Probability for Data Science

Probability theory is important in data science as we often want to use data to make some predictions about the future. It is also fundamental for many machine learning algorithms.

Learn about probability in this four-part course where we will cover:

• Conditional probability
• Bayes theorem
• Bayesian inference
• Bayesian decision making
• Bayesian regression

The course consists of four lessons and four labs, for a total of 16 hours. There are also four assignments in the course, which will form part of your professional portfolio.

We take a learn-by-doing approach and will focus on writing code in python rather than study equations. Because this is more efficient and more relevant for your day to day work.

You will receive a certificate upon successful completion of the course and assignments.

Who should take this course?

• Aspiring Data Scientists or Machine Learning engineers without a base in probability theory
• Developers who want a career in Data Science
• Anyone who wants a thorough understanding of probability and Bayes theorem.

What do you get?

• Personalized training
• Learn common libraries for working with data
• Non-alcoholic drinks and light snacks will be served
• Professional portfolio
• Certificate of Completion

Prerequisites

• Working knowledge of Python with numpy and pandas libraries.
• You need to bring your own laptop (Windows/Linux/macOS), you will not need to install anything on your laptop.
• You need access to a free Google account (such as Gmail)