Your instructors

Mitesh Khapra and Pratyush Kumar

Mitesh and Pratyush are Assistant Professors at the Department of Computer Science and Engineering at IIT Madras. They have both industry and academic experience in working with deep learning and related areas. They are both passionate about teaching and contributing to nation building.
Mitesh Khapra and Pratyush Kumar
Watch Intro Video

PadhAI One FAQs

The PadhAI One Series

This course is part of the three-course series

  • Foundations of Data science

    To begin on 1st Feb 2020
    4 months
    Rs 1,000 for students

    This course

  • Machine Learning

    To begin on 1st Jul 2020
    4 months
    Rs 1,000 for students

    Coming soon!

  • Deep Learning

    To begin on 1st Dec 2020
    4 months
    Rs 1,000 for students

    Coming soon!

Is this the right course for me?

If you are familiar with programming (in any language) and comfortable with mathematics at 12th standard (high school) level, then you should be able to follow along with this course.

This course is well suited for the following learning objectives:

  • Understand the value of data science and the process behind using it.
  • Learn the fundamentals of statistics and probability required for data science.
  • Use Python to gather, store, clean, analyse, and visualise data-sets.
  • Apply statistical methods to formulate and test data hypotheses.
  • Apply statistical inference to uncover relationships within data-sets.
  • Understand the role of machine learning and deep learning in the data science pipeline.
  • Understand real-world challenges with several case studies.

Course curriculum

  • 2

    Week 1: Introduction

  • 3

    Week 2: Primer on Python

    • Introduction to programming for Data Science

    • Setting up your system for Data Science

    • Python basics (Variables, Data types, Functions)

    • Python basics assignment

  • 4

    Week 3: Descriptive statistics

    • Describing datasets (graphs and plots)

    • Summarising datasets (measures of centrality and dispersion)

    • Assignment on Statistics

  • 5

    Week 4: Python (continued)

    • Complex data types (lists, tuples, dictionary, sets)

    • Iterators and operators on lists

    • Introduction to NumPy

  • 6

    Week 5: Descriptive statistics (continued)

    • Measures of spread

    • Correlation and covariance

  • 7

    Week 6: Numpy (continued) and Pandas

    • Numpy continued (computing measures of centrality and spread)

    • Introduction to Pandas

    • Pandas: Indexing and computing statistics

  • 8

    Week 7: Probability

    • Basics of probability

    • Counting principles

    • Random Variables, Expected value, and Variance

  • 9

    Week 8: Data preprocessing and Data Visualisation

    • Data cleaning, filling missing values, standardisation, normalisation, outlier detection

    • Data visualisation with Seaborn

  • 10

    Week 9: Probability Distributions

    • Bernoulli, Binomial, and Poisson distributions

    • Continuous random variable

    • Uniform and normal distributions

  • 11

    Week 10: Data visualisation (continued)

    • Data visualisation with Seaborn

    • Simulating probabilistic events

  • 12

    Week 11: Break

    • Break

  • 13

    Week 12: Sampling and Sampling Statistics

    • Sampling strategies

    • Distribution of sampling statistics (mean, variance, proportion)

    • Central Limit Theorem

  • 14

    Week 13: Sampling with Python

    • Sampling strategies with Python

    • Demonstration of central limit theorem

    • Practice case study

  • 15

    Week 14: Interval Estimation

    • Interval estimation for mean (variance known)

    • Interval estimation for mean (variance unknown)

    • Demonstration in Python

  • 16

    Week 15: Hypothesis testing

    • Anatomy of Hypothesis Testing

    • Type I and Type II Errors

    • Single sample mean with known variance

    • Single sample mean with unknown variance

    • Demonstration in Python

  • 17

    Week 16: Hypothesis testing (continued)

    • Single sample variance

    • Single sample proportion

    • Demonstration in Python

  • 18

    Week 17: Hypothesis testing (continued)

    • Two population mean known variance

    • Two population mean, known variance, small sample

    • Two population mean, known variance, large sample

    • Paired t-test

    • Two population, proportion

    • Demonstration in Python

  • 19

    Week 18: Analysis of variance

    • One factor analysis

    • Two factor analysis

    • Demonstration in Python

  • 20

    Week 19: Linear Regression

    • Model

    • Estimating parameters

    • Measuring goodness of fit

Course begins in

  • 00Days
  • 00Hours
  • 00Minutes
  • 00Seconds

Fee Structure

Driven by our passion for teaching and interest in nation building, 
all PadhAI One courses are offered at very affordable prices.


For students/faculty For professionals
Students enrolled in schools/colleges and faculty members Working professionals and those looking to up-skill
Applicants must provide a valid ID card indicating present affiliation. No pre-requisites
Rs 1,000 + 18% GST for each course Rs 5,000 + 18% GST for each course
Join

Instructions on how to apply for discount 
Join


FAQ

  • What is the time commitment required?

    Each week, we will release 2 to 3 hours of video content. We recommend 2 to 3 hours of self-learning and practice. Thus, a weekly commitment of 4 to 6 hours is required. The duration of the course is for 18 to 20 weeks.
    However, in case you are unable to find this time due to other commitments, you can do the course at your own pace and complete it within any time within one year.

  • Will I get a certificate at the end of the course?

    Yes, if you complete the entire course and finish the assignments, you will receive a certificate from One Fourth Labs. This is digitally signed and can be shared on LinkedIn and other websites.
    Each course in the PadhAI One Data Science series will have a separate certificate.

  • How long will I have access to the course?

    You will have access to the course content (videos, assignments, community) for 1 year from the start of the course.

  • I am interested in Machine Learning, should I still do the Foundations of Data Science course?

    The Foundations in Data Science course focuses on the basics of statistics and Python programming for data science. These fundamentals are required for many job roles.
    Also, in the machine learning course, we will assume a background in these areas. If you are confident about the topics enlisted in the syllabus, then you can directly join the Machine Learning course that begins later this year.

  • Will i get computational resources for doing my projects?

    No, we do not provide any computational resources. The course platform only hosts the video lectures and assignments. All programming assignments and projects will be done on Google Colaboratory, which is a freely available resource. In the course, we provide a tutorial on how to use Google Colaboratory. It is therefore sufficient to have a standard computer and a good internet connection.

  • How can I clear my doubts?

    You will have access to the PadhAI course community where you can post your queries. Dedicated TAs will answer them. You are also encouraged to interact with your peers and learn together.

  • Do you provide any support with finding jobs?

    While data science is a highly sought after job role, we do not provide any placement guarantee or support.