Introduction

Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists. 

In this course, you will learn important concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of certain securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability. 

We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing are likely occuring due to an experimental method or to chance.

Note that statistical inference, the next course in this series, builds upon probability theory.

What you'll learn:

  • Important concepts in probability theory including random variables and independence
  • How to perform a Monte Carlo simulation
  • The meaning of expected values and standard errors and how to compute them in R
  • The importance of the Central Limit Theorem

This course is part of the HarvardX Data Science Professional Certificate program:

Meet The Faculty

Rafael Irizarry

Rafael Irizarry

Professor of Biostatistics, T.H. Chan School of Public Health

Rafael Irizarry is a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health and a Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Institute. For the past 15 years, Dr. Irizarry’s research has focused on the analysis of genomics data. During this time, he has also has taught several classes, all related to applied statistics. Dr. Irizarry is one of the founders of the Bioconductor Project, an open source and open development software project for the analysis of genomic data. His publications related to these topics have been highly cited and his software implementations widely downloaded.

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