Department of


Syllabus for

1 Semester  2020  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
CSC131  PROGRAMMING USING C AND DIGITAL COMPUTER FUNDAMENTALS  4  4  100 
CSC151  C PROGRAMMING LAB  2  2  50 
ENG121  ENGLISH  I  3  2  100 
FRN121  FRENCH  3  3  100 
MAT131  DIFFERENTIAL CALCULUS  4  4  100 
MAT151  DIFFERENTIAL CALCULUS USING MAXIMA  2  2  50 
STA131  DESCRIPTIVE STATISTICS AND PROBABILITY THEORY  4  4  100 
STA151  DESCRIPTIVE STATISTICS AND PROBABILITY PRACTICAL  2  2  50 
2 Semester  2020  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
CSC231  DATA STRUCTURES AND OPERATING SYSTEMS  4  4  100 
CSC251  DATA STRUCTURES LAB  2  2  50 
ENG221  ENGLISH  II  3  2  100 
FRN221  FRENCH  3  3  100 
MAT231  DIFFERENTIAL EQUATIONS  4  4  100 
MAT251  DIFFERENTIAL EQUATIONS USING MAXIMA  2  2  50 
STA231  STATISTICAL METHODS  4  4  100 
STA232  R PROGRAMMING  4  4  100 
STA251  STATISTICAL METHODS PRACTICAL  2  2  50 
3 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
AEN321  ADDITIONAL ENGLISH  3  3  100 
CSC331  DATABASE MANAGEMENT SYSTEM AND JAVA PROGRAMMING  4  4  100 
CSC351  JAVA PROGRAMMING LAB  2  2  50 
ENG321  ENGLISHIII  3  3  100 
FRN321  FRENCH  3  3  100 
HIN321  HINDI  3  2  50 
KAN321  KANNADA  3  03  100 
MAT331  REAL ANALYSIS  4  4  100 
MAT351  INTRODUCTION TO PYTHON PROGRAMMING FOR MATHEMATICS  2  2  50 
STA331  STATISTICAL INFERENCE  4  4  100 
STA332  APPLIED EXCEL  4  4  100 
STA351  STATISTICAL INFERENCE PRACTICAL  2  2  50 
4 Semester  2019  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
AEN421  ADDITIONAL ENGLISH  3  3  100 
CSC431  SOFTWARE ENGINEERING AND COMPUTER NETWORKS  4  4  100 
CSC451  WEB TECHNOLOGY LAB  2  2  50 
ENG421  ENGLISHIV  3  3  100 
FRN421  FRENCH  3  3  100 
HIN421  HINDI  3  2  50 
KAN421  KANNADA  3  03  100 
MAT431  ALGEBRA  4  4  100 
MAT451  INTRODUCTION TO MATHEMATICAL MODELLING USING PYTHON  2  2  50 
STA431  SAMPLING TECHNIQUES  4  4  100 
STA451  SAMPLING TECHNIQUES PRACTICAL  2  2  50 
5 Semester  2018  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
CSC541A  DATA ANALYTICS  3  3  100 
CSC541B  INTERNET OF THINGS  3  3  100 
CSC541C  DIGITAL IMAGE PROCESSING  3  3  100 
CSC541D  BUSINESS INTELLIGENCE  3  3  100 
CSC542A  UNIX OPERATING SYSTEM  3  3  100 
CSC542B  PYTHON PROGRAMMING  3  3  100 
CSC542D  GRAPHICS AND ANIMATION  3  3  100 
CSC542E  .NET TECHNOLOGY  3  3  100 
CSC551A  DATA ANALYTICS LAB  2  2  50 
CSC551B  INTERNET OF THINGS LAB  2  2  50 
CSC551C  DIGITAL IMAGE PROCESSING LAB  2  2  50 
CSC551D  BUSINESS INTELLIGENCE LAB  2  2  50 
CSC552A  UNIX OPERATING SYSTEM LAB  2  2  50 
CSC552B  PYTHON PROGRAMMING LAB  2  2  50 
CSC552D  GRAPHICS AND ANIMATION LAB  2  2  50 
CSC552E  .NET TECHNOLOGY LAB  2  2  50 
MAT531  LINEAR ALGEBRA  3  3  100 
MAT541A  INTEGRAL TRANSFORMS  3  3  100 
MAT541B  MATHEMATICAL MODELLING  3  3  100 
MAT541C  GRAPH THEORY  3  3  100 
MAT541D  CALCULUS OF SEVERAL VARIABLES  3  3  100 
MAT541E  OPERATIONS RESEARCH  3  3  100 
MAT551  LINEAR ALGEBRA USING PYTHON  2  2  50 
MAT551A  INTEGRAL TRANSFORMS USING PYTHON  2  2  50 
MAT551B  MATHEMATICAL MODELLING USING PYTHON  2  2  50 
MAT551C  GRAPH THEORY USING PYTHON  2  2  50 
MAT551D  CALCULUS OF SEVERAL VARIABLES USING PYTHON  2  2  50 
STA531  LINEAR REGRESSION MODELS  3  3  100 
STA541A  STATISTICAL QUALITY CONTROL  3  3  100 
STA541B  DESIGN OF EXPERIMENTS  3  3  100 
STA541C  ACTUARIAL STATISTICS  3  3  100 
STA541D  INTRODUCTION TO SPATIAL STATISTICS  3  3  100 
STA551  LINEAR REGRESSION MODELS PRACTICAL  2  2  50 
STA552A  STATISTICAL QUALITY CONTROL PRACTICAL  2  2  50 
STA552B  DESIGN OF EXPERIMENTS PRACTICAL  2  2  50 
STA552C  ACTUARIAL STATISTICS PRACTICAL  2  2  50 
STA552D  SPATIAL STATISTICS PRACTICAL  2  2  50 
6 Semester  2018  Batch  
Paper Code 
Paper 
Hours Per Week 
Credits 
Marks 
CSC631  DESIGN AND ANALYSIS OF ALGORITHMS  3  3  100 
CSC641A  INTRODUCTION TO SOFT COMPUTING  3  3  100 
CSC641B  CLOUD COMPUTING  3  3  100 
CSC641C  COMPUTER ARCHITECTURE  3  3  100 
CSC641D  OOAD USING UML  3  3  100 
CSC641E  USER EXPERIENCE DESIGN(UX)  3  3  100 
CSC681  MAIN PROJECT  4  04  100 
MAT631  COMPLEX ANALYSIS  3  3  100 
MAT641B  NUMERICAL METHODS  3  3  100 
MAT641C  DISCRETE MATHEMATICS  3  3  100 
MAT641D  NUMBER THEORY  3  3  100 
MAT641E  FINANCIAL MATHEMATICS  3  3  100 
MAT651  COMPLEX ANALYSIS USING PYTHON  2  2  50 
MAT651A  MECHANICS USING PYTHON  2  2  50 
MAT651B  NUMERICAL METHODS USING PYTHON  2  2  50 
MAT651C  DISCRETE MATHEMATICS USING PYTHON  2  2  50 
MAT651D  NUMBER THEORY USING PYTHON  2  2  50 
MAT651E  FINANCIAL MATHEMATICS USING PYTHON  2  2  50 
MAT681  PROJECT ON MATHEMATICAL MODELS  5  5  150 
STA631  TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES  3  3  100 
STA641A  APPLIED STATISTICS  3  3  100 
STA641B  ELEMENTS OF STOCHASTIC PROCESS  3  3  100 
STA641C  BIOSTATISTICS  3  3  100 
STA641D  STATISTICAL GENETICS  3  3  100 
STA651  TIME SERIES ANALYSIS AND FORECASTING TECHNIQUES PRACTICAL  2  2  50 
STA652A  APPLIED STATISTICS PRACTICAL  2  2  50 
STA652B  ELEMENTS OF STOCHASTIC PROCESS PRACTICAL  2  2  50 
STA652C  BIOSTATISTICS PRACTICAL  2  2  50 
STA652D  STATISTICAL GENETICS PRACTICAL  2  2  50 
 
Assesment Pattern  
Exam pattern for theory
Exam Pattern for practical
Total Marks : 50  
Examination And Assesments  
 
Department Overview:  
Department of Computer Science of CHRIST(Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field.
Department of Mathematics, CHRIST (Deemed to be University) is one of the oldest departments of the University, established in the year 1969. It offers programmes in Mathematics at the undergraduate level, post graduate level as well as M.Phil and Ph.D. It is equipped with the highly committed team of instructors having versatile experience in teaching, research and has a passion to explore and innovate. Department is committed to provide the quality education in Mathematics, facilitate the holistic development, encourage students for pursuing higher studies in mathematics and motivate students to uphold scientific integrity and objectivity in professional endeavors.
Department of Statistics is committed to excellence in teaching and equipping students to become practicing statisticians. The main objectives of the department are: 1. To acquaint students with various statistical methods and their applications in different fields 2. To cultivate statistical thinking among students 3. To develop skills in handling complex problems in data analysis and research design 4. To prepare students for futu  
Mission Statement:  
Vission: EXCELLENCE AND SERVICE
Mission(Computer science department): To develop IT professionals with ethical and human values.
Mission(Department of Mathematics): To organize, connect, create and communicate mathematical ideas effectively, through 4D's; Dedication, Discipline, Direction and Determination.
Mission:The department of Statistics is committed to excellence in teaching and equipping students to become practicing statisticians.  
Introduction to Program:  
Bachelor of Science (BSc  Computer Science, Mathematics ,Statistics) is a 3year undergraduate triple main programme spread over six semesters. It is an interdisciplinary program aimed at fostering sound fundamentals in computer science, mathematics statistics. The curriculum in computer science scales from imparting basic concepts in lower semesters to fine grain level along with electives in the higher semesters. Programming labs and projects strengthen the domain knowledge and exposure during the triple main course.The students are imparted both theoretical as well as practical knowledge in statistics using multiple statistical software.
Mathematics: The undergraduate course in Mathematics is designed to enable the students to lay a strong foundation in various fields of Mathematics. The course enables the students to develop a respectable intellectual level seeking to expose the various concepts in Mathematics. It also aims at enhancing the students reasoning, analytical and problem solving skills. The first four semesters are devoted to appreciate the beauty of mathematics through Differential Calculus, Differential Equations, Real Analysis and Algebra. In order to help the students in exploration of mathematical concepts through activities and exploration, FOSS (Free and Open Source Software) tool MAXIMA and the computer language "Python" are introduced. Students find better perceptions of the classical courses like Linear Algebra, Complex Analysis and the elective co  
Program Objective:  
Programme Objective:The programme aims at providing theoretical and practical exposure to students to a varied range of statistical techniques in order to equip them to face the challenges of Industry and Higher Education.
Programme Outcomes: On successful completions of the BSc Programme students will be able to
PO1. Understand and apply the fundamental principles, concepts and methods in key areas of science and multidisciplinary fields
PO2. Demonstrate problem solving, analytical and logical skills to provide solutions for the scientific requirements
PO3. Develop the critical thinking with scientific temper
PO4. Communicate the subject effectively
PO5. Understand the importance and judicious use of technology for the sustainable growth of mankind in synergy with nature
PO6. Understand the professional, ethical and social responsibilities
PO7. Enhance the research culture and uphold the scientific integrity and objectivity
PO8. Engage in continuous reflective learning in the context of technological and scientific advancements
Programme Specific Outcomes:
PSO1. Apply the theoretical concepts to design and develop software.
PSO2. Demonstrate the problem solving skills in mathematical and digital sciences.
PSO3. Provide a comprehensive understanding of Data Science and its applications.
PSO4. Acquire a strong foundation in Statistical analytics
PSO5. Express proficiency in oral and written communications to appreciate innovation in research.
PSO6. Use sof  
 
Assesment Pattern  
CIA : 50% ESE : 50%  
Examination And Assesments  
CIA : 50% ESE : 50%  
Department Overview:  
Department of Computer Science of CHRIST(Deemed to be University) strives to shape outstanding computer professionals with ethical and human values to reshape nation?s destiny. The training imparted aims to prepare young minds for the challenging opportunities in the IT industry with a global awareness rooted in the Indian soil, nourished and supported by experts in the field. Department of Mathematics, CHRIST (Deemed to be University) is one of the oldest departments of the University, established in the year 1969. It offers programmes in Mathematics at the undergraduate level, post graduate level as well as M.Phil and Ph.D. It is equipped with the highly committed team of instructors having versatile experience in teaching, research and has a passion to explore and innovate. Department is committed to provide the quality education in Mathematics, facilitate the holistic development, encourage students for pursuing higher studies in mathematics and motivate students to uphold scientific integrity and objectivity in professional endeavors. Department of Statistics is committed to excellence in teaching and equipping students to become practicing statisticians. The main objectives of the department are: 1. To acquaint students with various statistical methods and their applications in different fields 2. To cultivate statistical thinking among students 3. To develop skills in handling complex problems in data analysis and research design 4. To prepare students for future cours  
Mission Statement:  
Vision and Mission:
Vission: EXCELLENCE AND SERVICE
Mission(Computer science department): To develop IT professionals with ethical and human values. Mission(Department of Mathematics): To organize, connect, create and communicate mathematical ideas effectively, through 4D's; Dedication, Discipline, Direction and Determination. Mission:The Department of Statistics is committed to excellence in teaching and equipping students to become practicing statisticians.  
Introduction to Program:  
Bachelor of Science (BSc  Computer Science, Mathematics , Statistics) is a 3year undergraduate triple main programme spread over six semesters. It is an interdisciplinary program aimed at fostering sound fundamentals in computer science, mathematics statistics. The curriculum in computer science scales from imparting basic concepts in lower semesters to finegrain level along with electives in the higher semesters. Programming labs and projects strengthen the domain knowledge and exposure during the triple main course. The students are imparted both theoretical as well as practical knowledge in statistics using multiple statistical software. Mathematics: The undergraduate course in Mathematics is designed to enable the students to lay a strong foundation in various fields of Mathematics. The course enables the students to develop a respectable intellectual level seeking to expose the various concepts in Mathematics. It also aims at enhancing the students reasoning, analytical, and problemsolving skills. The first four semesters are devoted to appreciate the beauty of mathematics through Differential Calculus, Differential Equations, Real Analysis and Algebra. In order to help the students in exploration of mathematical concepts through activities and exploration, FOSS (Free and Open Source Software) tool MAXIMA and the computer language "Python" are introduced. Students find better perceptions of the classical courses like Linear Algebra, Complex Analysis and elective cour  
Program Objective:  
Programme Objective:The programme aims at providing theoretical and practical exposure to students to a varied range of statistical techniques in order to equip them to face the challenges of Industry and Higher Education.
Programme Outcomes: On successful completions of the BSc Programme students will be able to PO1. Understand and apply the fundamental principles, concepts and methods in key areas of science and multidisciplinary fields PO2. Demonstrate problem solving, analytical and logical skills to provide solutions for the scientific requirements PO3. Develop the critical thinking with scientific temper PO4. Communicate the subject effectively PO5. Understand the importance and judicious use of technology for the sustainable growth of mankind in synergy with nature PO6. Understand the professional, ethical and social responsibilities PO7. Enhance the research culture and uphold the scientific integrity and objectivity PO8. Engage in continuous reflective learning in the context of technological and scientific advancements Programme Specific Outcomes: PSO1. Apply the theoretical concepts to design and develop software. PSO2. Demonstrate the problem solving skills in mathematical and digital sciences. PSO3. Provide a comprehensive understanding of Data Science and its applications. PSO4. Acquire a strong foundation in Statistical analytics PSO5. Express proficiency in oral and written communications to appreciate innovation in research. PSO6. Use software effectively f  
CSC131  PROGRAMMING USING C AND DIGITAL COMPUTER FUNDAMENTALS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course Objectives The course provides the fundamentals of C programming, number systems, Boolean algebra and logic gates. The C programming helps the students to solve problems through logical thinking and digital logic helps the students to understand the concepts of constructing combinational and sequentialcircuits.


Learning Outcome 

CO1: Understand the fundamentals of structured programming, number systems, Boolean algebra and logic gates CO2: Learn to implement the concepts of arrays, functions, pointers, structures and to analyse logical expressions. CO3: To create programs with ethical coding standards. CO4: To design combinational and sequential circuits.

Unit1 
Teaching Hours:6 
Introduction


Algorithms  flowcharts The C Character Set  Constants, Variables and Keywords  Types of C Constants  Types of C Variables  Variable Names  C Instructions – data Type Declaration I/O instructions  Arithmetic Instruction  TypeConversion.
 
Unit2 
Teaching Hours:6 
Control Structure


The Decision Control Structure  The if Statement ifelse Statement Nested ifelse Use of Logical Operators  ! Operator  Decisions Using switch  The Loop Control Structure While Loop  for Loop  break Statement  continue Statement dowhile Loop.  
Unit3 
Teaching Hours:6 
Arrays


A Simple Program Using Array  Array Initialization  Two Dimensional Arrays Initializing a 2Dimensional Array  Memory Map of a 2Dimensional Array – Strings  Standard Library String Functions  strlen( )  strcpy( )  strcat()  strcmp()  TwoDimensional Array of Characters.  
Unit4 
Teaching Hours:6 
Functions & Pointers


Function  Passing Values between Functions  Scope Rule of Functions  Calling Convention  Return Type of Function  Call by Value and Call by Reference  An Introduction to Pointers  Pointer Notation –Recursion.  
Unit5 
Teaching Hours:6 
Macros and Structures


Introduction to macros, Structures  Declaring a Structure  Accessing Structure Elements  How Structure Elements areStored.
 
Unit6 
Teaching Hours:7 
Introduction to Computers & Number systems


Different number systems and their conversions (Decimal, Binary, Octal and Hexadecimal) Binary arithmetic  Addition, subtraction, multiplication and division of binary numbers, 1’s and 2’s complement, Floating point numbers, Coding – BCD, Gray,ASCII
 
Unit7 
Teaching Hours:6 
Boolean Algebra


Boolean operations and expressions, Laws and rules of boolean algebra, Demorgan’s Theorem, Boolean expressions, Simplification of Booleanexpression.  
Unit8 
Teaching Hours:6 
Logic Gates


OR gate, NOR gate , NOT gate , AND gate, NAND gate XOR gate , XNOR gate, The universal property of NOR and NAND gate, Karnaugh map (SOP).  
Unit9 
Teaching Hours:5 
Combinational logic


Adders (Half and Full), Decoder, Encoder, Multiplexer, DeMultiplexer (Introductory ConceptsOnly).
 
Unit10 
Teaching Hours:6 
FlipFlops


Flipflops SR flipflop, JK flipflop, Master slave JK flipflop, Introduction to Registers andCounters.
 
Text Books And Reference Books: [1] Yashavant P. Kanetkar, Let Us C, 15th Edition, BPB Publications, 2012. [2] Floyd and Thomas L, Digital Computer Fundamentals, 11th Edition, Pearson International, 2015.
 
Essential Reading / Recommended Reading [1] Byron Gottfried and Jitender Chhabra, Programming with C, 3rd Ed, Tata McGrawHill, 2010. [2] Balagurusamy E, Programming in ANSI C, 4th Edition, TataMcGrawHill, 2007. [3] Deitel H M and Deitel P J, C  How to Program, 7th Edition, PrenticeHall, 2012. [4] Susant K Rout, Cimple,C, TataMcGrawHill Publishing Company Ltd., 2016. [5] Malvino, Paul Albert, Leach, Donald P. Gautam Saha, Digital Principles And Applications, 7th Edition,TMH,2010. [6] Bartee, Thomas C, Digital Computer Fundamentals, 6th Edition, TMH,2010  
Evaluation Pattern CIA50% ESE50%  
CSC131N  PROGRAMMING USING C AND DIGITAL COMPUTER FUNDAMENTALS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 
Max Marks:100 
Credits:4 
Course Objectives/Course Description 

Course Objectives The course provides the fundamentals of C programming, number systems, Boolean algebra and logic gates. The C programming helps the students to solve problems through logical thinking and digital logic helps the students to understand the concepts of constructing combinational and sequentialcircuits.


Learning Outcome 

CO1: Understand the fundamentals of structured programming, number systems, Boolean algebra and logic gates CO2: Learn to implement the concepts of arrays, functions, pointers, structures and to analyse logical expressions. CO3: To create programs with ethical coding standards. CO4: To design combinational and sequential circuits.

Unit1 
Teaching Hours:6 
Introduction


Algorithms  flowcharts The C Character Set  Constants, Variables and Keywords  Types of C Constants  Types of C Variables  Variable Names  C Instructions – data Type Declaration I/O instructions  Arithmetic Instruction  TypeConversion.
 
Unit2 
Teaching Hours:6 
Control Structure


The Decision Control Structure  The if Statement ifelse Statement Nested ifelse Use of Logical Operators  ! Operator  Decisions Using switch  The Loop Control Structure While Loop  for Loop  break Statement  continue Statement dowhile Loop.  
Unit3 
Teaching Hours:6 
Arrays


A Simple Program Using Array  Array Initialization  Two Dimensional Arrays Initializing a 2Dimensional Array  Memory Map of a 2Dimensional Array – Strings  Standard Library String Functions  strlen( )  strcpy( )  strcat()  strcmp()  TwoDimensional Array of Characters.  
Unit4 
Teaching Hours:6 
Functions & Pointers


Function  Passing Values between Functions  Scope Rule of Functions  Calling Convention  Return Type of Function  Call by Value and Call by Reference  An Introduction to Pointers  Pointer Notation –Recursion.  
Unit5 
Teaching Hours:6 
Macros and Structures


Introduction to macros, Structures  Declaring a Structure  Accessing Structure Elements  How Structure Elements areStored.
 
Unit6 
Teaching Hours:7 
Introduction to Computers & Number systems


Different number systems and their conversions (Decimal, Binary, Octal and Hexadecimal) Binary arithmetic  Addition, subtraction, multiplication and division of binary numbers, 1’s and 2’s complement, Floating point numbers, Coding – BCD, Gray,ASCII
 
Unit7 
Teaching Hours:6 
Boolean Algebra


Boolean operations and expressions, Laws and rules of boolean algebra, Demorgan’s Theorem, Boolean expressions, Simplification of Booleanexpression.  
Unit8 
Teaching Hours:6 
Logic Gates


OR gate, NOR gate , NOT gate , AND gate, NAND gate XOR gate , XNOR gate, The universal property of NOR and NAND gate, Karnaugh map (SOP).  
Unit9 
Teaching Hours:5 
Combinational logic


Adders (Half and Full), Decoder, Encoder, Multiplexer, DeMultiplexer (Introductory ConceptsOnly).
 
Unit10 
Teaching Hours:6 
FlipFlops


Flipflops SR flipflop, JK flipflop, Master slave JK flipflop, Introduction to Registers andCounters.
 
Text Books And Reference Books: [1] Yashavant P. Kanetkar, Let Us C, 15th Edition, BPB Publications, 2012. [2] Floyd and Thomas L, Digital Computer Fundamentals, 11th Edition, Pearson International, 2015.
 
Essential Reading / Recommended Reading [1] Byron Gottfried and Jitender Chhabra, Programming with C, 3rd Ed, Tata McGrawHill, 2010. [2] Balagurusamy E, Programming in ANSI C, 4th Edition, TataMcGrawHill, 2007. [3] Deitel H M and Deitel P J, C  How to Program, 7th Edition, PrenticeHall, 2012. [4] Susant K Rout, Cimple,C, TataMcGrawHill Publishing Company Ltd., 2016. [5] Malvino, Paul Albert, Leach, Donald P. Gautam Saha, Digital Principles And Applications, 7th Edition,TMH,2010. [6] Bartee, Thomas C, Digital Computer Fundamentals, 6th Edition, TMH,2010  
Evaluation Pattern CIA50% ESE50%  
CSC151  C PROGRAMMING LAB (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The course introduces programming approach and practical implementation of theoretical concepts in C language. It provides the ability to understand, program, evaluate the given problems. The course also develops analyzing and problem solving skills based on C language.


Learning Outcome 

CO1: Analyze and illustrate algorithm and flowchart for the given C program CO2: Implement structured C programs CO3: Trace and debug the programs written in C language

Unit1 
Teaching Hours:30 
List of programs


1. Program to implement conditional statements.
2. Program to implement the concepts of while loop.
3. Program implementing for loop concepts.
4. Program to implement 1D array concept.
5. Program based on string concepts.
6. Program to implement string library functions.
7. Program to implement 2D array concepts.
8. Program to implement functions.
9. Program demonstrating recursion functions.
10.Program to demonstrate call by value and call by reference.
 
Text Books And Reference Books: [1] Yashavant P. Kanetkar, Let Us C, 15th Edition, BPB Publications, 2012.
 
Essential Reading / Recommended Reading [1] Byron Gottfried and Jitender Chhabra, Programming with C, 3rd Ed, Tata McGrawHill, 2010. [2] Balagurusamy E, Programming in ANSI C, 4th Edition, TataMcGrawHill, 2007. [3] Deitel H M and Deitel P J, C  How to Program, 7th Edition, PrenticeHall, 2012. [4] Susant K Rout, Cimple,C, TataMcGrawHill Publishing Company Ltd., 2016.
 
Evaluation Pattern CIA  50% ESE  50%  
CSC151N  C PROGRAMMING LAB (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The course introduces programming approach and practical implementation of theoretical concepts in C language. It provides the ability to understand, program, evaluate the given problems. The course also develops analyzing and problem solving skills based on C language.


Learning Outcome 

CO1: Analyze and illustrate algorithm and flowchart for the given C program CO2: Implement structured C programs CO3: Trace and debug the programs written in C language

Unit1 
Teaching Hours:30 
List of programs


1. Program to implement conditional statements.
2. Program to implement the concepts of while loop.
3. Program implementing for loop concepts.
4. Program to implement 1D array concept.
5. Program based on string concepts.
6. Program to implement string library functions.
7. Program to implement 2D array concepts.
8. Program to implement functions.
9. Program demonstrating recursion functions.
10.Program to demonstrate call by value and call by reference.
 
Text Books And Reference Books: [1] Yashavant P. Kanetkar, Let Us C, 15th Edition, BPB Publications, 2012.
 
Essential Reading / Recommended Reading [1] Byron Gottfried and Jitender Chhabra, Programming with C, 3rd Ed, Tata McGrawHill, 2010. [2] Balagurusamy E, Programming in ANSI C, 4th Edition, TataMcGrawHill, 2007. [3] Deitel H M and Deitel P J, C  How to Program, 7th Edition, PrenticeHall, 2012. [4] Susant K Rout, Cimple,C, TataMcGrawHill Publishing Company Ltd., 2016.
 
Evaluation Pattern CIA  50% ESE  50%  
ENG121  ENGLISH  I (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

· To help improve their communication skills for larger academic purposes and vocational purposes · To enable learners to learn the contextual use of words and the generic meaning · To enable learners to listen to audio content and infer contextual meaning · To enable learners to be able to speak for various purposes and occasions using context specific language and expressions · To enable learners to develop the ability to write for various purposes using suitable and precise language. 

Learning Outcome 

· Understand how to engage with texts from various countries, historical, cultural specificities and politics
· Understand and develop the ability to reflect upon and comment on texts with various themes
· Develop an analytical and critical bent of mind to compare and analyze the various literature they read and discuss in class
· Develop the ability to communicate both orally and in writing for various purposes

Unit1 
Teaching Hours:6 
language


Common errors subjectverb agreement, punctuation, tense errors
 
Unit1 
Teaching Hours:6 
Unit 1 1. The Happy Prince By Oscar Wilde 2. Shakespeare Sonnet 18


Unit2 
Teaching Hours:6 
language


sentence fragments, dangling modifiers, faulty parallelism,  
Unit2 
Teaching Hours:6 
unit 2


1. Why We TravelPico Iyer 2. What Solo Travel Has Taught Me About the World – and Myself ShivyaNath Blogpost
 
Unit3 
Teaching Hours:6 
unit 3


1. Thinking Like a Mountain By Aldo Leopold 2. Short Text: On Cutting a Tree By Gieve Patel  
Unit3 
Teaching Hours:6 
language


Note taking  
Unit4 
Teaching Hours:6 
unit 4


1. Violence in the name of God is Violence against God By Rev Dr Tveit
2. Poem: Holy Willie's Prayer By Robert Burns  
Unit4 
Teaching Hours:6 
language


Paragraph writing  
Unit5 
Teaching Hours:6 
unit 5


1. The Story of B24 By Sir Arthur Conan Doyle 2. Short Text: Aarushi Murder case
 
Unit5 
Teaching Hours:6 
Language


Newspaper report  
Unit6 
Teaching Hours:6 
unit 6


1.Long text:My Story Nicole DeFreece
2. short text: Why You Should Never Aim for Six Packs
 
Unit6 
Teaching Hours:6 
Language


Essay writing  
Unit7 
Teaching Hours:6 
Language


Paraphrasing and interpretation skills  
Unit7 
Teaching Hours:6 
unit 7


1.Long Text: Sir Ranjth Singh Essay by SouravGanguly 2. Short text: Casey at the Bat Ernest Lawrence Thayer  
Unit8 
Teaching Hours:3 
visual text


Visual Text: Before the Flood  
Text Books And Reference Books: ENGlogue 1  
Essential Reading / Recommended Reading Addfitional material as per teacher manual will be provided by the teachers  
Evaluation Pattern CIA 1=20 CIA 2=50 CIA 3= 20 ESE= 50 marks online and 50 marks written exam  
ENG121N  ENGLISH  I (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

ENGlogue is an English language course book for the students of first year of undergraduate courses studying in Christ University. The book that covers both Semesters I and II is built around fourteen contemporary themes, with each unit including two interesting and engaging reading texts. The texts are meant to trigger not just the desired languagelearning behaviors but also to engage the students in thinking about various pertinent issues concerning the world around them. Each unit also includes teaching and tasks based on vocabulary, reading, writing and speaking. The overall objective of the book is to provide students with handson learning of language skills, equipping them not only for their immediate academic needs but also for their future professional careers.


Learning Outcome 


Unit1 
Teaching Hours:6 
Language


Common errors subjectverb agreement, punctuation, tense errors  
Unit1 
Teaching Hours:6 
Beauty


 
Unit2 
Teaching Hours:6 
Language


Sentence fragments, dangling modifiers, faulty parallelism  
Unit2 
Teaching Hours:6 
Travel


 
Unit3 
Teaching Hours:6 
Environment


 
Unit3 
Teaching Hours:6 
Language


Note taking  
Unit4 
Teaching Hours:6 
Language


Paragraph writing  
Unit4 
Teaching Hours:6 
Religion


 
Unit5 
Teaching Hours:6 
Crime


 
Unit5 
Teaching Hours:6 
Language


Newspaper report  
Unit6 
Teaching Hours:6 
Language


Essay writing  
Unit6 
Teaching Hours:6 
Health and Fitness


 
Unit7 
Teaching Hours:6 
Language


Paraphrasing and interpretation skills  
Unit7 
Teaching Hours:6 
Sports


 
Unit8 
Teaching Hours:3 
Visual Text


Before the Flood  
Text Books And Reference Books: ENGlogue 1  
Essential Reading / Recommended Reading Additional material as per teacher manual will be provided by the teachers.  
Evaluation Pattern CIA 1=20 CIA 2=50 CIA 3= 20 ESE= 50 marks online and 50 marks written exam  
FRN121  FRENCH (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

French as second language for the UG program 

Learning Outcome 

Enhancement of linguistic competencies and sharpening of written and oral communicative skills.

Unit1 
Teaching Hours:5 

Chapter 1 I Discover


Lesson 1: Good Morning, How are you?  
Unit2 
Teaching Hours:5 

Chapter 1  I discover


Lesson 2: Hello, My name is Agnes.  
Unit3 
Teaching Hours:5 

Chapter 2 Culture : Physical and Political france


Lesson 1: Who is it?  
Unit4 
Teaching Hours:5 

Chapter 2 Culture: Physical and Political France


Lesson 2: In my bag , I have......  
Unit5 
Teaching Hours:5 

Les Fables de la Fontaine


1. La cigale et la fourmis  
Unit6 
Teaching Hours:5 

Visual Text


A French Film  
Unit7 
Teaching Hours:5 

Chapter 3 Viideo Workshop: He is cute!


Lesson 1 : How is he?  
Unit8 
Teaching Hours:5 

Les Fables de la Fontaine


2. Le renard et le corbeau  
Unit9 
Teaching Hours:5 

Chapter 3 Video Workshop: He is cute


Lesson 2: Hello?  
Text Books And Reference Books: 1. Cocton, MarieNoelle. Génération A1. Paris : Didier, 2016 2. De Lafontaine, Jean. Les Fables de la Fontaine. Paris, 1668
 
Essential Reading / Recommended Reading 1. Thakker, Viral. Plaisir d’écrire. New Delhi : Langers International Pvt. Ltd., 2011 2. French websites like Bonjour de France, Fluent U French, Learn French Lab, Point du FLE etc.  
Evaluation Pattern
 
MAT131  DIFFERENTIAL CALCULUS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

Course Description: This course aims at enabling the students to know various concepts and principles of differential calculus and its applications. Sound knowledge of calculus is essential for the students of mathematics for the better perceptions of the subject and its development. Course objectives: This course will help the learner to COBJ1. Gain familiarity with the concepts of limit, continuity and differentiability. COBJ2. Understand the relationship between the concepts of differentiability and continuity. COBJ3. Analyse and interpret the different versions of mean value theorems. COBJ4. Learn successive differentiation and nth derivative of product of two functions. COBJ5. Find derivative of functions of more than one variable. COBJ6. Be familiar with curve tracing. 

Learning Outcome 

On successful completion of the course, the students should be able to CO1. Compute limits, derivatives and examine the continuity, differentiability of a function at a point. 
Unit1 
Teaching Hours:20 

Limits, Continuity, Differentiability and Mean Value Theorems


Definition of the limit of a function (εδ) form – Continuity, Uniform Continuity – Types of discontinuities – Properties of continuous functions on a closed interval  Boundedness theorem and extreme value theorem – Differentiability – Mean Value Theorems: Rolle’s theorem – Lagrange’s and Cauchy’s First Mean Value Theorems – Taylor’s theorem (Lagrange’s form and Cauchy’s forms of remainder) – Maclaurin’s theorem and expansions Indeterminate forms. .  
Unit2 
Teaching Hours:20 

Successive and Partial Differentiation


Successive differentiation – nth derivatives of functions – Leibnitz theorem and its applications – Partial differentiation – First and higher order derivatives – Differentiation of homogeneous functions – Euler’s theorem – Taylor’s theorem for two variables (only statements and problems) Maxima and Minima of functions of two variables.  
Unit3 
Teaching Hours:20 

Curve Tracing


Tangents and Normals, Concavity and convexity, Curvature, Asymptotes, Singular points, Tracing of curves (Parametric representation of curves and tracing of parametric curves, Polar coordinates and tracing of curves in polar coordinates)..  
Text Books And Reference Books: G.B. Thomas, M.D.Weir and J. Hass, ThomasCalculus, 12th ed., Pearson Education India, 2015.  
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
MAT131N  DIFFERENTIAL CALCULUS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

This course aims at enabling the students to know various concepts and principles of differential calculus and its applications. Sound knowledge of calculus is essential for the students of mathematics for the better perceptions of the subject and its development.


Learning Outcome 

CO1. Compute limits, derivatives and examine the continuity, differentiability of a function at a point. CO2. Understand the properties of continuous functions and prove that differentiability implies continuity CO3. Prove Mean value theorems and analyse its geometric interpretation. CO4. Compute derivatives of any order and apply Leibniz’ theorem to find nth derivative of product of two functions. CO5. Master the fundamental concepts of partial differentiation and apply Euler’s theorem for homogeneous functions. CO6. Gain knowledge on the concepts such as asymptotes, concavity/convexity and singular points and apply the same for curve tracing. 
Unit1 
Teaching Hours:20 

Limits, Continuity, Differentiability and Mean Value Theorems


Definition of the limit of a function (εδ) form – Continuity, Uniform Continuity – Types of discontinuities – Properties of continuous functions on a closed interval – Differentiability – Mean Value Theorems: Rolle’s theorem – Lagrange’s and Cauchy’s First Mean Value Theorems – Taylor’s theorem (Lagrange’s form and Cauchy’s forms of remainder) – Maclaurin’s theorem and expansions Indeterminate forms.  Maxima and Minima.  
Unit2 
Teaching Hours:20 

Successive and Partial Differentiation


Successive differentiation – nth derivatives of functions – Leibnitz theorem and its applications – Partial differentiation – First and higher order derivatives – Differentiation of homogeneous functions – Euler’s theorem – Taylor’s theorem for two variables (only statements and problems) Maxima and Minima of functions of two variables.  
Unit3 
Teaching Hours:20 

Curve Tracing


Tangents and Normals, Curvature, Asymptotes, Singular points, Tracing of curves (Parametric representation of curves and tracing of parametric curves, Polar coordinates and tracing of curves in polar coordinates).  
Text Books And Reference Books: G.B. Thomas, M.D.Weir and J. Hass, ThomasCalculus, 12th ed., Pearson Education India, 2015.
 
Essential Reading / Recommended Reading H. Anton, I. Birens and S. Davis, Calculus, John Wiley and Sons Inc., 2002. F. Ayres and E. Mendelson, Schaum's Outline of Calculus, 6th ed. USA: Mc. Graw Hill., 2013. J. Stewart, Single Variable Essential Calculus: Early Transcendentals, 2nd ed.: Belmont, USA: Brooks/Cole Cengage Learning., 2013. S. Narayanan & T. K. M. Pillay, Calculus, Reprint, India: S. Viswanathan Pvt. Ltd., 2009. (vol. I & II.) M. Spivak, Calculus, 3rd ed., Cambridge University Press, 2006. T.M. Apostol, Calculus, VolII, Wiley India Pvt. Ltd., 2011. J. Edwards, An elementary treatise on the differential calculus: with applications and numerous examples, Reprint, Charleston, USA: BiblioBazaar, 2010. N. P. Bali, Differential Calculus, New ed. New Delhi, India: Laxmi Publications (P) Ltd., 2012.  
Evaluation Pattern
 
MAT151  DIFFERENTIAL CALCULUS USING MAXIMA (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

Course Description: The course Differential Calculus Using wxMaxima is aimed at enabling the students to appreciate and understand core concepts of Differential Calculus with the help of the free and open source mathematical software Maxima. It is designed to gain hands on experience in using MAXIMA to perform plotting of standard curves, to find limits of a function, illustrate differentiability and solve applied problems on differentiation. Course objectives: This course will help the learner to COBJ1. Acquire skill in solving problems on Differential Calculus using MAXIMA. 

Learning Outcome 

On successful completion of the course, the students should be able to CO1. Acquire proficiency in using MAXIMA to study Differential Calculus. 
Unit1 
Teaching Hours:30 

Proposed Topics


 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading Sandeep Koranne, Handbook of Open Source Tools, Springer Science & Business Media, 2010.  
Evaluation Pattern The course is evaluated based on continuous internal assessments (CIA) and the lab erecord. The parameters for evaluation under each component and the mode of assessment are given below.
 
MAT151N  DIFFERENTIAL CALCULUS USING MAXIMA (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

The course Differential Calculus Using Maxima is aimed at enabling the students to appreciate and understand core concepts of Differential Calculus with the help of the free and open source mathematical software Maxima. It is designed to gain hands on experience in using MAXIMA to perform plotting of standard curves, to find limits of a function, illustrate differentiability and solve applied problems on differentiation. 

Learning Outcome 

CO1. Acquire proficiency in using MAXIMA to study Differential Calculus. CO2. Demonstrate the use of MAXIMA to understand and interpret the core concepts various types of functions from the algebraic and graphical points of view. CO3. Use MAXIMA to evaluate limits of functions and check for continuity graphically as well as algebraically. CO4. Be familiar with the builtin functions to find derivatives of any order and solve application problems dealing with the concept of rate of change. CO5. Sketch graphs of standard curves using MAXIMA to interpret tracing of curves. 
Unit1 
Teaching Hours:30 

Proposed Topics


1. Introduction to MAXIMA 2. Sketch the graph of various functions: explicitimplicitparametricpolar. 3. Evaluation of limits using builtin function in maxima and illustration of the same graphically. 4. Demonstration of continuous functions and types of discontinuities. 5. Determination of derivatives.  graphical interpretation of derivatives. 6. Verification of mean value theorems. 7. Evaluation of extreme points, maxima and minima. 8. Calculation of nth derivatives of functions 9. Partial differentiation of functions of two variables. 10. Tracing of curves. 11. Applications of differentiation  
Text Books And Reference Books: Zachary Hannan, wxMaxima for Calculus I (Creative Commons AttributionNonCommercialShare Alike 4.0 International), Solano Community College, Edition 1.0 Publisher, Published June 17, 2015. Zachary Hannan, wxMaxima for Calculus II (Creative Commons AttributionNonCommercialShare Alike 4.0 International), Solano Community College, Edition 1.0 Publisher, Published June 17, 2015.  
Essential Reading / Recommended Reading Sandeep Koranne, Handbook of Open Source Tools, Springer Science & Business Media, 2010.  
Evaluation Pattern
 
STA131  DESCRIPTIVE STATISTICS AND PROBABILITY THEORY (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

This course is designed to introduce the historical development of statistics, presentation of data, descriptive measures and fitting mathematical curves for the data.


Learning Outcome 

CO1:Demonstrate the history of statistics and present the data in various forms. CO2:Infer the concept of correlation and regression for relating two or more related variables. CO3:Demonstrate the probabilities for various events.

Unit1 
Teaching Hours:10 

Organization and presentation of data


 
Unit2 
Teaching Hours:15 

Descriptive Statistics


Measures of location or central tendency: Arthimetic mean, Median, Mode, Geometric mean,  
Unit3 
Teaching Hours:10 

Correlation


Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only). Regression: Concept of errors, Principles of Least Square, Simple linear regression and its properties.  
Unit4 
Teaching Hours:15 

Basics of probability


Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications.  
Unit5 
Teaching Hours:10 

Association of attributes


Relation between class frequencies, consistency of data, independence of attributes, criterion of independence, association of attributes: Yule’s coefficient of association, Yule’coefficient of colligation.  
Text Books And Reference Books: 1. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3 rd edition, John  
Essential Reading / Recommended Reading 1. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015.  
Evaluation Pattern
 
STA131N  DESCRIPTIVE STATISTICS AND PROBABILITY (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

This course is designed to introduce the historical development of statistics, presentation of data, descriptive measures and fitting mathematical curves for the data. This course also introduces measurement of relationship of quantitative and qualitative data and the concept of probability. 

Learning Outcome 

CO1:To enable the students understand and apply the descriptive measures and probability for data analysis. CO2: Implement theoritical concepts of descriptive measures and probability 
Unit1 
Teaching Hours:10 
Organization and presentation of data


Origin and development of Statistics, Scope, limitation and misuse of statistics. Types of data: primary, secondary, quantitative and qualitative data. Types of Measurements: nominal, ordinal, discrete and continuous data. Presentation of data by tables: construction of frequency distributions for discrete and continuous data, graphical representation of a frequency distribution by histogram and frequency polygon, cumulative frequency distributions (inclusive and exclusive methods).  
Unit2 
Teaching Hours:15 
Descriptive Statistics


Measures of location or central tendency: Arthimetic mean, Median, Mode, Geometric mean, Harmonic mean. Partition values: Quartiles, Deciles and percentiles. Measures of dispersion: Mean deviation, Quartile deviation, Standard deviation, Coefficient of variation. Moments: measures of skewness, Kurtosis  
Unit3 
Teaching Hours:10 
Correlation


Correlation: Scatter plot, Karl Pearson coefficient of correlation, Spearman's rank correlation coefficient, multiple and partial correlations (for 3 variates only).  
Unit4 
Teaching Hours:15 
Basics of probability


Random experiment, sample point and sample space, event, algebra of events. Definition of Probability: classical, empirical and axiomatic approaches to probability, properties of probability. Theorems on probability, conditional probability and independent events, Laws of total probability, Baye’s theorem and its applications.  
Unit5 
Teaching Hours:10 
Association of attributes


Relation between class frequencies, consistency of data, independence of attributes, criterion of independence, association of attributes: Yule’s coefficient of association, Yule’coefficient of colligation.  
Text Books And Reference Books: 1. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3 rd edition, John Wiley & Sons Inc., New Jersey, 2015. 2. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11 th edition, Sultan Chand & Sons, New Delhi, 2014.  
Essential Reading / Recommended Reading 1. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. 2. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017. 3. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013. 4. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.  
Evaluation Pattern Component Marks CIA I 10 Mid Semester Examination (CIA II) 25 CIA III 10 Attendance 05 End Semester Exam 50 Total 100  
STA151  DESCRIPTIVE STATISTICS AND PROBABILITY PRACTICAL (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The course is designed to provide a practical exposure to the students in Basic concepts of Excel and different way of representation of data. 

Learning Outcome 

CO1:Understand data and analysis CO2: Implement EXCEL in given data set CO3: Create a research statement and collect data related to the statement along with the representation of data and 
Unit1 
Teaching Hours:30 

List of practical assignments


1. 1. Questionnaire preparation, data collection and data base creation using Excel sheet 2. Basic data manipulation techniques: sorting, filtering, conditional formatting 3. Pivot Table construction 4. Diagrammatic representation 5. Graphical representation 6. Descriptive statistics using statistical functions 7. Data Analysis Pack (DAP) 8. Correlation and Correlation matrix 9. Exercise on partial and multiple correlation coefficient. 10. Regression analysis and their significance 11. Linear Curve estimation 12. Second order Polynomial Curve estimation  
Text Books And Reference Books:
1. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3^{rd} edition, John Wiley & Sons Inc., New Jersey, 2015. 2. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11^{th} edition, Sultan Chand & Sons, New Delhi, 2014.  
Essential Reading / Recommended Reading
1. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. 2. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017. 3. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013. 4. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.  
Evaluation Pattern
 
STA151N  DESCRIPTIVE STATISTICS AND PROBABILITY PRACTICAL (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

The course is designed to provide a practical exposure to the students in Basic concepts of Excel and different way of representation of data. 

Learning Outcome 

CO1:Understand data and analysis CO2: Implement EXCEL in given data set CO3: Create a research statement and collect data related to the statement along with the representation of data and practical exposure on DAP. 
Unit1 
Teaching Hours:30 

List of practical assignments


1. Questionnaire preparation, data collection and data base creation using Excel sheet 2. Basic data manipulation techniques: sorting, filtering, conditional formatting 3. Pivot Table construction 4. Diagrammatic representation 5. Graphical representation 6. Descriptive statistics using statistical functions and Data Analysis Pack (DAP) 7. Exercise on correlation and Correlation matrix 8. Exercise on partial and multiple correlation coefficients.  
Text Books And Reference Books: 1. Rohatgi V.K and Saleh E, An Introduction to Probability and Statistics, 3rd edition, John Wiley & Sons Inc., New Jersey, 2015. 2. Gupta S.C and Kapoor V.K, Fundamentals of Mathematical Statistics, 11th edition, Sultan Chand & Sons, New Delhi, 2014.  
Essential Reading / Recommended Reading 1. Mukhopadhyay P, Mathematical Statistics, Books and Allied (P) Ltd, Kolkata, 2015. 2. Walpole R.E, Myers R.H, and Myers S.L, Probability and Statistics for Engineers and Scientists, Pearson, New Delhi, 2017. 3. Montgomery D.C and Runger G.C, Applied Statistics and Probability for Engineers, Wiley India, New Delhi, 2013. 4. Mood A.M, Graybill F.A and Boes D.C, Introduction to the Theory of Statistics, McGraw Hill, New Delhi, 2008.  
Evaluation Pattern
 
CSC231  DATA STRUCTURES AND OPERATING SYSTEMS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

The course provides knowledge on the data storage techniques, accessing techniques, the various operations applied on the data and fundamental knowledge of operating system architecture and the various operations performed by the Operating system. This course helps the students to attain comprehensive understanding of programming and to acquire the knowledge on the different tasks like job scheduling, memory management, file handling done by operatingsystems. 

Learning Outcome 

CO1: Understand the different Data Structures using C and the fundamental principles of operating system and system structure. CO2: To implement the different operations on the data structures and to evaluate the process scheduling, deadlock system and effective memory management CO3: To analyse the applications of data structures in real time applications CO4: To analyse the file structure, directory structure and allocation methods. 
Unit1 
Teaching Hours:6 
Arrays


Introduction to data structures Arrays Introduction, Array Operations, linear search – Binary search – insertion in an array– deletion in an array – sort – Bubble Sort  Insertion Sort  SelectionSort.  
Unit2 
Teaching Hours:6 
Linked List


Introduction –Insertion – Deletion – Search  Double Linked List Representations.  
Unit3 
Teaching Hours:6 
Stack & Queue


Introduction  Stack Operations using arrays and linked lists  Infix to Prefix  Queue Operations using array and linkedlist.  
Unit4 
Teaching Hours:6 
Binary Trees


Introduction  Binary Trees Properties of Binary Trees  Binary Tree Representations  Binary TreeTraversals.  
Unit5 
Teaching Hours:6 
Graphs


Introduction – Definitions and terminology – graph representations – Depth first search – Breadth first search  
Unit6 
Teaching Hours:6 
Introduction and System Structures


Operating system definition, computer system organization, architecture, structure and operations, process, memory and storage management.
 
Unit7 
Teaching Hours:6 
Process Management


Process concepts, scheduling, operations on processes. Process Scheduling: Basic concepts, scheduling criteria, scheduling algorithms, Synchronization: Background, critical section problems.  
Unit8 
Teaching Hours:6 
Deadlock


Deadlock System model, deadlock characterization, methods for handling deadlock, deadlock prevention, avoidance and detection.  
Unit9 
Teaching Hours:6 
Memory Management


Memory Management Strategies: Background, swapping, Memory allocation, Paging, Structure of the pagetable.  
Unit10 
Teaching Hours:6 
File system


File system structure, directory structure, allocation methods and freespace management. Self Learning : Segmentation, File system structure.  
Text Books And Reference Books: [1] Silberschatz, P.B. Galvin and G. Gagne, Operating System Concepts, 9th Edition, New Delhi, Wiley India,2012.  
Essential Reading / Recommended Reading [1] William Stallings, Operating system Internals and Design Principles, 7th Edition, Prentice Hall,2017. [2] Andrew S. Tanenbaum and Herbert Bos, Modern Operating Systems, 4th Edition, Pearson,2014. [3] H.M. Deitel, P. J. Deitel, D. R. Choffnes, Operating Systems, 3rd Edition, Pearson, 2007.  
Evaluation Pattern 50% ESE + 50% CIA  
CSC251  DATA STRUCTURES LAB (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:2 
Max Marks:50 
Credits:2 
Course Objectives/Course Description 

The course introduces programming approach and practical implementation of data structure concepts. The course aims to familiarize with practical and real time application of linear and Nonlinear data structure. It provides the ability to identify, apply and evaluate relevant data structure concept for the given problems. 

Learning Outcome 

Upon completion of the course students will be able to: CO1: Understand the need for Data Structures when building application CO2: To write diversified solutions for given problem CO3: Improve logical, analytical, problem solving skill using Cprogramming

Unit1 
Teaching Hours:30 
List of lab Programs


1. Inserting an element into one dimensionalarray 2. Deletion of an element from one dimensionalarray 3. Implementation of insertionsort. 4. Implementation of selectionsort. 5. Implementation of BinarySearch. 6. Implementation of Linear Search in a linkedlist 7. Creation of a linked list and inserting nodes intoit. 8. Deletion from a linkedlist. 9. Implementation of different operations on astack. 10. Implementationofdifferentoperationsonaqueue  
Text Books And Reference Books: [1] Silberschatz, P.B. Galvin and G. Gagne, Operating System Concepts, 9th Edition, New Delhi, Wiley India,2012.  
Essential Reading / Recommended Reading [1] William Stallings, Operating system Internals and Design Principles, 7th Edition, Prentice Hall,2017. [2] Andrew S. Tanenbaum and Herbert Bos, Modern Operating Systems, 4th Edition, Pearson,2014. [3] H.M. Deitel, P. J. Deitel, D. R. Choffnes, Operating Systems, 3rd Edition, Pearson, 2007.  
Evaluation Pattern 50% CIA + 50% Three Tests  
ENG221  ENGLISH  II (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:2 
Course Objectives/Course Description 

· To help improve their communication skills for larger academic purposes and vocational purposes · To enable learners to learn the contextual use of words and the generic meaning · To enable learners to listen to audio content and infer contextual meaning · To enable learners to be able to speak for various purposes and occasions using context specific language and expressions · To enable learners to develop the ability to write for various purposes using suitable and precise language. 

Learning Outcome 

· Understand how to engage with texts from various countries, historical, cultural specificities and politics · Understand and develop the ability to reflect upon and comment on texts with various themes · Develop an analytical and critical bent of mind to compare and analyze the various literature they read and discuss in class · Develop the ability to communicate both orally and in writing for various purposes 
Unit1 
Teaching Hours:6 
food


Unit1 
Teaching Hours:6 
language


Presentation skills  
Unit2 
Teaching Hours:6 
Fashion


1.Long text: In the Height of FashionHenry Lawson
2. short text: Crazy for Fashion BabatundeAremu  
Unit2 
Teaching Hours:6 
Language


Report writing  
Unit3 
Teaching Hours:6 
Language


Group Discussion  
Unit3 
Teaching Hours:6 
Architecture


1. long text: Bharat Bhavan By Charles Correa 2. Short text: The Plain Sense of Things By Wallace Stevens
 
Unit4 
Teaching Hours:6 
Management


1.Long Text: The Amazing Dabbawalas of Mumbai ShivaniPandita
2. Short Text: If By Rudyard Kupling  
Unit4 
Teaching Hours:6 
Language


Interview skills and CV writing  
Unit5 
Teaching Hours:6 
History


1. Long tet: Whose Ambedkar is he anyway? By KanchaIlaiah
2. Short text: Dhauli By JayantaMahapatra  
Unit5 
Teaching Hours:6 
language


Developing arguments debating  
Unit6 
Teaching Hours:6 
language


Letter writing and email writing  
Unit6 
Teaching Hours:6 
War


1. Long text: An Occurrence at Owl Creek Bridge By Ambrose Bierce 2. Short text: Strange meeting By Wilfred Owen  
Unit7 
Teaching Hours:6 
language


Ethics of writing on social media platforms  
Unit7 
Teaching Hours:6 
Social Media


1.Long text: Facebook and the Epiphanator: An End to Endings? By Paul Ford 2. Short text: 'Truth in the time of Social Media' by Girish Balachandran  
Unit8 
Teaching Hours:3 
visual text


BBC Documentary Dabbawalas  
Text Books And Reference Books: ENGlogue 1  
Essential Reading / Recommended Reading teacher manual and worksheets that teachers would provide. Listening skills worksheets.  
Evaluation Pattern CIA1 20 MSE50 CIA3 20 ESE 50 online and 50 written  
FRN221  FRENCH (2020 Batch)  
Total Teaching Hours for Semester:45 
No of Lecture Hours/Week:3 
Max Marks:100 
Credits:3 
Course Objectives/Course Description 

French as second language for the UG program 

Learning Outcome 

Enhancement of linguistic competencies and sharpening of written and oral communicative skills. 
Unit1 
Teaching Hours:5 

Chapter 4 Culture: A country of Vacations


Lesson 1: Hobbies  
Unit2 
Teaching Hours:5 

Chapter 4 Culture: A country of Vacations


Lesson 2: The routine  
Unit3 
Teaching Hours:5 

Poem


1. Demain dès l'aube  Victor Hugo  
Unit4 
Teaching Hours:5 

Chapter 5  I discover


Lesson 1 : Where to shop?  
Unit5 
Teaching Hours:5 

Chapter 5: I discover


Lesson 2: Discover and Taste  
Unit6 
Teaching Hours:5 

Visual Text


A French Film  
Unit7 
Teaching Hours:5 

Chapter 6 Culture: Gourmet Countries


Lesson 1: Everyone is having fun  
Unit8 
Teaching Hours:5 

Poem


2. Le Lac  Alphonse de Lamartine  
Unit9 
Teaching Hours:5 

Chapter 6 Culture: Gourmet countries


Lesson 2: Daily routine of Teenagers  
Text Books And Reference Books: 1. Cocton, MarieNoelle. Génération A1. Paris : Didier, 2016 2. Poèmes : Demain dès l'aube par Victor Hugo & Le Lac par Alphonse de Lamartine (contenu rédigé sur ligne)  
Essential Reading / Recommended Reading 1. Thakker, Viral. Plaisir d’écrire. New Delhi : Langers International Pvt. Ltd., 2011 2. French websites like Bonjour de France, Fluent U French, Learn French Lab, Point du FLE etc.  
Evaluation Pattern
 
MAT231  DIFFERENTIAL EQUATIONS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

Course Description: This course aims at introducing the students to the theory of ordinary and partial differential equations through various methods of solutions. Course objectives: This course will help the learner to COBJ1. Solve first order ODE. COBJ1. Solve higher order ODE with constant coefficients. COBJ1. Solve second order linear differential equations with variable coefficients. COBJ1. Form PDE and solve linear and non linear PDE’s of first order. 

Learning Outcome 

On successful completion of the course, the students should be able to CO1. Understand the concepts of order, degree and linearity of ODE and recognize ODEs and PDEs. CO2. Apply multiple approaches/appropriate techniques to solve first order ODEs. CO3. Solve second order linear differential equations by finding Complementary function and particular integrals. CO4. Solve second order linear differential equations with variable coefficients by different methods such as if part of the integral is known, exactness and method of variation of parameter. CO5. Formulation of PDE by eliminating arbitrary constants and functions, solve linear PDEs using Lagrange’s auxiliary equation and solve nonlinear PDE’s of first order by Charpit’s method. 
Unit1 
Teaching Hours:20 

First Order ODE's


Solution of ordinary differential equations of first order and first degree – Variable separable and reducible to variable separable forms – Homogeneous and reducible to homogeneous forms – linear differential equations and reducible to linear differential equations – First order exact differential equations Integrating factors, rules to find an integrating factor – Clairauts equation – Orthogonal trajectory.  
Unit2 
Teaching Hours:20 

Explicit methods of solving higher order linear differential equations


Linear homogeneous equations with constant coefficients, Linear nonhomogenous equations, The CauchyEuler equation, Simultaneous differential equations with constant coefficients. Second order linear differential equations with variable coefficients by the following methods: (i) when a part of complementary functions is given, (ii) reducing to normal form, (iii) change of independent variable (iv) variation of parameters and (v) by finding the first integral (exact equation), equations of the form (dx/P)=(dy/Q)=(dz/R).  
Unit3 
Teaching Hours:20 

Partial differential equations


Order and degree of partial differential equations, Formation of first order partial differential equations, Linear partial differential equation of first order, Lagrange’s method, Charpit’s method. Classification of second order partial differential equations into elliptic, parabolic and hyperbolic through illustrations only.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
MAT251  DIFFERENTIAL EQUATIONS USING MAXIMA (2020 Batch)  
Total Teaching Hours for Semester:30 
No of Lecture Hours/Week:2 

Max Marks:50 
Credits:2 

Course Objectives/Course Description 

Course Description:This course aims at introducing the students to an open source software MAXIMA and make students proficient in using Maxima for solving first and second order ODEs, study the nature of solution by plotting the general/particular solutions. Course objectives: This course will help the learner to COBJ1. Acquire skill in solving problems on Differential Equations using MAXIMA. COBJ2. Gain proficiency in using MAXIMA to solve problems on Differential Equations and its applications. 

Learning Outcome 

On successful completion of the course, the students should be able to CO1. Acquire proficiency in using Maxima to study Differential Equations. CO2. Demonstrate the use of Maxima to understand and interpret the core concepts in Differential Equations. CO3. Find general and particular solutions of first and second order Differential Equations and to sketch the graph for solutions. CO4. Apply MAXIMA to learn applications of Differential Equations in real world such as population, radioactive decay and Newton’s law of cooling. 
Unit1 
Teaching Hours:30 

Proposed Topics


 
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern The course is evaluated based on continuous internal assessments (CIA) and the lab erecord. The parameters for evaluation under each component and the mode of assessment are given below.
 
STA231  STATISTICAL METHODS (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

This course is designed to teach the basic concepts of random variables and its generation functions. It also gives a brief idea about standard probability distributions and how they are applied in real time situations. 

Learning Outcome 

1. :1. Demonstrate the random variables and its functions 2. Infer the expectations for random variable functions and generating functions. 3. Demonstrate various discrete and continuous distributions and their usage 
Unit1 
Teaching Hours:10 

Random Variables


Definition, Discrete and continuous random variables, Probability Mass function and Probability density function, Distribution function and its properties. Two dimension random variables: Discrete and continuous type, Joint Density function, Marginal and conditional Probability Mass function and Probability Density function, independence of variables with illustration.  
Unit2 
Teaching Hours:10 

Mathematical Expectation and Generating functions


Expectation of single and bivariate random variables and its properties. Moments and Cumulants, moment generating function, cumulant generating function and characteristic function. Uniqueness and inversion theorems (without proof) along with applications, Conditional expectations.  
Unit3 
Teaching Hours:15 

Discrete Probability distributions


Discrete distributions: Binomial, Poisson, geometric, negative binomial, Hypergeometric distributions along with their properties, limiting/approximation cases and applications.
 
Unit4 
Teaching Hours:15 

Continuous Probability distributions


Continuous distributions: Uniform, normal, exponential, Cauchy, beta and gamma distributions along with their properties, limiting/approximation cases and applications.  
Unit5 
Teaching Hours:10 

Limiting Theorems


Chebyshev’s inequality, Week Law of Large numbers, Strong Law of Large numbers and their applications, Central Limit Theorem for i.i.d variates and its application, DeMoivre Laplace theorem.  
Text Books And Reference Books:
 
Essential Reading / Recommended Reading
 
Evaluation Pattern
 
STA232  R PROGRAMMING (2020 Batch)  
Total Teaching Hours for Semester:60 
No of Lecture Hours/Week:4 

Max Marks:100 
Credits:4 

Course Objectives/Course Description 

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

Learning Outcome 

CO1: Handle data using statistical tool CO2: Perform graphical representation of data using R CO3: Use R for an introductory statistics. 
Unit1 
Teaching Hours:12 
Introduction


Introduction and preliminariesThe R environment, R and statistics, R commands, Data permanency and removing objects, Simple manipulations, Numbers and Vectors, Objects modes and attributes, Ordered and unordered Factors, Arrays and Matrices  
Unit2 
Teaching Hours:12 
Lists and Data Frames


Constructing and modifying lists, Making Data frames, attach( ) and detach( ), Working with data frame, Reading data from files using read.table( ), scan( ), Grouping, Conditional execution: if statements, Repetitive execution: for loops, repeat and while loops, Functions.  
Unit3 
Teaching Hours:12 
Data Exploration for Univariate and Bivariate Data


Univariate Data  Handling categorical data and numerical data using R, Bivariate Data Handling bivariate categorical data using R, Categorical vs. Numerical, Numerical vs. Numerical  
Unit4 
Teaching Hours:12 
Data Exploration for Multivariate Data


Multivariate Data Storing multivariate data in R data frames, Accessing and manipulating data in R data frames, view multivariate data, apply( ) family functions  apply( ), sapply( ), lapply( ), tapply( ), dplyr package select( ), filter( ), arrange( ), rename( ), mutate( ), group_by( ), %>%, summarize( ).  
Unit5 
Teaching Hours:12 
Correlation

