Bayesian methods have grown recently because of their success in solving hard data analytics problems. They are rapidly becoming a must-have in every data scientists toolkit. The course uses a hands-on method to teach you how to use Bayesian methods to solve data analytics problems in the real world. You will understand the principles of estimation, inference, and hypothesis testing using the Bayesian framework. You will also learn to use them to solve problems such as A/B testing, understanding consumer habits, risk evaluation, adjusting machine learning predictions, reliability analysis, detecting the influence of one variable over an outcome, and many others.
By taking this course, you will be able to apply and use Bayesian methods as part of your data analytics toolbox, thus helping you use Python to solve a majority of common statistical problems in data science.
Target Audience
The course is made for data scientists, engineers, and professionals who are constantly working with data in quantitative fields such as Finance, Economics, Operations Research, and more and who want to apply Bayesian methods to solve complex problems and improve their data analysis and decision-making skills. Some knowledge of statistics and Python programming skills is mandatory.
