## Welcome!

We are very excited that you've decided to join our micro-course and become an expert in **Probability and Statistics for Data Science**. The course consists of the following topics:

- Introduction to Probability and Statistics.
- Probability theory and distributions.
- Conditional probability
- Bayes theorem.
- Law of large numbers.
- Central limit theorem.
- Hypothesis testing.

**Course duration:** ~25h

**Prerequisites**

The prerequisite of this course is the basic knowledge of Python, including the syntax and its data types. The exercises are shared through Github, so basic knowledge of GIT is also required.

**By end of this course you will be able to:**

- Understand probability theory behind Machine Learning models.
- Undestand statistical terminology.
- Understand conditional probabilities.
- Apply Law of large numbers and Central limit theorem in real world situations.
- Using hypothesis testing framework.
- Apply all concepts above in Python.

**Mentor support**

If you opted for one of the plans with mentor support, you will be added to our Slack channel. Also, if you selected mentor calls, one of our mentors will contact you and you'll arrange your online meetings at your convenience. If you chose the basic plan but feel like you could use some help throughout the course, just let us know and we'll help you **upgrade**.

**Feedback**

We are trying to always keep improving and make our courses better but that wouldn't be possible without your feedback!

At the end of each section, you will find a feedback form, where we'll ask you to rate the respective section, let us know how much time you spent on it, and add any suggestions, comments, complaints, etc. Any feedback you give us will be highly appreciated!

*Let's get to work!*