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Introduction to Probability
About this courseSkip About this course
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
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 Transcripts: اَلْعَرَبِيَّةُ, Deutsch, Español, Français, हिन्दी, Bahasa Indonesia, Português, Kiswahili, తెలుగు, Türkçe, 中文
- Associated skills: Finance, Forecasting, Medical Testing, Algorithms, Stochastic Process, Statistics, Prediction, Economics, Engineering Economics, Probability, Data Science, Statistical Inference
What you'll learnSkip What you'll learn
- 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
More about this courseSkip More about this course
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