I have talked with almost hundred or more over Data Science Learner’s over the topic – **Best Ways to Learn Probability for Data Science** .As you know every individual is different. Hence ,They have their own strategies as well. Still I found a big confusion over this topic . Specially probability is a big topic to cover .Hence I prepared a combined approach for Best Ways to Learn Probability for Data Science . I am really excited to share with you . This article is completely all about it . I will request you to stay with it till end . As I tried my best to keep it short and interesting for you –

## Three Phases to Learn Probability for Data Science**–**

There are actually three phases which you should follow to understand the probability for data science.

- Fundamental Concept.
- Numerical Exercise /Case Studies.
- Programming Approach for Probability.

### 1. Fundamental Concept –

In this phase, we all need to revise our academics knowledge of probability. As we all are from a mathematics background . Hence we must have read probability in our schools and college. Some of us still remember it but some of us may have forgot . There are few books which may help to finish all of the concepts at a place.

1.An Introduction to Probability Theory and its Applications, Vol 1, 3ed (WSE)

2.Probability: For the Enthusiastic Beginner 1st Edition

### 2. Numerical Exercise /Case Studies –

There is no replacement of practicals in science and maths. In the same way, this section and followed by the coming section is completely based on practicals and practice. Here you may choose two strategy to do these exercises and questions.

2.1 – Solve the exercise book of a dedicated book on probability which must be of engineering level . This will give you consolidated approach of all topics at a place .

2.2- Here you can start with your school days text books and solve only the probability and related chapters of it .Trust me it is only seeming you a big task but really it is not .Once you read them in childhoods , Topics created some knowledge dots . But when you revise them . It will connect those dots and help you to understand them in data science context .

### 3. Programming Approach for Probability-

Python has very strong packages for Maths and scientific analysis . Like scipy and numpy etc .This will help you to perform underline calculation while probability estimation. Apart from this in order to visualize the distribution function , you may use matplotlib and seaborn etc .

## Conclusion –

This intent to create this article –**Best Ways to Learn Probability for Data Science** is to introduce you with all three phases with their importance.T he biggest mistake people commit is to ignore some phases. See all are equally important. I have seen most of the data scientist aspirants start from last ( programming phase). Please do not do that. Giving some attention and revising the basic concepts from books is really important. See in school we learn how to solve any probabilistic problem. Here we will learn how to convert real-world problems into probability formula or equation.

**Thanks**

**Data Science Learner Team**

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