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Statistics & Probability Courses and Certifications

Learn Statistics & Probability, earn certificates with paid and free online courses from Harvard, Stanford, MIT, University of Pennsylvania and other top universities around the world. Read reviews to decide if a class is right for you.

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• With free certificate (34)
• University course only (557)
• Beginner (363)
• Intermediate (195)
• Advanced (42)
• < 30 mins (144)
• 30 - 60 mins (352)
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• English (1916)
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Estadística Aplicada a los Negocios

Usa estadística descriptiva e inferencial en los negocios. Aprende a tomar decisiones con ayuda de estimadores, análisis regresional y más.

• 5 weeks, 5-6 hours a week
• Free Online Course (Audit)

Probability - The Science of Uncertainty and Data

Build foundational knowledge of data science with this introduction to probabilistic models, including random processes and the basic elements of statistical inference -- Part of the MITx MicroMasters program in Statistics and Data Science.

• 16 weeks, 10-14 hours a week

Intro to Statistics

Get ready to analyze, visualize, and interpret data! Thought-provoking examples and chances to combine statistics and programming will keep you engaged and challenged.

• Free Online Course

Statistical Learning with R

Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R. Course material updated in 2021 for second edition of the course textbook.

• 11 weeks, 3-5 hours a week

Fundamentals of Statistics

Develop a deep understanding of the principles that underpin statistical inference: estimation, hypothesis testing and prediction. -- Part of the MITx MicroMasters program in Statistics and Data Science.

• 17 weeks, 10-14 hours a week

Basic Statistics

Explore statistical concepts, from descriptive methods to inferential techniques. Learn to calculate, interpret, and apply statistics using real-world examples and free software.

• 1 day 2 hours 45 minutes

Intro to Descriptive Statistics

Learn essential concepts of descriptive statistics, including data visualization, central tendency, variability, standardization, and sampling distributions, to analyze and interpret data effectively.

Fat Chance: Probability from the Ground Up

Increase your quantitative reasoning skills through a deeper understanding of probability and statistics.

• 7 weeks, 3-5 hours a week

Understanding Clinical Research: Behind the Statistics

If you’ve ever skipped over`the results section of a medical paper because terms like “confidence interval” or “p-value” go over your head, then you’re in the right place.

• 1 day 3 hours 11 minutes

Python and Statistics for Financial Analysis

Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data.

• 12 hours 49 minutes

Intro to Inferential Statistics

Intro to Inferential Statistics will teach you how to test your hypotheses and begin to make predictions based on statistical results drawn from data!

Statistical Inference and Modeling for High-throughput Experiments

A focus on the techniques commonly used to perform statistical inference on high throughput data.

• 4 weeks, 2-4 hours a week

Introduction to Probability

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

• 10 weeks, 5-10 hours a week

Introduction to Statistics

You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.

• 14 hours 31 minutes

Probabilistic Graphical Models

Explore probabilistic graphical models, a powerful framework for encoding complex probability distributions, with applications in machine learning, medical diagnosis, and more.

• 17 weeks, 11 hours a week
• Paid Course

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Browse Course Material

Course info, instructors.

• Dr. Jeremy Orloff
• Dr. Jennifer French Kamrin

Departments

• Mathematics

As Taught In

• Discrete Mathematics
• Probability and Statistics

Learning Resource Types

Introduction to probability and statistics, course description.

This course provides an elementary introduction to probability and statistics with applications. Topics include basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.

These same course materials, including interactive …

These same course materials, including interactive components (online reading questions and problem checkers) are available on MIT’s  Open Learning Library , which is free to use. You have the option to enroll and track your progress, or you can view and use the materials without enrolling.

You are leaving MIT OpenCourseWare

When Predictions Fail: Crash Course Statistics #43

When Predictions Succeed: Crash Course Statistics #44

War: Crash Course Statistics #42

Neural Networks: Crash Course Statistics #41

Statistics in the Courts: Crash Course Statistics #40

Big Data Problems: Crash Course Statistics #39

Intro to Big Data: Crash Course Statistics #38

Unsupervised Machine Learning: Crash Course Statistics #37

Supervised Machine Learning: Crash Course Statistics #36

Fitting Models is Like Tetris: Crash Course Statistics #35

ANOVA Part 2: Dealing with Intersectional Groups: Crash Course Statistics #34

ANOVA: Crash Course Statistics #33

Regression: Crash Course Statistics #32

The Replication Crisis: Crash Course Statistics #31

P-Hacking: Crash Course Statistics #30

Chi-Square Tests: Crash Course Statistics #29

Degrees of Freedom and Effect Sizes: Crash Course Statistics #28

T-Tests – A Matched Pair Made in Heaven: Crash Course Statistics #27

Test Statistics: Crash Course Statistics #26

Bayes in Science and Everyday Life: Crash Course Statistics #25

You Know I’m All About that Bayes: Crash Course Statistics #24

Playing with Power: P-Values, Part 3: Crash Course Statistics #23

P-Value Problems: Crash Course Statistics #22

How P-Values Help Us Test Hypotheses: Crash Course Statistics #21

Confidence Intervals: Crash Course Statistics #20

The Normal Distribution: Crash Course Statistics #19

Z-Scores and Percentiles: Crash Course Statistics #18

Randomness: Crash Course Statistics #17

Geometric Distributions and The Birthday Paradox: Crash Course Statistics #16

The Binomial Distribution: Crash Course Statistics #15

Probability, Part 2 – Updating Your Beliefs with Bayes: Crash Course Statistics #14

Probability, Part 1 – Rules and Patterns: Crash Course Statistics #13

Science Journalism: Crash Course Statistics #11

Henrietta Lacks, the Tuskegee Experiment, and Ethical Data Collection: Crash Course Statistics #12

Sampling Methods and Bias with Surveys: Crash Course Statistics #10

Controlled Experiments: Crash Course Statistics #9

Correlation Doesn’t Equal Causation: Crash Course Statistics #8

The Shape of Data: Distributions: Crash Course Statistics #7

Plots, Outliers, and Justin Timberlake: Data Visualization, Part 2: Crash Course Statistics #6

Charts Are Like Pasta – Data Visualization Part 1: Crash Course Statistics #5

Measures of Spread: Crash Course Statistics #4

Mean, Median, and Mode: Measures of Central Tendency: Crash Course Statistics #3

Crash Course Statistics Preview

Mathematical Thinking: Crash Course Statistics #2

What Is Statistics?: Crash Course Statistics #1

Intro to Statistics

Get ready to analyze, visualize, and interpret data! Thought-provoking examples and chances to combine statistics and programming will keep you engaged and challenged.

Last Updated March 7, 2022

Prerequisites:

No experience required

Course Lessons

2. looking at data, 3. scatter plots, 4. bar charts, 5. pie charts, 6. programming charts (optional), 7. admissions case study, problem set 1: visualization, 8. probability, 9. conditional probability, 10. bayes rule, 11. programming bayes rule (optional), 11a. probability distributions, 12. correlation vs. causation, problem set 2: probability, 13. estimation, 14. averages, 15. variance, 16. programming estimators (optional), problem set 3: estimators, 17. outliers, 18. binomial distribution, 19a. central limit theorem, 19. central limit theorem programming (optional), 20. the normal distribution, 21. manipulating normals, 22. most better than average, problem set 4, 23. sebastian's weight and proofs (optional), 24. confidence intervals, 25. normal quantiles, 26. hypothesis test, 27. hypothesis test 2, 28. programming tests and intervals (optional), problem set 5: inference, 29. regression, 30. correlation, 31. monty hall problem (optional), problem set 6: regression and correlation, 32. weight case studies, 33. flash crash example, 34. challenger example, taught by the best.

Sebastian Thrun

Founder and Executive Chairman, Udacity

As the Founder and Chairman of Udacity, Sebastian's mission is to democratize education by providing lifelong learning to millions of students worldwide. He is also the founder of Google X, where he led projects including the Self-Driving Car, Google Glass, and more.

The Udacity Difference

Combine technology training for employees with industry experts, mentors, and projects, for critical thinking that pushes innovation. Our proven upskilling system goes after success—relentlessly.

Demonstrate proficiency with practical projects

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, Intermediate

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Stanford Online

Introduction to applied statistics.

Stanford School of Humanities and Sciences

Statistical tools for modern data analysis can be used across a range of industries to help you guide organizational, societal and scientific advances. This course uses industry-standard applications and software (R and Python) for numerical reasoning and predictive data modeling, with an emphasis on conceptual rather than theoretical understanding.

Topics Include

• Correlated errors
• Data snooping
• Interactions and qualitative variables
• Multiple linear regression
• Penalized regression
• Regression and prediction
• Simple linear regression
• Transformations
• Variance and cross-validation

Prerequisites

• A conferred Bachelor’s degree with an undergraduate GPA of 3.3 or better
• An introductory statistical methods course
• Recommended: introduction to statistical methods: precalculus (STATS60), statistical methods in engineering and the physical sciences ( STATS110 ), biostatistics (STATS141) or equivalents

What You Need To Get Started

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Once you have enrolled in a course, your application will be sent to the department for approval. You will receive an email notifying you of the department's decision after the enrollment period closes. You can also check your application status in your my stanford connection account at any time.

Learn more about the graduate application process .

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Statistics and R

An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.

Associated Schools

Harvard T.H. Chan School of Public Health

What you'll learn.

Random variables

Distributions

Inference: p-values and confidence intervals

Exploratory Data Analysis

Non-parametric statistics

Course description

We will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.

Given the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.

Instructors

Rafael Irizarry

Michael Love

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Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor.

Advanced Bioconductor

Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data.

Join our list to learn more

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Locales in the Area

Tsentralniy region.

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• Location: Krasnodar Krai and Adygea , Southern Russia , Russia , Eastern Europe , Europe
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