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Introduction to Probability

Learn probability, an essential language and set of tools for understanding data, randomness, and uncertainty.

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129,273 already enrolled! After a course session ends, it will be archived.
Starts Nov 29
Estimated 10 weeks
5–10 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

About this course

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Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you 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.

At a glance

  • Institution: HarvardX
  • Subject: Data Analysis & Statistics
  • Level: Intermediate
  • Prerequisites:

    Familiarity with U.S. high school level algebra concepts; Single-variable calculus: familiarity with matrices. derivatives and integrals.

    Not all units require Calculus, the underlying concepts can be learned concurrently with a Calculus course or on your own for self-directed learners.

    Units 1-3 require no calculus or matrices; Units 4-6 require some calculus, no matrices; Unit 7 requires matrices, no calculus.

    Previous probability or statistics background not required.

  • Language: English
  • Video Transcript: English

What you'll learn

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  • 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
  • Unit 0: Introduction, Course Orientation, and FAQ
  • Unit 1: Probability, Counting, and Story Proofs
  • Unit 2: Conditional Probability and Bayes' Rule
  • Unit 3: Discrete Random Variables
  • Unit 4: Continuous Random Variables
  • Unit 5: Averages, Law of Large Numbers, and Central Limit Theorem
  • Unit 6: Joint Distributions and Conditional Expectation
  • Unit 7: Markov Chains

About the instructors

More about this course

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