Introduction to Probability
Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty.
This course will give you the tools needed to understand data, science, philosophy, engineering, economics, and finance.
5-10 hours per week
What You'll Learn
Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you the tools needed to understand data, science, philosophy, engineering, economics, and finance. You will learn not only how to solve challenging technical problems, but also how you can apply those solutions in everyday life.
With examples ranging from medical testing to sports prediction, you will gain a strong foundation for the study of statistical inference, stochastic processes, randomized algorithms, and other subjects where probability is needed.
The course will be delivered via edX and connect learners around the world. By the end of the course, participants will understand:
- How to think about uncertainty and randomness
- How to make good predictions
- The story approach to understanding random variables
- Common probability distributions used in statistics and data science
- Methods for finding the expected value of a random quantity
- How to use conditional probability to approach complicated problems
Your Instructors
Joseph Blitzstein
Professor of the Practice in Statistics, Harvard University
Joe Blitzstein is Professor of the Practice in Statistics at Harvard University, where has taught since 2006, after completing his Ph.D. in Mathematics (with a masters in Statistics) at Stanford University, advised by Persi Diaconis. He is originally from Los Angeles, California, and did his undergraduate studies in Mathematics at the California Institute of Technology. At Harvard, he has taught a wide range of undergraduate and graduate probability and statistics courses, including the popular statistics class Stat 110, which provides a comprehensive introduction to probability as a language and framework that can be applied wherever there is data, randomness, or uncertainty. Stat 110 has grown to over 500 on campus students per year at Harvard. With Professor Hanspeter Pfister from Computer Science, Joe also launched Harvard's first course in data science in 2013. Joe’s main research interests are in statistical inference for networks, “big data”, and other complex data structures.
Ways to take this course
When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.
A Verified Certificate costs $209 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate.
Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.