Introduction to Probability: Data and Uncertainty
Master the foundations of probability theory to understand data, randomness, and uncertainty in this Harvard course.
Master the foundations of probability theory to understand data, randomness, and uncertainty in this Harvard course.
Dive into the world of probability with Harvard's comprehensive course. Learn to navigate uncertainty and randomness, gaining essential tools for data analysis, science, engineering, economics, and finance. From medical testing to sports predictions, you'll develop a strong foundation in statistical inference and stochastic processes. This course empowers you to solve complex problems and apply probability concepts to real-world scenarios.
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What you'll learn
Develop critical thinking skills for handling uncertainty and randomness
Learn techniques for making accurate predictions based on probability
Master the story approach to understanding random variables
Explore common probability distributions used in statistics and data science
Understand methods for calculating expected values of random quantities
Apply conditional probability to solve complex problems
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 8 modules in this course
This course provides a comprehensive introduction to probability theory and its applications. It covers fundamental concepts such as uncertainty and randomness, probability distributions, expected values, conditional probability, and Markov chains. The curriculum is designed to build a strong foundation for further study in statistical inference, stochastic processes, and randomized algorithms. Through diverse examples from medical testing to sports prediction, students learn to apply probability concepts to real-world scenarios.
Unit 0: Introduction, Course Orientation, and FAQ
Module 1
Unit 1: Probability, Counting, and Story Proofs
Module 2
Unit 2: Conditional Probability and Bayes' Rule
Module 3
Unit 3: Discrete Random Variables
Module 4
Unit 4: Continuous Random Variables
Module 5
Unit 5: Averages, Law of Large Numbers, and Central Limit Theorem
Module 6
Unit 6: Joint Distributions and Conditional Expectation
Module 7
Unit 7: Markov Chains
Module 8
Fee Structure
Instructor
1 Course
Distinguished Statistics Educator and Probability Expert
Professor Joseph K. Blitzstein serves as Professor of the Practice in Statistics and Co-Director of Undergraduate Studies at Harvard University, where he has transformed statistical education since 2006. After completing his PhD in Mathematics and MS in Statistics from Stanford University under Persi Diaconis, and his undergraduate studies at Caltech, he has become one of Harvard's most influential educators. His signature course, Statistics 110: Introduction to Probability, has grown from 80 to over 500 students annually, becoming a cornerstone of Harvard's quantitative education. He co-authored the widely-used textbook "Introduction to Probability" and pioneered Harvard's first data science course in 2013. Beyond his teaching achievements, which include winning the Fannie Cox Prize for Excellence in Science Teaching and being voted "Favorite Professor" in the Harvard College Yearbook for 11 consecutive years, Blitzstein's research focuses on statistical inference for networks and complex data structures. A chess expert who advises the Harvard Chess Club, he has earned particular recognition during the pandemic for his innovative teaching methods and dedication to student support. His expertise spans probability theory, data science, and statistical education, making complex concepts accessible while maintaining rigorous academic standards
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4.4 course rating
40 ratings
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