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Free Online Statistics Courses

There are quite a few statistics courses available for free online. Most of the courses listed here are introductory and do not require programming, though the topic is introduced in all of them. If you're new to statistics & have the time for regular coursework, any one of these would be a great endeavor.

If you're ready for something a little more advanced, take a look at the available free statistical programming courses.

One thing to note is that these are not degree seeking programs. Some of them provide a certificate upon successful completion, but you won't get college credit. Don't let that dissuade you though, this is excellent instruction free for the taking.


Probability & Statistics at Khan Academy - This one isn't exactly a course, but rather a collection of educational videos on statistics presented by Salman Khan. Go at your own pace, and in any order. This is a comprehensive introduction to the material though, covering everything from mean, median & mode all the way up to confidence intervals & chi-squared tests. There are plenty of practice problems online as well, giving you a chance to apply what you're learning. This is a great place for you to start.

Statistics One at Coursera - This course is taught by Princeton professor Andrew Conway. It begins on September 22, 2013 and will be 12 weeks long with weekly instructional videos and homework assignments. This course introduces the major tenets of statistics and will give you a solid foundation to move forward in statistical programming.

Statistics: Making Sense of Data at Coursera - This course focuses on statistical menthods for data analysis, but also includes sections on R programming for those that are interested. It's taught by Alison Gibbs & Jeffrey Rosenthal of the University of Toronto. While I haven't taken this course, I have taken several Coursera courses and they're very well done. The course has already begun, but the video lectures & discussion forums should remain live for a few more months.

Intro to Statistics at Udacity - Taught by former Stanford professor Sebastian Thrun. This course is a complete introduction to statistics, covering probability, distributions, inference, regression & more. Programming in Python is encouraged, but not required. The teaching here is broken up into small sections, each followed by a quiz to check your understanding. Udacity also has a very active & helpful forum if you get stuck on any one part.

Statistical Thinking for Managerial Decisions at The University of Baltimore - Taught by Dr. Hossein Arsham. This course is different from the others in a couple of respects. While it is an introductory course in statistics, the focus is on the practical application of statistics within the business world. That fact alone makes this material worth considering. Second, the material isn't presented in videos, the format is a collection of web pages that are readable as a book. This is an excellent course on business statistics though, so if you're interested in learning how to apply statistics in your professional life, you'll want to go through this material.

Intro to Statistics: Descriptive Statistics at edX - Taught by University of California at Berkeley professors Philip B. Stark and Ani Adhikari. This is part one of a three part introduction to statistics. The others are Intro to Statistics: Probability & Intro to Statistics: Inference. These three courses comprise a 15 week course broken up into three separate 5-week components. This looks like great material.

Statistics Tutorials at sentimentmining.net - This is a collection of 17 videos introducing the concepts of statistics. The videos are well done and the material moves at a comfortable pace. The videos aren't titled or categorized so it only makes sense to start at the beginning and work your way through. Otherwise this is good material.

Statistics at Udacity - Taught by Sean Laraway and Ronald Rogers. This course is a thorough introduction to statistics, covering both descriptive and inferential statistics. One nice thing about this course is that you have the option of earning college credit for taking it. You'll have to pay a fee, but if you want credit for taking statistics, this could be a good option.

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