Probability, Statistics and Data Visualization

ES 114 · IIT Gandhinagar · Semester II, 2024–25

ES 114 is a first-year undergraduate course. It starts with the Python data stack (NumPy, pandas and Matplotlib), then builds up probability from sets: conditional probability and Bayes' theorem, random variables and their distributions, expectation and moments, joint distributions, sums and transformations of random variables, the law of large numbers, the multivariate normal distribution and principal component analysis.

Instructors
Prof. Nipun Batra and Prof. Manisha Padala
Offices
AB 13/401C (directions) and AB 13/405C
Email
nipun.batra@iitgn.ac.in, manisha.padala@iitgn.ac.in
Lectures
Jasubhai Auditorium, A1 slot
Labs
Computer labs (AB 7/108, AB 7/109, AB 10/104, AB 10/105), A2 and D1 slots
Prerequisites
None
Teaching assistants
35 TAs
Discussion
Course Slack, used for all communication

Lectures

YouTube playlist of recordings All 15 cheat sheets All 27 lectures

Questions

What if I miss a quiz?

It is marked 0. The best three of four quizzes count, which is designed for such cases, and there are no makeup quizzes for any reason. The mid-semester and end-semester exams count as quizzes.

Are quizzes open book?

No. Quizzes and exams are closed book, and you may not carry notes. They may mix multiple-choice and written questions.

How are assignments done and graded?

In groups of three, in Python, with no extensions. Each is followed by a viva: code that runs and solves the problem 50%, questions on the assignment and your code 25%, code quality 12.5%, theory 12.5%. See Assignments.

Can I use my laptop or phone in class?

No, except in a lab session where the instructor has explicitly allowed laptops.

How do I apply for leave?

Fill out the leave application form and email the three leave TAs with the subject line "ES114 Leave Request". See Leave.

All questions · or ask on the course Slack.

References

Reference textbooks:

  • Stanley Chan, Introduction to Probability for Data Science, Michigan Publishing, 2021 (free digital copy)
  • Ani Adhikari, John DeNero and David Wagner, Computational and Inferential Thinking: The Foundations of Data Science, second edition, UC Berkeley, 2019 (free digital copy)
  • Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong, Mathematics for Machine Learning, Cambridge University Press, 2020 (free digital copy)
  • David Spiegelhalter, The Art of Statistics: Learning from Data, Penguin UK, 2019
  • Nicolas Rougier, Scientific Visualization: Python + Matplotlib, 2021 (free digital copy)

Course materials:

Teaching assistants

  • Ayush Shrivastavashrivastavaayush@iitgn.ac.in
  • Rishabh Mondalrishabh.mondal@iitgn.ac.in
  • Abhyudaya Nair24210005@iitgn.ac.in
  • Alay Patelalay.patel@iitgn.ac.in
  • Ashish Rawatashish.rawat@iitgn.ac.in
  • Balkrishna Sehra24210022@iitgn.ac.in
  • Charan Teja24210056@iitgn.ac.in
  • Chetana Dubey24250030@iitgn.ac.in
  • Dekka Muni Kumar24210028@iitgn.ac.in
  • Dhruv Pateldhruv.patel@iitgn.ac.in
  • Diya24210032@iitgn.ac.in
  • Drishti24210033@iitgn.ac.in
  • Indrayudh Mandal24210041@iitgn.ac.in
  • Jenil Patel24210048@iitgn.ac.in
  • Kaloori Shiva Prasad24250041@iitgn.ac.in
  • Manish Bairwamanish.bairwa@iitgn.ac.in
  • Mithlesh Singla24210063@iitgn.ac.in
  • Palak Gupta24250063@iitgn.ac.in
  • Sawan Vermasawan.verma@iitgn.ac.in
  • Shataxi Dubeyshataxi.dubey@iitgn.ac.in
  • Shivansh Gupta24250086@iitgn.ac.in
  • Suruchi Hardaha24250097@iitgn.ac.in
  • Vinayak Rana24210114@iitgn.ac.in
  • Aadya Aroraaadya.arora@iitgn.ac.in
  • Abhishek Kushwahaabhishek.kushwaha@iitgn.ac.in
  • Aryan Sahuaryan.sahu@iitgn.ac.in
  • Bhavik Patelbhavik.patel@iitgn.ac.in
  • Bhoumik Patidarbhoumik.patidar@iitgn.ac.in
  • Dewansh Chandeldewanshsingh.chandel@iitgn.ac.in
  • Dewansh Kumardewansh.kumar@iitgn.ac.in
  • Guntas Singh Saranguntassingh.saran@iitgn.ac.in
  • Shreyans Jainshreyans.jain@iitgn.ac.in
  • Vamsi Srivathsavamsi.srivathsa@iitgn.ac.in
  • Viraj Vekariaviraj.vekaria@iitgn.ac.in
  • Yash Bachwanayash.bachwana@iitgn.ac.in