004 

R2. Sharma, R. and Mohan, K. Business Correspondence and Report Writing, TMH New Delhi 2002.

R3. Xebec, Presentation Book, TMH New Delhi, 2000. (ISBN 0402213)

CIA 50 Marks

ESE 50 Marks

CIA 1 Quiz/Assignment/Grammar Test/Oral Talk

CIA 2 Mid Semester Exam: MSE CIA

3 Presentation/Mini Project/Portfolio

COURSE DESCRIPTION:

This course, Discrete Mathematics (MA334) is offered for three credits in the third semester for the branch of Computer Science Engineering and Information and Technology for different streams. This course develops the logical augmenting and it has topics like Propositional Calculus, Set theory, Group theory, and Coding various Counting techniques.

COURSE OBJECTIVE:

The objective of the paper is to apply logical reasoning to validate the computer algorithms, to perform the operations associated with sets, functions, relations and groups for the coding and decoding information to check the security of the data. 

Propositions – Logical connectives – Compound propositions – Conditional and bi conditional propositions – Truth tables – Tautologies and contradictions – Contrapositive – Logical equivalences and implications – De Morgan’s Laws - Normal forms, Rules of inference – Arguments - Validity of arguments.

Basic concepts of Sets - Subset – Algebra of sets – The power set – Ordered pairs and Cartesian product – Relations on sets –Types of relations and their properties – Matrix and Graph representation of a relation – Partial ordering – Poset – Hasse diagram – Lattices and their properties – Sublattices – Boolean algebra.

Properties – Subgroups - Cosets and Lagrange’s theorem – Normal subgroups – Algebraic system with two binary operations – Preliminaries of Coding -  Hamming Metric  - group codes: –  Basic notions of error correction - Error recovery in group codes.

Types of functions - Examples – Composition of functions – Inverse functions – Characteristic function of a set, Mathematical Induction, The Rules of Sum and Product, Permutations, Combinations.

Fundamental principles of counting, pigeonhole principle, principle of inclusion and exclusion, Solving Linear Recurrence Relations, Divide-and-Conquer Algorithms and Recurrence Relations, generating functions, Solve Recurrence Relations using Generating Functions.

T1. Trembly J.P and Manohar R, Discrete Mathematical Structures with Applications to Computer Science, Tata McGrawHill Pub.Co. Ltd, New Delhi, 2003. 

T2. Ralph. P. Grimaldi, Discrete and Combinatorial Mathematics: An Applied Introduction, Fifth Edition, Pearson Education Asia,Delhi, 2009.

1.      R1. Bernard Kolman, Robert C. Busby, Sharan Cutler Ross, Discrete Mathematical Structures,  Fourth Indian reprint, Pearson Education Pvt Ltd., New Delhi, 2003.

2.      R2. Kenneth H. Rosen, Discrete Mathematics and its Applications, Fifth Edition, Tata McGraw Hill Pub. Co. Ltd., New Delhi, 2003.

3.      R3. Richard Johnsonbaugh, Discrete Mathematics, Fifth Edition,Pearson Education Asia, New Delhi, 2002.

End Semester Examination(ESE)      : 50% (50 marks out of 100 marks) 

Components of the CIA

CIA I  :  Subject Assignments / Online Tests                  : 10 marks

CIA II :   Mid Semester Examination (Theory)                : 25 marks            

CIAIII: Quiz/Seminar/Innovative Assignments/presentations/publications : 10 marks

Attendance                                                                           : 05 marks

            Total                                                                                       : 50 marks

Mid Semester Examination (MSE) : Theory Papers:

• The MSE is conducted for 50 marks of 2 hours duration.
• Question paper pattern; Four questions have to be answered in part A without any choice. One question need to be answered out of two in part B. Each  question carries 10 marks

End Semester Examination (ESE):

The ESE is conducted for 100 marks of 3 hours duration.

The syllabus for the theory papers are divided into FIVE units and each unit carries equal Weightage in terms of marks distribution.

Question paper pattern is as follows.

Two full questions with either or choice will be drawn from each unit. Each question carries 20 marks. There could be a maximum of three sub divisions in a question. The emphasis on the questions is to test the objectiveness, analytical skill and application skill of the concept, from a question bank which reviewed and updated every year

The criteria for drawing the questions from the Question Bank are as follows

50 % - Medium Level questions

25 % - Simple level questions

25 % - Complex level questions

This course focuses on the fundamentals of psychology. It is an introductory paper that gives an overall understanding about the human behavior. It will provide students with an introduction to the key concepts, perspectives, theories, and sub-fields on various basic processes underlying human behavior. 

1. To understand the fundamental processes underlying human behavior
2. To become aware of one’s idiosyncrasies and predispositions
3. To apply the understanding of concepts in day-to-day activities

Definition, Characteristics of Sensory modalities: Absolute and difference threshold; Signal detection theory; sensory coding; Vision, Audition, Other Senses. Assessment of Perception and Sensation

Practicum: Aesthesiometer

Definition, Understanding perception, Gestalt laws of organization, Illusions and Perceptual constancy; Various sensory modalities; Extrasensory perception.

Practicum:  Muller-Lyer Illusion

Learning:Definition, Classical conditioning, Instrumental conditioning, learning and cognition; Memory:  Types of Memory; Sensory memory, working memory, Long term memory, implicit memory, Constructive memory, improving memory; Assessment of memory.

Practicum: Memory drum

Concepts and nature of Individual differences; Nature vs. nurture; Gender difference in cognitive processes and social behavior; Intelligence: Definition, Contemporary theories of intelligence; Tests of intelligence; Emotional, Social and Spiritual intelligence.

Practicum: Bhatia’s Battery of Performance

Definition, Type and trait theories of personality, Type A, B & C. Psychoanalytic -  Freudian perspective; Types of personality assessment.

Practicum: NEO-FFI 3

Rathus, S. A. (2017). Introductory Psychology, 5thEd. Belmont, CA: Wadsworth.

Nolen-Hoeksema, S., Fredrickson, B.L. & Loftus, G.R. (2014). Atkinson & Hilgard'sIntroduction to Psychology.16th Ed. United Kingdom: Cengage Learning.

Morgan, C. T., King, R. A., & Schopler, J. (2004). Introduction to Psychology. New Delhi: Tata     McGraw Hill.

Kalat, J. W. (2016). Understanding Psychology. New York: Cengage Learning

CIA 2: Mid-Semester Examinations (Written Examination)

Pattern: Section A       5 x 02 = 10 marks

               Section B      4 x 05 = 20 marks      

               Section C       1 x 10 = 10 marks (Internal Choice)

               Section D       1 x 10 = 10 marks (Case Vignette)

CIA 3: Group Assignments

ESE: End Semester Examination (Written Examination)

Pattern: Section A       5 x 02 = 10 marks

               Section B      4 x 05 = 20 marks      

               Section C       1 x 10 = 10 marks (Internal Choice)

               Section D       1 x 10 = 10 marks (Case Vignette)

The  PCAP: Programming Essentials in Python course covers  all the basics  of programming  in  Python 3, as well as general computer  programming  concepts and techniques. The course also familiarizes the student with the object-oriented approach. 

Introduction to Python and Computer Programming, Data Types, Variables, Basic, Input-Output Operations, Basic Operators,Boolean Values, Conditional Execution, Loops, Lists and ListProcessing, Logic and BitwiseOperations, Functions, Tuples, Dictionaries, and Data Processing.

Exceptions, Strings, String and List Methods,Object Oriented Programming in Python,Working with filesystem, Directory trees and Files,Selected Python Standard Library modules (os, date, datetime,calendar).

This course of study builds on the skills gained by students in Java Fundamentals or Java Foundations to help advance Java programming skills. Students will design object-oriented applications with Java and will create Java programs using hands-on, engaging activities. 

Getting Started with Eclipse, Object and Driver Classes,  Data Types and Operators, Strings, Program Structure, Scanner and Conditional Statements, Control Statements, Arrays, Classes, Objects, and Methods - Parameters and Overloading Methods, The Static Modifier and Nested Classes,  Inheritance , Polymorphism, Generics, Collections ,  Sorting and Searching using Collection, String Class, String methods, Regular Expressions, Basics of Input and Output , Input and Output Fundamentals • Deploying an Application, JDBC Introduction.

Red Hat System Administration I (RH124) is designed for IT professionals without previous Linux system administration experience. The course provides students with Linux administration competence by focusing on core administration tasks. This course also provides a foundation for students who plan to become full-time Linux system administrators by introducing key command-line concepts and enterprise-level tools.

Red Hat System Administration II (RH134) is designed to build the skills to perform the key tasks needed to become a full-time Linux administrator. The course goes deeper into core Linux system administration skills 

Get started with Red Hat Enterprise LinuxDescribe and define open source, Linux, Linux distributions, and Red Hat Enterprise LinuxAccess the command lineLog into a Linux system and run simple commands using the shell.Manage files from the command lineCopy, move, create, delete, and organize files while working from the bash shell.Get help in Red Hat Enterprise LinuxResolve problems by using local help systems.Create, view, and edit text filesManage text files from command output or in a text editor.Manage local users and groupsCreate, manage, and delete local users and groups, as well as administer local password policies.Control access to filesSet Linux file system permissions on files and interpret the security effects of different permission settings.Monitor and manage Linux processesEvaluate and control processes running on a Red Hat Enterprise Linux system.Control services and daemonsControl and monitor network services and system daemons with the systemd service.Configure and secure SSHConfigure secure command line service on remote systems, using OpenSSH.Analyze and store logsLocate and accurately interpret logs of system events for troubleshooting purposes.Manage networkingConfigure network interfaces and settings on Red Hat Enterprise Linux servers.Install and update software packagesDownload, install, update, and manage software packages from Red Hat and DNF package repositories.Access Linux files systemsAccess, inspect, and use existing file systems on storage attached to a Linux server.Analyze servers and get supportInvestigate and resolve issues in the web-based management interface, getting support from Red Hat to help solve problems.Comprehensive reviewReview the content covered in this course by completing hands-on exercises.

Improve command line productivityRun commands more efficiently by using advanced features of the Bash shell, shell scripts, and various utilities provided by Red Hat Enterprise Linux.Schedule future tasksSchedule commands to run in the future, either one time or on a repeating schedule.Analyze and Store LogsLocate and accurately interpret system event logs for troubleshooting purposes.Archive and Transfer FilesArchive and copy files from one system to another.Tune system performanceImprove system performance by setting tuning parameters and adjusting scheduling priority of processes.Manage SELinux securityProtect and manage the security of a server by using SELinux.Manage logical volumesCreate and manage logical volumes containing file systems and swap spaces from the command line.Access network-attached storageUse the NFS protocol to administer network-attached storage.Control the boot processManage the boot process to control services offered and to troubleshoot and repair problems.Manage network securityControl network connections to services using the system firewall and SELinux rules.Install Red Hat Enterprise LinuxInstall Red Hat Enterprise Linux on servers and virtual machines.Run ContainersObtain, run, and manage simple, lightweight services as containers on a single Red Hat Enterprise Linux server.

To train students in applications of biology in the engineering domain. The course will deal with problems specific to the circuit branches

1. Experiment on biological sensors and their characteristics. 

2. Development of a biomedical instrument using sensors and signal processors. 6

3. Imaging technology for biological signals.

4. Integration and testing of the biomedical instrumentation systems.

5. Measurement of air quality.

T2. Gilbert M. Masters, ‘Introduction to Environmental Engineering and Science’, 2nd edition, Pearson Education, 2004. 

R2.ErachBharucha, “Textbook of Environmental Studies”, Universities Press(I) Pvt, Ltd, Hydrabad, 2015.

R3. G. Tyler Miller and Scott E. Spoolman, “Environmental Science”, Cengage 

ESE : 50 Marks

Introduction : What operating systems do, Computer System Architecture, Operating System Structure, Operating System Operations, Process Management,  Memory Management, Storage Management, Protection and Security; System Structures: Operating System Services, User Operating System Interface, System Calls, Types of System Calls.

Process Management: Process Concept, Process Scheduling, Operations on Processes, Inter-process Communication; Threads: Overview, Multithreading Models, Thread Libraries; CPU Scheduling: Basic Concepts, Scheduling Criteria, Scheduling Algorithms, Multiple- Processor Scheduling

Process Synchronization: Background, The Critical Section Problem, Peterson’s Solution, Synchronization Hardware, Semaphores, Classical Problems of Synchronization, Monitors, Synchronization Examples, Deadlocks

Memory Management: Background, Swapping, Contiguous Memory Allocation, Paging.

Virtual Memory : Background, Demand Paging, Copy on Write, Page Replacement, Allocation of frames, Thrashing, Allocating Kernel Memory

File System Interface: File System: File Concept, Access Methods, Directory Structure, File System Mounting, File Sharing, Protection.

File System Implementation & Mass Storage Structure: Implementing File Systems: File System Structure, File System Implementation, Directory Implementation, Allocation Methods, Free Space Management. Disk structure, Disk Attachment, Disk Scheduling Methods, Disk Management, Swap-Space Management

2. Harvey M. Deitel, “Operating Systems”, Pearson Education Pvt. Ltd, Third Edition, 2008.

R2. William Stallings, “Operating System- Internals and Design Principles”, Pearson Education, Nineth Edition, 2018.

R3. Pramod Chandra P. Bhatt – “An Introduction to Operating Systems, Concepts and Practice”, PHI, Fifth Edition, 2019.

ESE 30 Marks

Software development in business environment has become more sophisticated, the software implementation is becoming increasingly complex and requires the best programming paradigm which helps to eliminate complexity of large projects. Object Oriented Programming (OOP) has become the predominant technique for writing software at present. Many other important software development techniques are based upon the fundamental ideas captured by object-oriented programming. The course also caters to the understanding of event driven programming, generic programming and concurrent programming.

Review of OOP - Objects and classes in Java – defining classes – methods - access specifiers – static members – constructors – finalize method – Arrays – Strings - Packages – JavaDoc comments.

Inheritance – class hierarchy – polymorphism – dynamic binding – final keyword – abstract classes – the Object class – Reflection – interfaces – object cloning – inner classes

Graphics programming – Frame – Components – working with 2D shapes – Using color, fonts, and images - Basics of event handling – event handlers – adapter classes – actions – mouse events – AWT event hierarchy – introduction to Swing – Model-View- Controller design pattern – buttons – layout management – Swing Components

Motivation for generic programming – generic classes – generic methods – generic code and virtual machine – inheritance and generics – reflection and generics – exceptions – exception hierarchy – throwing and catching exceptions.

Multi-threaded programming – interrupting threads – thread states – thread properties – thread synchronization – thread-safe Collections – Executors – synchronizers – threads and event-driven programming, Parallel programming –fork, join framework.

R1. Herbert Schildt,  “Java: The Complete Reference (Complete Reference Series)”, Eleventh Edition, 2020.

R2. Cay S. Horstmann , “Java SE8 for the Really Impatient: A Short Course on the  Basics (Java Series)”, 2014.

R3. Bruce Eckel, “Thinking in Java”, 4th Edition, Prentice Hall Professional, 2006.

R4. Doug Rosenberg and Matt Stephens, “Use Case Driven Object Modeling with UML: Theory and Practice (Expert's Voice in UML Modeling)”,APress, 2013.

R5.Martina Seidl, Marion Scholz, Christian Huemer and GertiKappel , “UML @ Classroom An Introduction to Object-Oriented Modeling Series: Undergraduate Topics in Computer Science”, Springer, 2015

ESE - 30 Marks

 1. To have an understanding of finite state and pushdown automata.

2. To have a knowledge of regular languages and context free languages.

3. To know the relation between regular language, context free language and corresponding recognizers.

4. To study the Turing machine and classes of problems.

Automata - Introduction to formal proof – Additional forms of proof – Inductive proofs –Finite Automata (FA) – Central concepts of Automata Theory, Representation of Automata, Deterministic Finite Automata (DFA) – Non-deterministic Finite Automata (NFA) – Finite Automata with Epsilon transitions. Introduction to automata simulation tools

Regular Expressions and Languages 

Regular Expression – FA and Regular Expressions – Proving languages not to be regular – Closure properties of regular languages – Equivalence and minimization of Automata.

Context-Free Grammar and Languages  

Context-Free Grammar (CFG) – LMD, RMD, Parse Trees – Ambiguity in grammars and languages – Definition of the Pushdown automata – Languages of a Pushdown Automata, Designing of a PDA and string acceptance – Equivalence of Pushdown automata and CFG, Non-Deterministic Pushdown Automata.

Properties of Context-Free Languages  

Simplifications of CFG, Normal forms for CFG – Pumping Lemma for CFL - Closure Properties of CFL – Turing Machines – Definition, Problems, Language accepted, String acceptance, Programming Techniques for TM.

Undecidability  

A language that is not Recursively Enumerable (RE) – An undecidable problem that is RE – Undecidable problems about Turing Machine – Post’s Correspondence Problem - The classes P and NP, Linear Bounded Automata - Definition and examples

2. Peter Linz  “An Introduction to formal languages and automata”, sixth edition, Jones and Bartlett Learning, 2016.

2. J.Martin, “Introduction to Languages and the Theory of Computation”, 3rd Edition, TMH, 2003.

3. MichealSipser, “Introduction of the Theory and Computation”, Thomson Brokecole, 3rd Edition, 1997

End Semester Exam - 50 Marks

This course will help the students to learn about basic structure of computer system, design of arithmetic and logic unit with the implementation of fixed and floating point numbers. Further, it will give knowledge about design of control unit and pipelined processing concepts. It discusses about various parallel processing architectures, different memory systems and I/O Communication systems

Functional Units – Basic Operational Concepts – Performance – Instructions: Language of the Computer – Operations, Operands – Instruction representation – Logical operations – decision making – MIPS Addressing.

Addition and Subtraction – Multiplication – Division – Floating Point Representation – Floating Point Operations – Subword Parallelism

A Basic MIPS implementation – Building a Datapath – Control Implementation Scheme – Pipelining – Pipelined data path and control – Handling Data Hazards & Control Hazards – Exceptions.

Parallel processing challenges – Flynn‘s classification – SISD, MIMD, SIMD, SPMD, and Vector Architectures - Hardware multithreading – Multi-core processors and other Shared Memory Multiprocessors - Introduction to Graphics Processing Units, Clusters, Warehouse Scale Computers and other Message-Passing Multiprocessors.

Memory Hierarchy - memory technologies – cache memory – measuring and improving cache performance – virtual memory, TLB‘s – Accessing I/O Devices – Interrupts – Direct Memory Access – Bus structure – Bus operation – Arbitration – Interface circuits - USB.

T1. David A. Patterson and John L. Hennessy, “Computer Organization and Design: The Hardware/Software Interface”, Fifth Edition, Morgan Kaufmann / Elsevier, 2014.

T2. Carl Hamacher, ZvonkoVranesic, SafwatZaky and NaraigManjikian, “Computer Organization and Embedded Systems”, Sixth Edition, Tata McGraw Hill, 2012.

R1. William Stallings, “Computer Organization and Architecture – Designing for Performance”, Sixth Edition, Pearson Education, 2003.

R2.  John L. Hennessey and David A. Patterson, “Computer Architecture –A Quantitative Approach”, Fifth Edition, Morgan Kaufmann / Elsevier Publishers,2012.

ESE - 30% out of 100

Course Description:

This course offers a solid foundation of key ideas in machine learning. The objective is to impart expertise on using Python packages related to Machine Learning and supervised and unsupervised techniques.

Course Objective:

1. To gain knowledge in the basics of Python Machine Learning Libraries.

2. To analyze the applicability, benefits, and drawbacks of the Supervised and Unsupervised machine learning techniques.

3. To acquire knowledge on the recommendation systems and their working methodology

Artificial Intelligence - Introduction to Artificial Intelligence, Machine Learning, Deep Learning, Applications of Artificial Intelligence, Learning Agent.

Forms of Learning - Supervised Learning, Unsupervised Learning, Semi supervised Learning, Reinforcement Learning

Python libraries suitable for Machine Learning - Numpy, Pandas, Data visualization using matplotlib, sklearn.

Linear and Non Linear regression -Simple Linear regression,Multiple Linear Regression -

Multivariate Linear regression -Model Evaluation Methods(Loss Function, the cost function,

Residual Errors and Mean Square Error(MSE))-Applications of Regression.

K-Nearest Neighbors - Decision Tree - Support Vector Machines -Logistic RegressionClassification Metrics (Confusion Matrix, Accuracy, Precision Recall (Sensitivity), F1 Score, Area Under the Curve-Receiver Operator Characteristic (AUC-ROC))-Applications of classification

K-means clustering - Hierarchical clustering, Agglomerative Hierarchical clustering,Types of linkage - Density-Based Clustering,DBSCAN-Applications of Clustering

Recommendation System,Content-based filtering,Collaborative filtering,Hybrid - Real time case studies on Recommender Systems.

T2. Sebastian Raschka, Vahid Mirjalili, “Python Machine Learning - Third Edition”, O'Reilly Media, Inc.,Third Edition, December 2019, ISBN: 9781789955750.

R2. Wes McKinney, ”Python for Data: Data Wrangling with Pandas, NumPy, and IPython”, Second Edition,O′Reilly, 2017.

R3. John Paul Mueller, Luca Massaron, “Machine Learning For Dummies”, John Wiley & Sons, Inc., 2016.

R4. Tom M. Mitchell, Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.

R5. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.

ESE: 50

Course Description:

This course includes introductory concepts of computer networks scanning , hacking techniques, mobile hacking techniques, firewall techniques, and few case studies on various hacking scenarios.

Course Objective:

Teaching the phases of ethical hacking using various open source tools available for ethical hacking process like penetration testing ,information gathering, password cracking,and vulnerability assessment.

Introduction to ethical hacking, IP addressing, Network routing protocols, network security, network scanning, vulnerability assessment OpenVAS, Nessus, etc. of computation device (Mobile, PC, etc.) and system network.

Computation system hacking, modes of gathering information, password cracking, penetration testing including backdoor issues, Malware threats, and different cyber-related attacks.

Introduction to Mobile Hacking, encryption types and attacks, different mobile platforms, and corresponding vulnerabilities.

Evading firewalls, standard detection systems and frameworks, and other possible attack detection methods.

Case studies: various hacking scenarios, information gathering, and possible solutions.

T2. Weidman, Georgia. Penetration testing: a hands-on introduction to hacking. No Starch Press, 2014.

T3. Engebretson, Patrick. The basics of hacking and penetration testing: ethical hacking and penetration testing made easy. Elsevier, 2013.

T4. Dwivedi, Himanshu. Mobile application security. Tata McGraw-Hill Education, 2010.

https://www.openvas.org open source tool, network vulnerability scanner

Hacking: The Art of Exploitation

Metasploit: The Penetration Tester's Guide

Metasploit, Nessus, Recon-ng, Better cap, Ettercap, Open-Vas, Burp-suit, Hydra, Medusa, Air crack-ng.

ESE-50

Course Description:

This course gives an overview of Big Data Analytics and it can be described as the acts of studying data to observe patterns and to draw a conclusion to make an important decision. In addition, it also focuses on the big data technologies and tools such as Hadoop, Hive, HBase, and Pig that are available for storage, retrieval, and processing of big data. It helps a student to perform a variety of real-time analytics and processing of different data sets on different domains.

Course Objective:

1. To know the fundamental concepts of big data and analytics.

2. To explore tools and practices for working with big data.

3. To examine large amounts of data to uncover hidden patterns, correlations and other insights to help make data-informed decisions.

What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing,

Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big

data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big

data– big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics –

inter and trans firewall analytics.

Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships –graph databases – schema less databases – materialized views – distribution models – sharding – version – Map reduce –partitioning and combining – composing map-reduce calculations.

Data format – analysing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures.

MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic

Map-reduce and YARN – job scheduling – shuffle and sort – task execution –MapReduce types – input formats – output formats.

Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model –Cassandra examples – Cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation –HiveQL queries-case study.

T2. Eric Sammer, "Hadoop Operations",1st Edition, O'Reilley, 2012.

T3. Nataraj Dasgupta, "Practical Big Data Analytics", Packt Publishing Ltd., 2018. ISBN 978-1-78355-439-3

T4. Arshdeep Bahga & Vijay Madisetti, "Big Data Science & Analytics: A Hands-On Approach", Published by Vijay Madisetti, 2016. ISBN: 978-1-949978-00-1

R1. VigneshPrajapati, Big data analytics with R and Hadoop, SPD 2013.

R2. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011.

R3. Alan Gates, "Programming Pig", O'Reilley, 2011.

R4. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012.

ESE - 30%

To understand the scope and importance of environmental science towards developing a conscious community for environmental issues, both at global and local scale.  

Environment and Eco systems – Definition, Scope and importance. Components of environment. Concept and Structure of eco systems. Material Cycles – Nitrogen, Carbon, Sulphur, Phosphorous, Oxygen. Energy Flow and classification of Eco systems.   

Classification and importance- Forest, Water, Mineral, Food, Energy. Management of natural resources – challenges and methods. Sustainable development – Goals, Agriculture, Industries

Causes and Impacts – Air pollution, Water pollution, Soil Pollution, Noise Pollution, Marine Pollution, Municipal Solid Wastes, Bio Medical and E-Waste. Solid Waste Management

Global Temperature, Greenhouse effect, global energy balance, Global warming potential, International Panel for Climate Change (IPCC) Emission scenarios, Oceans and climate change. Adaptation methods. Green Climate fund. Climate change related planning- small islands and coastal region. Impact on women, children, youths and marginalized communities

Technology, Modern Tools – GIS and  Remote Sensing,. Institutional Mechanisms - Environmental Acts and Regulations, Role of government, Legal aspects. Role of Nongovernmental Organizations (NGOs) , Environmental Education and Entrepreneurship

T2Asthana and Asthana, “A text Book of Environmental Studies”, S. Chand, New Delhi, Revised Edition, 2010 [Unit: I, II, III and V]

T3Nandini. N, Sunitha. N and Tandon. S, “environmental Studies” , Sapana, Bangalore,  June 2019 [Unit: I, II, III and IV]

T4R Rajagopalan, “Environmental Studies – From Crisis to Cure”, Oxford, Seventh University Press, 2017, [Unit: I, II, III and IV]

R2.Masters, G andEla, W.P (2015), Introduction to environmental Engineering and Science, 3rd Edition. Pearson., New Delhi, 2013.

R3.Raman Sivakumar, “Principals of Environmental Science and Engineering”, Second Edition, Cengage learning Singapore, 2005.

R4.P. Meenakshi, “Elements of Environmental Science and Engineering”, Prentice Hall of India Private Limited, New Delhi, 2006.

R5.S.M. Prakash, “Environmental Studies”, Elite Publishers Mangalore, 2007

R6.ErachBharucha, “Textbook of Environmental Studies”, for UGC, University press, 2005.

R7. Dr. Pratiba Sing, Dr. AnoopSingh and Dr. PiyushMalaviya, “Textbook of Environmental and Ecology”, Acme Learning Pvt. Ltd. New Delhi.