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

**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.
**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.
**Linear Algebra** : pretty much everything under this topic.
Miscellaneous : some topics from here and there like . Information Theory and Game Theory.

### b) Programming Language:

**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 .
**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.
**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 -

**Courses**:

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

**Further Readings:**

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

**Youtube Channels:**

- Two Minute Papers
- 3Blue1Brown
- Primer
- Numberphile