Hands-On Bayesian Methods with Python - Udemy

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.

About the Author

Colibri Digital is a technology consultancy company founded in 2015 by James Cross and Ingrid Funie. The company works to help its clients navigate the rapidly changing and complex world of emerging technologies, with deep expertise in areas such as Big Data, Data Science, Machine Learning, and Cloud Computing. Over the past few years, they have worked with some of the world's largest and most prestigious companies, including a tier 1 investment bank, a leading management consultancy group, and one of the World's most popular soft drinks companies, helping each of them to better make sense of its data, and process it in more intelligent ways. The company lives by its motto: Data -> Intelligence -> Action.

Rahul Tiwari trains and consults organizations and individuals on Business Analytics, Data Science, and Machine Learning (Using R and Python). For 12 years, he has been helping students and organizations in various domains (such as retail, telecom, life sciences, finance, and more) solve their business problems using Data Science, Business Analytics, and Machine Learning. He has implemented machine learning algorithms in R extensively. He worked on various classification and regression models for his clients using R and Python. He has a sound knowledge of statistics as well, which is very much necessary for Data Science projects.

After starting his career 12 years back in data warehousing, he moved on to the Data Science domain and held various roles. Mostly working with CTOs, key IT decision makers, and students, he has always focused on building capacity, knowledge, and solutions in Data Science, Business Analytics, and Machine Learning.

He is a certified Tableau and Teradata associate. His core expertise is in R, Python, Tableau, Power BI, and SQL.