0 - Academics - IIT Madras Degree Program

NOTIFY ME Home Academics Overall Structure

There are six levels in the IIT Madras Degree program and to get the BS Degree in Data Science and Applications from IIT Madras, a learner has to successfully complete the first four levels.

There is also the flexibility to exit at any level. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education) or Diploma(s) from IIT Madras or BSc Degree in Programming and Data Science from IIT Madras or BS Degree in Data Science and Applications from IIT Madras.

Those who are interested in pursuing an exclusive Diploma Program in Programming or Data Science can also check out our Diploma Program website.

Courses and Credits in Each Level:

Foundation Level: 32 credits | 8 courses Diploma Level:

Programming: 27 credits | 6 courses + 2 projects
Data Science: 27 credits | 6 courses + 2 projects BSc Degree Level: 28 credits BS Degree Level: 28 credits PG Diploma Level: 20 credits | 3 core + 2 electives MTech Level: 20 credits | MTech Project

Total credits to be earned to get:

BSc Degree: 114 credits BS Degree: 142 credits PG Diploma in AI & ML: 162 credits (BS + PG Diploma) MTech in AI & ML: 182 credits (BS + PG Diploma + MTech)

Completion time: upto 8 years

The time period for this is based on learner’s preferred pace and performance in assessments. Expected learner engagement will be approximately 10hrs/course/week. Foundation Level: 1-3 years Diploma Level: 1-2 years each BSc/BS Degree Level: 1-2 years each PG Diploma: 1-2 years MTech: up to 8 years from starting of the Foundation level

Fees: Each term, pay only for courses you register for!

Refer Fee Structure.

Online Courses & Assignments

Duration of each course: 12 weeks - Each week comprising 2-3 hrs of videos, practice questions, text transcripts and online graded assignment(s).

Quizzes and Exams

In-person invigilated quizzes and exams as per the grading pattern defined for each course.

Term Structure

Every year is divided into three terms of four months each - January Term, May Term and September Term.

Each term of four months has 12 weeks of coursework (video lectures and assignments), 2 in-person invigilated Quizzes and End Term Exams. Depending on the course, assessments may include programming exams, mini projects, vivas, take home assignments, etc.

Course Registrations

In each term, a learner may register for upto 4 courses depending on their CCC (Credit Clearing Capability).

A learner’s CCC in the Foundation Level is calculated based on their performance in the Qualifier Exam or the previous term’s End Term Exams. The CCC in the Diploma Level and thereafter is 4.

Level Progression Requirements: • Foundation Level: All 8 courses must be successfully completed before enrolling in any Diploma Level course. • Diploma Level: All courses and projects must be successfully completed before enrolling in any Degree Level course. • BS Degree Level: All courses must be successfully completed before enrolling in PG Diploma Level. • PG Diploma Level: All courses must be successfully completed before enrolling in MTech Level.

Assessments

There are 3 types of assessments for each course: Weekly Assignments which are online monthly in-person Quizzes in-person End Term Exam View More Details

In addition, assessments may include programming exams, mini projects, vivas, take home assignments, etc.

Exam Cities

The Invigilated Quizzes and End Term exams are conducted in a number of cities spread across India. The map shows our current Exam Cities List. View List

Students residing/physically present in India on exam day

All students residing in India or physically present in India on the day of an in-centre exam must write exams at one of the exam centres in india.

Learners based outside India

We also conduct in-person exams in Bahrain, Kuwait, Oman and UAE.

Learners based out of other countries will be allowed to take up remote proctored exams. On exam day, students writing such internet based exams will be asked to pin the location exam is being taken from.

If any overseas students are planning to be in India on exam day, it is the student’s responsibility to notify us ahead of time so that we can arrange for you to write the exam(s) in one of the exam centres in india; hall tickets will also be issued suitably. If any of these norms are violated, it will be considered as malpractice. Exam results may be withheld pending investigation and findings of the exam committee.

Note: Additional Exam Fee applies for all learners opting to write exams outside India.

If you reside outside India and cannot find a centre in your city / country, please write to ge@study.iitm.ac.in for assistance.

Fee Structure

For details about application fees, check Application Process in Admissions page.

Each term, pay only for the courses you register for in that specific term.

Goal Total Credits Total Fees INR Foundation Only 32 ₹32,000 Foundation + One Diploma 59 ₹94,500 Foundation + Two Diplomas 86 ₹1,57,000 BSc Degree 114 ₹2,21,000 - ₹2,27,000 BS Degree 142 ₹3,15,000 - ₹3,51,000 PG Diploma in AI & ML 162 (BS + PG Diploma) ₹4,15,000 - ₹4,91,000 MTech in AI & ML 182 (BS + PG Diploma + MTech) ₹6,15,000 - ₹6,91,000

The IITM BS program strives to secure scholarships from CSR and Alumni donations for its students from socially and economically disadvantaged backgrounds to cover the full program fees.

As intermittent support till such donations are secured, IIT Madras covers part of the fees for the BS program students from socially and economically disadvantaged backgrounds.

The fraction of IIT Madras’ fee support depends on the learner’s category and family income and is given below:

Family Income > 5 LPA Family Income > 1 LPA and ⇐ 5 LPA Family Income ⇐ 1 LPA Fee Support from IIT Madras Docs Required Fee Support from IIT Madras Docs Required Fee Support from IIT Madras Docs Required General N/A NIL 50% EWS + Family Income 75% EWS + Family Income OBC N/A NIL 50% OBC-NCL + Family Income 75% OBC-NCL + Family Income SC / ST 50% SC / ST 50% SC / ST 75% SC / ST + Family Income PwD 50% PwD 50% PwD 75% PwD + EWS / OBC-NCL + Family Income SC / ST + PwD 75% SC / ST + PwD 75% SC / ST + PwD 75% SC / ST + PwD *IITM contribution does not apply to International students.

The term family income for the purpose of availing IITM fee support includes the income of the candidate, the income of his/her parents and spouse, also the income of his/her siblings and children below the age of 18 years. Family income certificate is not required while applying for the Degree program, but will be required to avail IITM fee support when joining the program. Download Family Income Certificate format

OBC-NCL / EWS certificate, if applicable, need to be obtained in following format while applying: Download OBC-NCL Certificate format Download EWS Certificate format

Note: If a learner does not pass a course in the term they registered for, they will need to repeat the entire course in a later term with re-payment of full course fee. If a learner completed all course requirements, but couldn’t attend the end term exam alone, they can choose to repeat just the end term exam in the next term with the payment of an end term exam fee (₹1000 for foundation level courses; ₹2000 for diploma / degree level courses).

Foundation Level

The Foundation Level comprises courses in Mathematics, Statistics, Basics of Programming and Python, and English. These courses have been chosen to ensure that the learner who passes these successfully is well prepared to proceed to the Diploma Level courses.

Requirements for registration

The learner should apply for and clear the Qualifier Process.

Options on successful completion

Learners have the following two options when they successfully complete all 8 Foundation Level courses:

Exit: The learner may exit with a Foundation Certificate from Centre for Outreach and Digital Education (CODE), IIT Madras. Proceed to next level: The learner can join the Diploma Level.

8 courses

32 credits

1 - 3 years

10 hrs/course/week

₹32,000*

*Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites Mathematics for Data Science I 4 BSMA1001 None None Statistics for Data Science I 4 BSMA1002 None None Computational Thinking 4 BSCS1001 None None English I 4 BSHS1001 None None Mathematics for Data Science II 4 BSMA1003 BSMA1001 None Statistics for Data Science II 4 BSMA1004 BSMA1002, BSMA1001 BSMA1003 Programming in Python 4 BSCS1002 BSCS1001 None English II 4 BSHS1002 BSHS1001 None Diploma Level

There are two sections in the Diploma Level with courses for Diploma in Programming and courses for Diploma in Data Science. Each of these diplomas comprises 5 core courses, 2 projects and 1 skill enhancement course.

Requirements for registration

The learner should have cleared all 8 Foundation Level courses.

Options on successful completion

Learners have the following options based on the courses completed in this level:

If a learner has completed all the courses and projects in Foundation Level and both Diplomas, they can proceed to the BSc Degree Level. OR they may exit with a Diploma in Programming from IIT Madras. OR they may exit with a Diploma in Data Science from IIT Madras. OR they may exit with both Diplomas from IIT Madras.

12 courses + 4 projects

54 credits

1 - 3 years

15 hrs/course/week

₹1,25,000*

*Refer Fee Structure

Courses for Diploma in Programming

The Diploma in Programming lays a sturdy foundation in databases and programming concepts with data structures and algorithms. The learner goes on to apply these in the building of a web application by the end of the diploma.

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

₹62,500*

*Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites Database Management Systems 4 BSCS2001 None None Programming, Data Structures and Algorithms using Python 4 BSCS2002 None None Modern Application Development I 4 BSCS2003 None BSCS2001 PROJECT Modern Application Development I - Project 2 BSCS2003P None BSCS2003 Programming Concepts using Java 4 BSCS2005 None None Modern Application Development II 4 BSCS2006 BSCS2003 None PROJECT Modern Application Development II - Project 2 BSCS2006P BSCS2003P BSCS2006 System Commands 3 BSSE2001 None None Courses for Diploma in Data Science

The Diploma in Data Science exposes the learner to the holistic approach of gathering, analysing, and interpreting data for a variety of problems. The courses on Business Data lays down the context and the need for the data, while the Machine Learning courses equip the learner to use and analyse this data towards impactful conclusions.

Diploma in Data Science Pathway

Once the student is in the Diploma level, they will have 2 options to complete the Diploma in Data Science as shown below. Students can gain 27 Credits in Diploma in Data Science in 2 ways:

Mandatory: 21 Credits

5 Mandatory Courses + 1 Project

Choose Your Track for remaining 6 Credits

Choose one of two options (Option I or Option II)

Option 1: Business Analytics

Business Analytics + BDM Project

Option 2: Introduction to Deep Learning & AI

Introduction to Deep Learning and Generative AI + Project

Important Notes:

This is effective for students in the Diploma level from the September 2025 Term onwards. Students have to complete the mandatory 5 courses (19 credits) + 1 Project (2 Credits) The remaining 6 credits can be earned by choosing any one of the two options comprising a theory course and a project.

6 courses + 2 projects

27 credits

1 - 2 years

15 hrs/course/week

₹62,500*

*Refer Fee Structure

Course Name Credits Code Prerequisites Corequisites Machine Learning Foundations 4 BSCS2004 None None Business Data Management 4 BSMS2001 None None Machine Learning Techniques 4 BSCS2007 None BSCS2004 Machine Learning Practice 4 BSCS2008 BSCS2004, BSCS2007 None Machine Learning Practice - Project 2 BSCS2008P None BSCS2008 Tools in Data Science 3 BSSE2002 None BSCS2004 Business Data Management - Project OPTION 1 2 BSMS2001P None BSMS2001 Business Analytics OPTION 1 4 BSMS2002 BSMS2001 None Introduction to Deep Learning and Generative AI OPTION 2 4 BSDA2001 None BSCS2008 Deep Learning and Generative AI - Project OPTION 2 2 BSDA2001P BSCS2007 BSCS2008, BSDA2001 BSc Degree Level

for BSc in Programming and Data Science

Requirements for registration

The learner should have cleared all 8 courses in Foundation Level and all 12 courses + 4 projects in Diploma Level.

Options on successful completion

Once the learner successfully completes overall 114 credits including credits earned in all previous levels:

they can proceed to the BS Degree Level. OR they may exit with a BSc Degree in Programming & Data Science from IIT Madras.

BSc Degree Level

28 credits (Total 114 credits)

1 - 3 years

15 hrs/course/week

₹64,000 - ₹70,000*

*Refer Fee Structure

BS Degree Level

for BS in Data Science and Applications

Requirements for registration

The learner should have earned 114 credits and completed the BSc Degree Level to enter the BS Degree Level.

Options on successful completion

Once the learner successfully completes 142 credits and the course requirements:

They can exit with a BS Degree in Data Science and Applications from IIT Madras. OR they can proceed to the PG Diploma Level (if they meet the eligibility criteria of minimum CGPA of 8.0).

BS Degree Level

28 credits (Total 142 credits)

1 - 3 years

15 hrs/course/week

₹94,000 - ₹1,24,000*

*Refer Fee Structure

Degree Level Courses

Core Courses

There are two pairs of core courses in the degree level. It is mandatory for the learner to complete all four core courses.

Core Courses Pair I Core Courses Pair II Software Engineering AI: Search Methods for Problem Solving Software Testing Deep Learning Elective Courses

Here is the list of elective courses offered in the program. In the BSc and BS level, a maximum of 8 credits can be transferred from NPTEL and there is the option to do an apprenticeship and transfer up to a maximum of 12 credits in the BS level. (Note: List of elective courses may change each term depending on availability.)

Search courses with name or code… Course Name Code Credits

Software Engineering CORE COURSE BSCS3001 4
Software Testing CORE COURSE BSCS3002 4
AI: Search Methods for Problem Solving CORE COURSE BSCS3003 4
Deep Learning CORE COURSE BSCS3004 4
Strategies for Professional Growth MANDATORY COURSE BSGN3001 4
Algorithmic Thinking in Bioinformatics BSBT4001 4
Big Data and Biological Networks BSBT4002 4
Data Visualization Design BSCS4001 4
Special topics in Machine Learning (Reinforcement Learning) BSDA5007 4
Speech Technology BSEE4001 4
Design Thinking for Data-Driven App Development BSMS4002 4
Industry 4.0 BSMS4001 4
Sequential Decision Making BSDA5007 4
Market Research BSMS3002 4
Privacy & Security in Online Social Media BSCS4003 4
Introduction to Big Data BSDA5001 4
Financial Forensics BSMS4003 4
Linear Statistical Models BSMA3012 4
Advanced Algorithms BSCS4021 4
Statistical Computing BSMA3014 4
Computer Systems Design BSCS3031 4
Programming in C BSCS3005 4
Mathematical Thinking BSMA2001 4
Large Language Models BSDA5004 4
Introduction to Natural Language Processing (i-NLP) BSDA5005 4
Deep Learning for Computer Vision BSDA5006 4
Managerial Economics BSMS3033 4
Game Theory and Strategy BSMS4023 4
Corporate Finance BSMS3034 4
Deep Learning Practice BSDA5013 4
Operating Systems BSCS4022 4
Mathematical Foundations of Generative AI BSDA5002 4
Algorithms for Data Science (ADS) BSDA5003 4
Machine Learning Operations (MLOps) BSDA5014 4
Data Science and AI Lab BSDA4001 4
App Dev Lab BSCS4010 4
Computer Networks BSCS4024 4 PG Diploma Level

for PG Diploma in AI & ML from IIT Madras

Requirements for registration

Students must have completed all core course requirements at the Degree level and must complete the CGPA requirements at the time of applying for the upgrade.

Options on successful completion

Once the learner successfully completes the course requirements, they can either continue to MTech Level for additional 20 credits (MTech Project) and complete MTech degree or exit with a PG Diploma in AI & ML from IIT Madras.

PG Diploma in AI & ML

20 credits (3 core + 2 electives)

1 - 2 years

15 hrs/course/week

₹1,00,000 - ₹1,40,000*

*Refer Fee Structure

PG Diploma Level Courses

Core Courses

There are three core courses in the PG Diploma level. It is mandatory for the learner to complete all three core courses.

Course Name Code Credits ML Ops BSDA5014 4 Generative AI BSDA5002 4 Algorithms for Data Science BSDA5003 4 Elective Courses

Here is the list of elective courses offered in the PG Diploma Programme. Student can choose 2 elective courses from the following options for a total of 8 credits. (Note: List of elective courses may change each term depending on availability.)

Course Name Code Credits

Large Language Models BSDA5004 4
Introduction to Natural Language Processing (i-NLP) BSDA5005 4
Deep Learning for Computer Vision BSDA5006 4
Reinforcement Learning BSDA5007 4
Responsible AI BSDA6001 4
Statistical Learning Theory BSDA6002 4
Deploybility Aspects of AI BSDA6003 4
Sequential Decision Making BSDA6004 4
Information Theory and Learning BSDA6005 4
Speech Technology BSEE5001 4
Research Project BSDA6006 4 MTech Level

for MTech in AI & ML from IIT Madras

Requirements for registration

Must have completed PG Diploma Level (20 credits) to be eligible for MTech registration. Students can start the project after completing the PG Diploma. Projects can be done in a company or research lab.

Exit

Once the learner successfully completes the MTech requirements:

Students who complete the mandatory MTech Project in AI & ML earn a BS + MTech degree from IIT Madras. The project is executed and evaluated in the same way as Web MTech programs with mandatory project work in broad areas of Machine Learning and AI. Project Timeline: M.Tech should be completed within 8 years from the starting of the program.

MTech in AI & ML

20 credits (MTech Project)

Flexible Timeline

Over approximately 5 years

₹2,00,000*

*Refer Fee Structure

MTech Level Courses

Core Courses

There are three core courses in the MTech level. It is mandatory for the learner to complete all three core courses.

Course Name Course Code Credits MTech Project BSDA6901 20 Sample Certificates

MTech in AI & ML from IIT Madras

PG Diploma in AI & ML from IIT Madras

BS in Data Science and Applications from IIT Madras

BSc in Programming and Data Science from IIT Madras

Diploma in Programming from IIT Madras

Diploma in Data Science from IIT Madras

Advanced Certificate in Programming and Application Development from IIT Madras

Advanced Certificate in Machine Learning and Data Science from IIT Madras

Foundation Certificate from CODE, IIT Madras

support@study.iitm.ac.in 7850999966 IITM BS Degree Office, 3rd Floor, ICSR Building, IIT Madras, Chennai - 600036 Please use only the above methods for program queries. Response time: 3 working days. During peak periods, Google Meet links will be shared. Call wait times may be longer.

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NOTIFY ME Home Academics BSMA1001 Foundational Level Course

Mathematics for Data Science I

This course introduces functions (straight lines, polynomials, exponentials and logarithms) and discrete mathematics (basics, graphs) with many examples. The students will be exposed to the idea of using abstract mathematical structures to represent concrete real life situations.

by Neelesh Upadhye , Madhavan Mukund Course ID: BSMA1001

Course Credits: 4

Course Type: Foundational

Pre-requisites: None

What you’ll learnVIEW COURSE VIDEOS

Recall the basics of sets, natural numbers, integers, rational numbers, and real numbers. Learn to use the coordinate system, and plot straight lines. Identify the properties and differences between linear, quadratic, polynomial, exponential, and logarithmic functions. Find roots, maxima and minima of polynomials using algorithmic methods. Learn to represent sets and relations between set elements as discrete graphs using nodes and edges. Formulate some common real-life problems on graphs and solve them. Course structure & Assessments

12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.

WEEK 1	Set Theory - Number system, Sets and their operations, Relations and functions - Relations and their types, Functions and their types
WEEK 2	Rectangular coordinate system, Straight Lines - Slope of a line, Parallel and perpendicular lines, Representations of a Line, General equations of a line, Straight-line fit
WEEK 3	Quadratic Functions - Quadratic functions, Minima, maxima, vertex, and slope, Quadratic Equations
WEEK 4	Algebra of Polynomials - Addition, subtraction, multiplication, and division, Algorithms, Graphs of Polynomials - X-intercepts, multiplicities, end behavior, and turning points, Graphing & polynomial creation
WEEK 5	Functions - Horizontal and vertical line tests, Exponential functions, Composite functions, Inverse functions
WEEK 6	Logarithmic Functions - Properties, Graphs, Exponential equations, Logarithmic equations
WEEK 7	Sequence and Limits - Function of One variable - • Function of one variable • Graphs and Tangents • Limits for sequences • Limits for function of one variable • Limits and Continuity
WEEK 8	Derivatives, Tangents and Critical points - • Differentiability and the derivative • Computing derivatives and L’Hˆopital’s rule • Derivatives, tangents and linear approximation • Critical points: local maxima and minima
WEEK 9	Integral of a function of one variable - • Computing areas, Computing areas under a curve, The integral of a function of one variable • Derivatives and integrals for functions of one variable
WEEK 10	Graph Theory - Representation of graphs, Breadth-first search, Depth-first search, Applications of BFS and DFS; Directed Acyclic Graphs - Complexity of BFS and DFS, Topological sorting
WEEK 11	Longest path, Transitive closure, Matrix multiplication Graph theory Algorithms - Single-source shortest paths, Dijkstra’s algorithm, Bellman-Ford algorithm, All-pairs shortest paths, Floyd–Warshall algorithm, Minimum cost spanning trees, Prim’s algorithm, Kruskal’s algorithm
WEEK 12	Revision

Show less Reference Documents / Books

Sets & Functions (VOL 1)

Calculus (VOL 2)

GRAPH THEORY (VOL 3)

Prescribed Books

The following are the suggested books for the course:

Introductory Algebra: a real-world approach (4th Edition) - by Ignacio Bello

About the Instructors

Neelesh Upadhye Associate Professor, Department of Mathematics, IIT Madras Experienced Associate Professor with a demonstrated history of working in the higher education industry. Skilled in Mathematical Modeling, R, Stochastic Modeling, and Statistical Modeling. Strong education professional with a Doctor of Philosophy (Ph.D.) focused in Mathematical Statistics and Probability from Indian Institute of Technology, Bombay. Visit website

Madhavan Mukund Director, Chennai Mathematical Institute Madhavan Mukund studied at IIT Bombay (BTech) and Aarhus University (PhD). He has been a faculty member at Chennai Mathematical Institute since 1992.His main research area is formal verification. He has active research collaborations within and outside India and serves on international conference programme committees and editorial boards of journals. … more Visit website Other courses by the same instructor: BSCS1001 - Computational Thinking , BSCS2002 - Programming, Data Structures and Algorithms using Python and BSCS2005 - Programming Concepts using Java

View all Foundational Level courses support@study.iitm.ac.in 7850999966 IITM BS Degree Office, 3rd Floor, ICSR Building, IIT Madras, Chennai - 600036 Please use only the above methods for program queries. Response time: 3 working days. During peak periods, Google Meet links will be shared. Call wait times may be longer.

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NOTIFY ME

Home Academics BSMA1002 Foundational Level Course

Statistics for Data Science I

The students will be introduced to large datasets. Using this data, the students will be introduced to various insights one can glean from the data. Basic concepts of probability also will be introduced during the course leading to a discussion on Random variables.

by Usha Mohan Course ID: BSMA1002

Course Credits: 4

Course Type: Foundational

Pre-requisites: None

What you’ll learnVIEW COURSE VIDEOS

Create, download, manipulate, and analyse data sets. Frame questions that can be answered from data in terms of variables and cases. Describe data using numerical summaries and visual representations. Estimate chance by applying laws of probability. Translate real-world problems into probability models. Calculating expectation and variance of a random variable. Describe and apply the properties of the Binomial Distribution and Normal distribution. Course structure & Assessments

WEEK 1	Introduction and type of data, Types of data, Descriptive and Inferential statistics, Scales of measurement
WEEK 2	Describing categorical data Frequency distribution of categorical data, Best practices for graphing categorical data, Mode and median for categorical variable
WEEK 3	Describing numerical data Frequency tables for numerical data, Measures of central tendency - Mean, median and mode, Quartiles and percentiles, Measures of dispersion - Range, variance, standard deviation and IQR, Five number summary
WEEK 4	Association between two variables - Association between two categorical variables - Using relative frequencies in contingency tables, Association between two numerical variables - Scatterplot, covariance, Pearson correlation coefficient, Point bi-serial correlation coefficient
WEEK 5	Basic principles of counting and factorial concepts - Addition rule of counting, Multiplication rule of counting, Factorials
WEEK 6	Permutations and combinations
WEEK 7	Probability Basic definitions of probability, Events, Properties of probability
WEEK 8	Conditional probability - Multiplication rule, Independence, Law of total probability, Bayes’ theorem
WEEK 9	Random Variables - Random experiment, sample space and random variable, Discrete and continuous random variable, Probability mass function, Cumulative density function
WEEK 10	Expectation and Variance - Expectation of a discrete random variable, Variance and standard deviation of a discrete random variable
WEEK 11	Binomial and poisson random variables - Bernoulli trials, Independent and identically distributed random variable, Binomial random variable, Expectation and variance of abinomial random variable, Poisson distribution
WEEK 12	Introduction to continous random variables - Area under the curve, Properties of pdf, Uniform distribution, Exponential distribution

Show less Reference Documents / Books

Descriptive Statistics (VOL 1)

Probability and Probability Distributions (VOL 2)

Prescribed Books

The following are the suggested books for the course:

Introductory Statistics (10th Edition) - ISBN 9780321989178, by Neil A. Weiss published by Pearson

Introductory Statistics (4th Edition) - by Sheldon M. Ross

About the Instructors

Usha Mohan Professor, Department of Management Studies, IIT Madras Usha Mohan holds a Ph.D. from Indian Statistical Institute. She has worked as a researcher in ISB Hyderabad and Lecturer at University of Hyderabad prior to joining IIT Madras. She offers courses in Data analytics, Operations research, and Supply chain management to under graduate, post graduate and doctoral students. In addition, she conducts training in Optimization methods and Data Analytics for industry professionals. Her research interests include developing quantitative models in operations management and combinatorial optimization. Visit website View all Foundational Level courses support@study.iitm.ac.in 7850999966 IITM BS Degree Office, 3rd Floor, ICSR Building, IIT Madras, Chennai - 600036 Please use only the above methods for program queries. Response time: 3 working days. During peak periods, Google Meet links will be shared. Call wait times may be longer.

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NOTIFY ME Home Academics BSCS1001 Foundational Level Course

Computational Thinking

The students will be introduced to a number of programming concepts using illustrative examples which will be solved almost entirely manually. The manual execution of each solution allows for close inspection of the concepts being discussed.

by Madhavan Mukund , Dr. G Venkatesh Course ID: BSCS1001

Course Credits: 4

Course Type: Foundational

Pre-requisites: None

What you’ll learnVIEW COURSE VIDEOS

Applying a procedural approach to real life problems: sequencing basic steps, identifying common patterns. Communicating procedural descriptions: flowcharts, pseudo-code. Understanding underlying abstractions used in programming, through examples: variables, iteration, accumulation, filtering, parametrised procedures, polymorphism and state. Selecting appropriate data structures to store relationships between data: lists, trees, matrices, graphs. Identifying algorithmic techniques to solve a given problem: searching, sorting, indexing, matching. Decomposing problems into smaller units to find a solution: recursion, divide and conquer. Understanding and checking algorithms: predict their behaviour, design tests to verify their output, perform simple debugging. Course structure & Assessments

WEEK 1	Variables, Initialization, Iterators, Filtering, Datatypes, Flowcharts, Sanity of data
WEEK 2	Iteration, Filtering, Selection, Pseudocode, Finding max and min, AND operator
WEEK 3	Multiple iterations (non-nested), Three prizes problem, Procedures, Parameters, Side effects, OR operator
WEEK 4	Nested iterations, Birthday paradox, Binning
WEEK 5	List, Insertion sort
WEEK 6	Table, Dictionar
WEEK 7	Graph, Matrix
WEEK 8	Adjacency matrix, Edge labelled graph
WEEK 9	Backtracking, Tree, Depth First Search (DFS), Recursion
WEEK 10	Object oriented programming, Class, Object, Encapsulation, Abstraction, Information hiding, Access specifiers
WEEK 11	Message passing, Remote Procedure Call (RPC), Cache memory, Parallelism, Concurrency, Polling, Preemption, Multithreading, Producer Consumer, Atomicity, Consistency, Race condition, Deadlock, Broadcasting
WEEK 12	Top-down approach, Bottom-up approach, Decision tree, Numerical prediction, Behaviour analysis, Classification

Show less About the Instructors

Madhavan Mukund Director, Chennai Mathematical Institute Madhavan Mukund studied at IIT Bombay (BTech) and Aarhus University (PhD). He has been a faculty member at Chennai Mathematical Institute since 1992.His main research area is formal verification. He has active research collaborations within and outside India and serves on international conference programme committees and editorial boards of journals. … more Visit website Other courses by the same instructor: BSCS2002 - Programming, Data Structures and Algorithms using Python , BSCS2005 - Programming Concepts using Java and BSMA1001 - Mathematics for Data Science I

Dr. G Venkatesh Professor of Practice, Department of Humanities and Social Sciences, IIT Madras Dr. Venkatesh is a Professor of Practice at IIT Madras, where he is involved with several projects in the field of education. He is also a Fellow and Director of Sasken Communication Technologies Ltd, a leading Indian R&D services provider, and a founder of Mylspot, an education technology startup that aims to bridge knowledge gaps of students through a mentored learning platform. … more Other courses by the same instructor: BSMS2001 - Business Data Management and BSMS2002 - Business Analytics

4 - Course Page - IIT Madras Degree Program

NOTIFY ME Home Academics BSHS1001 Foundational Level Course

English I

This course aims at achieving fluency and confidence in spoken and written English. This course will use insights from theories of learning and dominant methods of teaching language.

by Rajesh Kumar , Karthika Sathyanathan Course ID: BSHS1001

Course Credits: 4

Course Type: Foundational

Pre-requisites: None

What you’ll learnVIEW COURSE VIDEOS

Acquiring wide range of vocabulary and linguistic competence that is required for functional performance; Identifying patterns of basic sentence types and structural accuracy; Building elementary foundations for the knowledge related to conventions and use of language in society, particularly in speaking and listening skills; Developing the basic skills for creative reading and writing with precision. Course structure & Assessments

WEEK 1	Sounds and Words (Vowel and Consonant sounds)
WEEK 2	Parts of Speech
WEEK 3	Sentences (Phrases and Idioms)
WEEK 4	Speaking Skills (Spoken English Preliminaries)
WEEK 5	Tenses and Agreement in English Sentences
WEEK 6	Reading Skills (Skimming, Scanning and Comprehension)
WEEK 7	Listening Skills
WEEK 8	Aspiration, Word Stress and Syllabification
WEEK 9	Speaking Skills (Presentation and Group Discussion)
WEEK 10	Grammar (Common Errors in English) and Writing Skills
WEEK 11	Writing Skills (Basics of Writing)
WEEK 12	Writing Skills (Professional Writing)

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Addition Learning Support for English - 1 (Basic English)

Prescribed Books

The following are the suggested books for the course:

Aarts, Bas (2011). Oxford Modern English Grammar, New York: Oxford University Press

Murphy, Raymond (2012). English Grammar in Use, New York: Cambridge University Press. 4th Edition

Krishnaswamy, Subashree and K. Srilata eds. (2007). Short Fiction from South India. Delhi: OUP.

Dhanavel, S.P. (2010). English and soft skills (V-1). Chennai: Orient Blackswan.

References:

Oxford English Dictionary

Croft, Sebastian (2018). How to Analyze People: The Ultimate Guide to Speed Reading People Through Proven Psychological Techniques, Body Language Analysis and Personality Types and Patterns (Available on Kindle)

Malgudi Days: A collection of short-stories (RK Narayan)

365 Jataka Tales (Om Books International)

365 Panchtatra Stories (Adil Mukesh)

365 Tales from Indian Mythology (Om Books International)

About the Instructors

Rajesh Kumar Professor, Department of Humanities and Social Sciences, IIT Madras Rajesh Kumar is professor of linguistics in the Department of Humanities and Social Sciences at the Indian Institute of Technology Madras, Chennai. He obtained his PhD in linguistics from the University of Illinois at Urbana-Champaign. Prior to joining IIT Madras, he taught at IIT Kanpur, and IIT Patna in India and at the University of Texas at Austin in the USA. He has been a visiting faculty at the Tata Institute of Social Sciences in Mumbai in India. His book on Syntax of Negation and Licensing of Negative Polarity Items was published by Routledge in their prestigious series Outstanding Dissertations in Linguistics in 2006. He is associate editor of the journal Language and Language Teaching. He has been part of the language teaching program at all the institutions he has been affiliated with. The broad goal of his research is to uncover regularities underlying both the form (what language is) and sociolinguistic functions (what language does) of natural languages. Visit website Other courses by the same instructor: BSHS1002 - English II

Karthika Sathyanathan Alumna, Department of Humanities and Social Sciences, IIT Madras Karthika has an MA in English Studies from IIT Madras. She has worked as a Language & Education Consultant with multiple government departments and non-government organisations. Currently she is working as project officer with IIT Madras. Her areas of interest include ELT, multilingualism, multiculturalism and second language learning. Other courses by the same instructor: BSHS1002 - English II

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Home Academics BSMA1003 Foundational Level Course

Mathematics for Data Science II

This course aims to introduce the basic concepts of linear algebra, calculus and optimization with a focus towards the application area of machine learning and data science.

by Sarang S Sane Course ID: BSMA1003

Course Credits: 4

Course Type: Foundational

Pre-requisites: BSMA1001 - Mathematics for Data Science I

What you’ll learnVIEW COURSE VIDEOS

Manipulating matrices using matrix algebra. Performing elementary row operations. Using Gaussian Elimination: Solving systems of linear equations. Find out whether a set of vectors are linearly independent. Writing down a set of dependencies in case vectors are not linearly independent. Finding subspaces along with their bases and ranks. Finding distances and angles using norms and inner products. Obtaining orthonormal basis using the Gram-Schmidt process. Finding maxima and minima of single variable functions using derivatives. Finding maxima and minima of multivariate functions using vector calculus. Course structure & Assessments

11 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.

WEEK 1	Vector and matrices - Vectors; Matrices; Systems of Linear Equations; Determinants (part 1); Determinants (part 2)
WEEK 2	Solving linear equations - Determinants (part 3); Cramer’s Rule; “Solutions to a system of linear equations with an invertible coefficient matrix”; The echelon form; Row reduction; The Gaussian elimination method
WEEK 3	Introduction to vector spaces - Introduction to vector spaces; Some properties of vector spaces; Linear dependence; Linear independence - Part; Linear independence - Part 2
WEEK 4	Basis and dimension - What is a basis for a vector space?; Finding bases for vector spaces; What is the rank/dimension for a vector space; Rank and dimension using Gaussian elimination
WEEK 5	Rank and Nullity of a matrix; Introduction to Linear transformation - The null space of a matrix: finding nullity and a basis - Part 1; The null space of a matrix: finding nullity and a basis - Part 2; What is a linear mapping - Part 1; What is a linear mapping - Part 2; What is a linear transformation
WEEK 6	Linear transformation, Kernel and Images - Linear transformations, ordered bases and matrices; Image and kernel of linear transformations; Examples of finding bases for the kernel and image of a linear transformation
WEEK 7	Equivalent and Similar matrices; Introduction to inner products - Equivalence and similarity of matrices; Affine subspaces and affine mappings; Lengths and angles; Inner products and norms on a vector space
WEEK 8	Orthogonality, Orthonormality; Gram-schmidt method - Orthgonality and linear independence; What is an orthonormal basis? Projections using inner products; The Gram-Schmidt process; Orthogonal transformations and rotations
WEEK 9	Multivariable functions, Partial derivatives, Limit, continuity and directional derivatives - Multivariable functions: visualization; Partial derivatives; Directional derivatives; Limits for scalar-valued multivariable functions; Continuity for multivariable functions; Directional derivatives in terms of the gradient
WEEK 10	Directional ascent and descent, Tangent (hyper) plane, Critical points - The directional of steepest ascent/descent; Tangents for scalar-valued multivariable functions; Finding the tangent hyper(plane); Critical points for multivariable functions
WEEK 11	Higher order partial derivatives, Hessian Matrix and local extrema, Differentiability - Higher order partial derivatives and the Hessian matrix; The Hessian matrix and local extrema for f(x,y); The Hessian matrix and local extrema for f(x,y,z); Differentiability for Multivariable Functions; Review of Maths - 2

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Linear Algebra

About the Instructors

Sarang S Sane Assistant Professor, Department of Mathematics, IIT Madras I completed my B.Stat. (Hons.) and M.Stat. from the Indian Statistical Institute, Kolkata in 2004 and my Ph.D. from TIFR, Mumbai in 2010. I was a postdoctoral fellow in TIFR, a visiting assistant professor in the University of Kansas and very briefly an INSPIRE faculty fellow in IISc, Bengaluru before I joined the mathematics department in IITM in 2015. Visit website View all Foundational Level courses support@study.iitm.ac.in 7850999966 IITM BS Degree Office, 3rd Floor, ICSR Building, IIT Madras, Chennai - 600036 Please use only the above methods for program queries. Response time: 3 working days. During peak periods, Google Meet links will be shared. Call wait times may be longer.

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Home Academics BSMA1004 Foundational Level Course

Statistics for Data Science II

This second course will develop on the first course on statistics and further delve into the main statistical problems and solution approaches

by Andrew Thangaraj Course ID: BSMA1004

Course Credits: 4

Course Type: Foundational

Pre-requisites: BSMA1002 - Statistics for Data Science I BSMA1001 - Mathematics for Data Science I

Co-requisites: BSMA1003 - Mathematics for Data Science II

What you’ll learnVIEW COURSE VIDEOS

Recalling statistical modeling, description of data. Applying Probability distributions and related concepts to the data sets Explaining the concept of estimation of parameters. Solving the problems related to point and interval estimation. Explaining the concept of Testing of hypothesis related to mean and variance Analysing the data using simple regression models and setting up relevant hypothesis tests Course structure & Assessments

WEEK 1	Multiple random variables - Two random variables, Multiple random variables and distributions
WEEK 2	Multiple random variables - Independence, Functions of random variables - Visualization, functions of multiple random variables
WEEK 3	Expectations Casino math, Expected value of a random variable, Scatter plots and spread, Variance and standard deviation, Covariance and correlation, Inequalities
WEEK 4	Continuous random variables Discrete vs continuous, Weight data, Density functions, Expectations
WEEK 5	Multiple continuous random variables - Height and weight data, Two continuous random variables, Averages of random variables - Colab illustration, Limit theorems, IPL data - histograms and approximate distributions, Jointly Gaussian random variables Probability models for data - Simple models, Models based on other distributions, Models with multiple random variables, dependency, Models for IPL powerplay, Models from data
WEEK 6	Refresher week
WEEK 7	Estimation and Inference I
WEEK 8	Estimation and Inference II
WEEK 9	Bayesian estimation
WEEK 10	Hypothesis testing I
WEEK 11	Hypothesis Testing II
WEEK 12	Revision week

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Joint Discrete Distributions (VOL 1)

Joint Continuous Distributions (VOL 2)

Prescribed Books

The following are the suggested books for the course:

Probability and Statistics with Examples using R. Author: Siva Athreya, Deepayan Sarkar and Steve Tanner

About the Instructors

Andrew Thangaraj Professor , Electrical Engineering Department , IIT Madras Andrew Thangaraj received his B. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Madras in 1998 and Ph.D. in Electrical Engineering from the Georgia Institute of Technology, Atlanta, USA in 2003. … more Visit website View all Foundational Level courses support@study.iitm.ac.in 7850999966 IITM BS Degree Office, 3rd Floor, ICSR Building, IIT Madras, Chennai - 600036 Please use only the above methods for program queries. Response time: 3 working days. During peak periods, Google Meet links will be shared. Call wait times may be longer.

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Home Academics BSCS1002 Foundational Level Course

Programming in Python

This will be the first formal programming course that students will see in this programme. The goal of this course is to introduce Python programming, which is used throughout the programme, with a basic problem solving and algorithmic flavour.

by Sudarshan Iyengar Course ID: BSCS1002

Course Credits: 4

Course Type: Foundational

Pre-requisites: BSCS1001 - Computational Thinking

What you’ll learnVIEW COURSE VIDEOS

Using basic programming concepts such as variables, expressions, loops, conditionals and functions in Python Creating, manipulating, and using more Python specific features such as lists, tuples, and dictionaries Familiarising with and using common Python libraries such as random, math, datetime, scipy, matplotlib, Pandas etc Analysing real life activities and casting them as programming problems Applying programming concepts to analyse and solve diverse problems Writing Readable code and debugging it Building small applications using python Course structure & Assessments

WEEK 1	Introduction to algorithms
WEEK 2	Conditionals
WEEK 3	Conditionals (Continued)
WEEK 4	Iterations and Ranges
WEEK 5	Iterations and Ranges (Continued)
WEEK 6	Basic Collections in Python
WEEK 7	Basic Collections in Python (Continued)
WEEK 8	Basic Collections in Python (Continued)
WEEK 9	File Operations
WEEK 10	File Operations (Continued)
WEEK 11	Module system in python
WEEK 12	Basic Pandas and Numpy processing of data

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The following are the suggested books for the course:

Title: Python for Everybody. Author: Charles R. Severance. Publisher: Shroff Publishers. ISBN: 9789352136278

(The PDF of this book is currently available freely at http://do1.dr-chuck.com/pythonlearn/EN_us/pythonlearn.pdf)

About the Instructors

Sudarshan Iyengar Associate Professor and Head , Department of Computer Science and Engineering, IIT Ropar Sudarshan Iyengar has a PhD from the Indian Institute of Science and is currently working as an Associate Professor and Head of CSE at IIT Ropar. Visit website View all Foundational Level courses support@study.iitm.ac.in 7850999966 IITM BS Degree Office, 3rd Floor, ICSR Building, IIT Madras, Chennai - 600036 Please use only the above methods for program queries. Response time: 3 working days. During peak periods, Google Meet links will be shared. Call wait times may be longer.

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Home Academics BSHS1002 Foundational Level Course

English II

Focus on achieving greater degree of fluency in functional and conversational English to understand subtle and detailed meaning in conversations and texts through short literary pieces and contextualized content.

by Rajesh Kumar , Karthika Sathyanathan Course ID: BSHS1002

Course Credits: 4

Course Type: Foundational

Pre-requisites: BSHS1001 - English I

What you’ll learnVIEW COURSE VIDEOS

Integrating the basic skills of language into developing advanced skills of language proficiency to help compose clear and detailed writing on a range of subjects; Learning advanced level of vocabulary and socio-linguistic/ socio-pragmatic competence for advance reading and writing; Building nuanced structure for grammatical accuracy for fluency and creating confidence and appropriateness for expressing view-points clearly; Developing elementary foundations for comprehending and conveying underlying meaning in spoken discourse Course structure & Assessments

WEEK 1	Patterns in Sentences
WEEK 2	Patterns in Sentences (Continued)
WEEK 3	Patterns in Sentences (Continued)
WEEK 4	Listening Skills
WEEK 5	Listening Skills (Continued)
WEEK 6	Speaking Skills
WEEK 7	Speaking Skills (Continued)
WEEK 8	Reading Skills
WEEK 9	Writing Skills
WEEK 10	Writing Skills (Continued)
WEEK 11	Social Skills
WEEK 12	Social Skills (Continued)

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Addition Learning Support for English - 2 (Basic English)

About the Instructors

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Foundational Level Syllabus

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