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IIT Madras BS Degree Foundation Level

· 4 min read
Meet Jain
Meet Jain
Author

Bachelor of Science in Data Science and Applications​

Indian Institute of Technology Madras (IITM)
Online Program
September 2022 – June 2026 (Expected)

Current Status:

  • Foundation Level Completed: Achieved a CGPA of 7.00
  • Result Released: September 8, 2024

Completed Courses​

1. Statistics for Data Science I
Instructor: Usha Mohan

  • Concepts Covered:
    • Data Analysis: Techniques for creating, manipulating, and analyzing large datasets.
    • Descriptive Statistics: Methods to summarize and describe data, including measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation).
    • Probability Basics: Fundamental concepts of probability, including random variables, probability distributions, and expectation.
    • Distribution Analysis: Detailed study of the Binomial and Normal distributions, including their properties and applications in real-world scenarios.
  • Course Structure: Included weekly assignments, quizzes, and an end-term exam to assess understanding and application of statistical principles.

Instructor Background:
Usha Mohan is a Professor at the Department of Management Studies, IIT Madras. She holds a Ph.D. from the Indian Statistical Institute and has extensive experience in data analytics, operations research, and supply chain management.

2. Statistics for Data Science II
Instructor: Andrew Thangaraj

  • Concepts Covered:
    • Advanced Statistical Modeling: Techniques for modeling data, including parameter estimation and hypothesis testing.
    • Probability Distributions: In-depth analysis of continuous and discrete random variables and their distributions.
    • Estimation Techniques: Methods for point and interval estimation, including confidence intervals and testing of hypotheses related to means and variances.
    • Regression Analysis: Application of simple linear regression models to analyze relationships between variables and test hypotheses.
  • Course Structure: Included comprehensive coursework, practical assignments, quizzes, and an end-term exam.

Instructor Background:
Andrew Thangaraj is a Professor in the Electrical Engineering Department at IIT Madras. He completed his Ph.D. from the Georgia Institute of Technology and has expertise in electrical engineering and statistical modeling.

3. Mathematics for Data Science I
Instructors: Neelesh Upadhye, Madhavan Mukund

  • Concepts Covered:
    • Functions and Graphs: Detailed study of functions including linear, polynomial, exponential, and logarithmic functions.
    • Discrete Mathematics: Basics of set theory, relations, and functions, with practical applications in graph theory.
    • Polynomial Algebra: Operations with polynomials, including addition, subtraction, multiplication, and division.
    • Graph Theory: Representation of sets and relations as graphs, and solving real-life problems using graph-based methods.
  • Course Structure: Comprises weekly coursework, assignments, and quizzes with an end-term exam to evaluate understanding.

Instructor Background:
Neelesh Upadhye is an Associate Professor in the Department of Mathematics at IIT Madras, specializing in mathematical modeling and statistics. Madhavan Mukund, Director at Chennai Mathematical Institute, is an expert in formal verification and theoretical computer science.

4. Mathematics for Data Science II
Instructor: Sarang S Sane

  • Concepts Covered:
    • Linear Algebra: Matrix operations, Gaussian elimination, and vector space theory.
    • Calculus: Differentiation and integration of single-variable and multivariate functions.
    • Optimization: Techniques for finding maxima and minima of functions using calculus and linear algebra.
    • Application to Machine Learning: Application of mathematical concepts to solve problems in machine learning and data science.
  • Course Structure: Includes weekly assignments, quizzes, and an end-term exam to assess grasp of mathematical concepts and their applications.

Instructor Background:
Sarang S Sane is an Assistant Professor in the Department of Mathematics at IIT Madras, with expertise in linear algebra and optimization. He holds a Ph.D. from TIFR and has a background in mathematical statistics and probability.

5. Programming in Python

  • Concepts Covered:
    • Core Python Programming: Basic and advanced programming techniques, including data structures, algorithms, and object-oriented programming.
    • Applications in Data Science: Use of Python for data analysis, manipulation, and visualization.
  • Course Structure: Focused on practical programming skills with assignments and coding projects.

6. Computational Thinking

  • Concepts Covered:
    • Problem-Solving Techniques: Algorithmic thinking and problem-solving strategies relevant to data science and computational tasks.
    • Algorithm Design: Fundamentals of designing and analyzing algorithms for efficient problem-solving.
  • Course Structure: Included practical exercises and projects to develop computational thinking skills.

7. English I & II

  • Concepts Covered:
    • Professional Communication: Development of technical writing skills, academic writing, and effective communication in professional contexts.
  • Course Structure: Focused on improving writing and communication skills through assignments and assessments.

Current Enrollment (September 2024 Term):

  1. Machine Learning Techniques (NEW COURSE)

    • Focus: Advanced machine learning methodologies and their practical applications.

  2. Business Data Management (NEW COURSE)

    • Focus: Managing and analyzing business data, including data warehousing, data integration, and business intelligence.

  3. Machine Learning Foundations (NEW COURSE)

    • Focus: Fundamental principles of machine learning, including algorithms, models, and their theoretical underpinnings.

Current Project:

  1. Business Data Management - Project
    • Focus: Applying concepts from the Business Data Management course to a real-world data management project, with emphasis on data analysis and business decision-making.