Qualifier Applications for the second batch are now open.   

Qualifier Applications for the second batch are now open.   

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: BSCMA1001

Course Credits: 4

Course Type: Foundational

Prerequisites: None

What you’ll learn

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, 3 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.

WEEK 1 - Click Here Basics of discrete mathematics
WEEK 2 Straight Lines
WEEK 3 Straight Lines (Continued)
WEEK 4 Quadratic equations
WEEK 5 Quadratic equations (Continued)
WEEK 6 Polynomials
WEEK 7 Polynomials (Continued)
WEEK 8 Exponentials and logarithms
WEEK 9 Exponentials and logarithms (Continued)
WEEK 10 Relations and Graphs
WEEK 11 Other Graph problems
WEEK 12 Other Graph problems (Continued)

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, 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.


Other courses by the same instructor: BSCMA1004 - Statistics for Data Science II

Madhavan Mukund
Professor, Department of Computer Science & Engineering, 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, where he is presently Deputy Director and Dean of Studies. 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.

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He has served as President of both the Indian Association for Research in Computing Science (IARCS) (2011-2017) and the ACM India Council (2016-2018). He has been the National Coordinator of the Indian Computing Olympiad since 2002. He served as the Executive Director of the International Olympiad in Informatics from 2011-2014.

In addition to the NPTEL MOOC programme, he has been involved in organizing IARCS Instructional Courses for college teachers. He is a member of ACM India's Education Committee. He has contributed lectures on algorithms to the Massively Empowered Classroom (MEC) project of Microsoft Research and the QEEE programme of MHRD.


Other courses by the same instructor: BSCCS1001 - Computational Thinking , BSCCS2002 - Programming, Data Structures and Algorithms using Python and BSCCS2005 - Programming Concepts using Java