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

04/30/20201 Min Read — In Data Science, Machine Learning

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:


  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