Foundations of Data Science (PadhAi)
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May 08, 2023

Foundations of Data Science (PadhAi)

Learn Data Science with PadhAi's Data Science Training Course and become a certified Data Scientist. Discover the foundations of Data Science, the process behind it, and the various related operations such as Collection, Storing, Processing, Describing, and Modelling. This course is suitable for learners familiar with programming and mathematics. Register now for the Foundations of Data Science course and gain valuable insights on statistics, Python, descriptive statistics, Numpy, Pandas, and visualization.

Why Learn Data Science?

By understanding this, you've previously made your most memorable strides on the way the turning into a data scientist. The following are a couple of motivations to stick along!

Data Science is quick becoming one of most sought after callings in India and all over the planet.

More than 1.5 Lakh job openings for Data Scientists projected in 2020, expanding by 62% from 2019.

Data is all over, it is a widespread money. Learning how to acquire experiences from data is a priceless ability to have.

What is Data Science?

Data is the new oil and Data Science is its burning motor! While there are numerous definitions with regards to what data science truly is, we have found it best to portray it as a field spinning around 5 data-related operations.


  • Data Collection is the most common way of get-together data (Mathematical, text, video, sound and so on), affected by two central point specifically, the inquiry that needs to be replied by the data scientist and the climate that the data scientist is working in!


  • Storing data includes keeping up with the gathered data for use during the data science pipeline. Structured data is regularly put away in social databases and amassed in data-stockrooms. With the approach of Huge Data, Data Lakes are currently used to store multimodal structured and unstructured data.


  • Data Processing is a bunch of 3 primary sub-processes. Data Fighting (Extraction, change, and stacking of the data), Data Cleaning (Handling Missing Qualities, Exceptions, and so on) and Data Scaling, Standardization and Standardization.


  • Data Portrayal has two angles. Data Picturing includes addressing handled data utilizing charts, graphs, outlines, and different perceptions. Data Summarisation includes working out different synopsis statistics like the mean, middle, mode, standard deviation, and fluctuation.


  • Statistical Demonstrating of data includes displaying the fundamental data distribution and relations in the data and then, at that point, making derivations on top of the model. Algorithmic demonstrating includes utilizing enormous volumes of data and enhancement strategies to best gauge the distribution and relations of the data, eg Machine Learning and Deep Learning.

Is this the right data science course for me?

In the event that you are know about programming (in any language) and OK with math at twelfth standard (secondary school) level, then, at that point, you ought to have the option to track with this course. This course is appropriate for the accompanying learning goals:

  • Understand the worth of data science and the cycle behind utilizing it.
  • Learn the basics of statistics and likelihood expected for data science.
  • Use Python to accumulate, store, clean, dissect, and envision data-sets.
  • Apply statistical methods to form and test data speculations
  • Apply statistical deduction to reveal connections inside data-sets
  • Understand the job of ML and DL in the data science pipeline
  • Understand genuine difficulties with a few contextual investigations

Your instructors

Mitesh Khapra and Pratyush Kumar

Mitesh and Pratyush are Associate Teachers at the Branch of Software engineering and Engineering at IIT Madras. They have both industry and scholastic involvement with working with deep learning and related regions. They are both energetic about teaching and adding to country building.

Data Science Course curriculum


Guidelines for this course


Data Poll


Week 1: Introduction


Week 2 - Section 1: Engineering Data Science Systems


Week 2 - Section 2 : What is Statistics?


Week 3: Beginning with Python


Blog Challenge - 1


Week 4: Descriptive Statistics (Section 1)


Week 5: Python (proceeded)


Week 6: Descriptive Statistics (Section 2)


Week 7: Descriptive Statistics (Section 3)


Week 8: Numpy


Week 9: Pandas


Week 10: Pandas (proceeded)


Week 11: Representation


Week 12: Representation (Proceeded)


Week 13: Approaching Open ended DS problems


Week 14: Counting


Week 15: Sample spaces and Events


Week 16: Random Variables


Week 17: Distributions and Sampling Strategies


Week 18: Distributions of Sample Statistics


Week 19: Central Limit Theorem


Week 20: Chi Square Distribution


Week 21 : Point and Interval Estimators


Week 22: Hypothesis Testing

Data Science Fee Structure

Driven by our energy for teaching and interest in country building,

all PadhAI One courses are presented at entirely reasonable costs.


For understudies/faculty For experts

Understudies signed up for schools/universities and staff members Working experts and those shifting focus over to up-expertise

Candidates should give a substantial ID card demonstrating present affiliation. No pre-necessities

Rs 1,000 + 18% GST for each course Rs 5,000 + 18% GST for each course


dicating present affiliation. No pre-necessities

Rs 1,000 + 18% GST for each course Rs 5,000 + 18% GST for each course


The PadhAI One Series

This course is important for the full PadhAI One series

Foundations of Data science

Open for enrollments

5 months

Rs 1,000 for understudies


This course

Machine Learning

Not far off!

5 months

Rs 1,000 for understudies


Deep Learning

Open to enlistments

5 months

Rs 1,000 for Students

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