What is machine learning and data science? What will you encounter while learning these skills.

In the last few months, several people asked me this question “What are machine learning and data science?”

My usual crisp response before any explanation : It [machine learning and data science] is a lot of mathematics and a little bit of programming.

Indeed, explanation is required, especially to those who want to make a lucrative career in Machine learning and Data Science.

Firstly, to pursue any of these two paths you undoubtedly need at least 70% understanding of the following topics:

a) Topics in mathematics

  1. Probability : Combinatorics , Probability Rules & Axioms , Bayes’ Theorem, Random Variables, Variance and Expectation, Conditional and Joint Distributions , Moment Generating Functions, Maximum Likelihood Estimation (MLE), Prior and Posterior , Maximum a Posteriori Estimation (MAP) and Sampling Methods.
  2. Statistics : Measures of central tendency, Spread of the data and Standard Distributions (Bernoulli, Binomial, Multinomial, Uniform and Gaussian). Multivariant Calculus: Differential and Integral Calculus, Partial Derivatives, Vector-Values Functions , Directional Gradient and Hessian, Jacobian, Laplacian and Lagrangian Distribution.
  3. Linear Algebra : pretty much everything under this topic. Miscellaneous : some topics from here and there like . Information Theory and Game Theory.

b) Programming Language:

  1. Python is important for making career in Machine learning. Few libraries and tools which will help you are pytorch,scikit learn,numpy,pandas,tensorflow and seaborn .
  2. R is For Data science as a career. R is a better choice, but not enough. You are also required to learn few tools like : Tableau , Microsoft Power BI.
  3. MATLAB or Octave : For Research based field MATLAB and Octave make more sense. These tools allow you to test your hypothesis.

Data Science vs Machine Learning?

In terms of mathematics:

(there is a link available below this article for details.)

In terms of programming language:

It is all about which path you want to choose ML Engineer , Data Scientist or a Researcher.

How much time to invest?

The ideal requirement is 15 hours a week to learn. The following table as per your understanding level further categorise the required time to invest:

Links to some important -


  1. Introduction to Artificial Intelligence by Udacity.
  2. Machine learning on Coursera (Offered by Stanford)

Further Readings:

  1. Mathematics behind Machine Learning — The Core Concepts you Need to Know

Youtube Channels:

  1. Two Minute Papers
  2. 3Blue1Brown
  3. Primer
  4. Numberphile