15 Free Courses to Master Statistics for Data Science

Introduction to Statistics (Udacity) This course covers the basics of statistics, including descriptive statistics, probability, inferential statistics, and hypothesis testing.

Introduction to Statistics (Coursera) This course is similar to Udacity's Introduction to Statistics course, but it also covers some additional topics, such as correlation and regression.

Intro to Descriptive Statistics (Udacity) This course focuses on descriptive statistics, which is used to summarize and describe data.

Intro to Inferential Statistics (Udacity) This course covers inferential statistics, which is used to draw conclusions about a population based on a sample of data.

Statistics for Data Science with Python (IBM) This course teaches you how to use Python to perform statistical tests and interpret the results of statistical analyses.

The Analytics Edge (MIT OpenCourseware) This course covers a variety of topics related to data analysis, including statistics, probability, and machine learning.

Essential Mathematics for Data Science (University of Washington) This course covers the essential math skills you need for data science, including linear algebra, calculus, and probability.

Statistics with Applications in R (Johns Hopkins University) This course teaches you how to use the R programming language to perform statistical analysis.

Introduction to Mathematical Statistics (Massachusetts Institute of Technology) This course is a more advanced course in statistics that covers topics such as probability theory, estimation, and hypothesis testing.

Statistical Learning (Stanford University) This course is another advanced course in statistics that covers topics such as linear regression, classification, and dimension reduction.

Probabilistic Graphical Models (Massachusetts Institute of Technology) This course covers probabilistic graphical models, which are a powerful tool for representing relationships between variables.

Introduction to Bayesian Statistics (University of California, Berkeley) This course covers Bayesian statistics, which is a type of statistics that allows you to incorporate prior knowledge into your analysis.

Machine Learning (Andrew Ng) This course is a classic course on machine learning that covers a variety of topics, including supervised learning, unsupervised learning, and reinforcement learning.

Deep Learning Specialization (DeepLearning.AI) This specialization teaches you how to build and train deep learning models.

Natural Language Processing with Deep Learning (DeepLearning.AI) This course teaches you how to use deep learning to process natural language.