Free Online Variance Courses & MOOCs

Coursera

Stanford University

Introduction to Statistics (Coursera)

Statistics & Data Analysis Data Science

Dive into Stanford University's renowned 'Introduction to Statistics' course on Coursera. This comprehensive program is designed for learners who want to develop a strong foundation in understanding and interpreting data. You'll explore key concepts such as statistical thinking, exploratory data analysis, sampling techniques, and significance testing. Whether you're new to statistics or looking to enhance your analytical skills, this course will equip you with the tools necessary to make informed decisions based on data.

Jun 15th 2026

5-12 Weeks

Statistics Analysis Probability

Coursera

University of Amsterdam

Inferential Statistics (Coursera)

Statistics & Data Analysis Data Science

Dive into Inferential Statistics, a comprehensive Coursera course that teaches you how to draw accurate conclusions about populations using statistical analyses of samples. Learn the principles of significance testing and apply various statistical tests to diverse datasets.

Jun 8th 2026

5-12 Weeks

Statistics Social Sciences Data Analysis

Stepik

Bioinformatics Institute

Introduction to Statistics (Stepik)

Statistics & Data Analysis

Embark on a four-week journey into the fundamentals of statistics with our 'Introduction to Statistics' course on Stepik. Whether you're new to data analysis or looking to refine your skills, this course offers an in-depth exploration of key statistical methods and concepts that are crucial for interpreting and analyzing data effectively.

Self-paced

Self-Paced

Statistics Data Analysis Linear Regression

Probability and Statistics II: Random Variables – Great Expectations to Bell Curves (edX)

EdX

Georgia Institute of Technology,GTx

Probability and Statistics II: Random Variables – Great Expectations to Bell Curves (edX)

Statistics & Data Analysis

Dive into the world of random variables and explore essential concepts like probability distributions, expectations, variances, and how they apply to real-world scenarios. This course is designed for those who want to deepen their understanding of statistics and probability theory, preparing them for advanced studies or practical applications in data analysis.

Self Paced

Self-Paced

Statistics Probability Variance