Best Online Statistics Courses – 10 Top Picks

Whether you simply love numbers or are aspiring to take the data science field by storm, the study of statistics is highly important, essential, and beneficial to students. When you’re collecting data from thousands of sources for your thesis, statistical tools help you collate, analyze and make sense out of seemingly random figures. A crucial subject offered in all universities, statistics help make discoveries, decisions and accurate predictions. 

With the increasing popularity and relevance of the subject, it comes as no surprise that almost all online educational portals are offering online statistics courses, some of them truly excellent and equivalent to any full-time course that a university may offer. Additionally, studying online lets you go at your own pace, according to your own schedule, access more resources faster and pay only a fraction of the costs you would pay at a university. This also makes them a great option for those who are working and are looking to further their skill sets simultaneously. So what are the best online statistics courses? Let’s take a look.

Best Online Statistics Courses

Learning statistics can seem daunting, there’s a lot of knowledge to gain. That’s where taking an online course comes in. Here’s a round up of the best online statistics courses today.

Statistics for Data Science and Business Analysis—Udemy 

A highly popular platform for online learning, Udemy offers this statistics course geared towards data science and business analysis, designed by 365 Careers. Students of this course can look forward to building a strong foundation in the subject, enabling them to understand and apply different statistical tools in real life. Some of the topics covered include basics such as regression analysis, central tendency measuring, variability, and asymmetry, as well as more advanced ones such as plotting various types of data, learning how to work with various data types, calculating covariance and correlation. 

Statistics with R Specialization—Coursera 

No list of online courses is complete without Coursera, the largest MOOC platform in the world. To add to the allure, all courses on the platform are free to read and review (unless you want a certificate of completion). 

This course is a specialization, meaning that it’s made up of a series of courses and includes a hands-on project—akin to a proper online degree! This specialization focuses on the analysis and visualization of data in R, covering a range of topics such as creating analysis reports, Bayesian statistical models and an in-depth study of statistical inference, to name a few. 

The course is designed for beginners and takes around 7 months to complete; however, users can opt to just do the individual courses offered in the specialization. 

Statistics for Data Science Micromasters—edX 

edX’s Micromasters program caters to those who are looking for masters’ degrees but cannot commit to full-time learning at a university. This program, geared towards those interested or looking for careers in data science, covers machine learning, statistics, foundational principles of data science, analysis of big data and predictions using statistical models. Completing the 5 graduate-level courses in this program makes students eligible for careers such as data analyst, data engineer, data scientist and business intelligence analyst. 

The course, offered by the Massachusetts Institute of Technology, takes 1 year and 4 months to complete, with at least 10-14 hours of study recommended per week. 

Learn Statistics with NumPy—Codecademy

A highly popular Python library, NumPy is a blessing when it comes to calculating vast amounts of data and descriptive statistics without having to start at ground zero each time. Students doing this course can learn how to do basic functions like adding, subtracting, selecting and creating arrays in NumPy, calculating descriptive statistics such as ranges, medians and means, normal and binomial distributions and creating histograms. Additionally, it’s only 5 hours long, making it a great option for those looking for a quick-yet-comprehensive course. 

Though there are no prerequisites to the course, it is recommended that students be familiar with very basic Python.

A Crash Course in Causality: Inferring Causal Effects from Observational Data—Coursera 

An excellent content-specific course that delves into causality, ‘A Crash Course in Causality’ is an intermediate-level course offered by the University of Pennsylvania. With 13 hours divided into 5 units spread over 5 weeks, students learn about the definition of causal effects, the required data and models and implementing and analyzing commonly-used statistical methods; students also get to apply their learnings to R. By the completion of the course, students will have learned to differentiate between causation and association, treatment weighing, matching, and instrument variables and formulate assumptions with causal graphs. 

Introduction to Statistics—Stanford School of Engineering 

A course that comes from one of the most renowned institutes in the world, this beginner-level course takes students through those concepts that are important data analysis and communication of insights. From exploratory data analysis to learning key sampling principles to selecting the right tests of significance in a range of circumstances, a strong foundation will be built to enable students to pursue more advanced studies in the field, especially in machine learning and statistical thinking. 

The course takes approximately 6 to 8 hours to complete and requires students to have a basic understanding of productivity software and computers. 

The Science of Decisions—Udacity 

Offered by the San Jose State University, this beginner-level course is divided into descriptive and inferential statistics lessons and is great for those seeking a detailed look into statistical research methods, data description, normal distribution analysis, mean comparison, non-parametrics, regression and correlation. 

The self-paced course contains 6 lessons and takes around 4 months to complete, with many interactive quizzes and rich content taught by industry experts. 

Business Statistics and Analysis Specialization—Coursera

This 5-course specialization, offered by Rice University, imparts basic understanding of the tools and techniques required in business data analysis, important spreadsheet functions, building business data measures and data modeling. Students will also be taught the basic tenets of probability, linear regression, and how to analyze data to make important business decisions. 

Since it is a specialization, there’s a capstone project included. This beginner-level course takes approximately 5 months to complete. 

Workshop in Probability and Statistics—Udemy 

This online workshop covers probability, regression, sampling and decision analysis and is a great option for both beginners and intermediate-level learners. The course is created and taught by George Ingersoll, an associate dean at the UCLA Anderson School of Management. The workshop covers basic probability and statistics. 

It is recommended that students doing this course have a basic understanding of algebra and Microsoft Excel. 

Statistics and Probability—Khan Academy 

Last but not least on the list is this extremely comprehensive course on statistics and probability from Khan Academy! The course covers the ‘A to Z’s of the subject, from comparing and summarizing quantitative data to modeling data distribution to advanced regression and analysis of variance (ANOVA). The course is self-paced and the institute makes it fun by offering challenges and points based on your mastery of the subjects. 

To Conclude: Learning Statistics

Whatever your reasons for learning statistics are and whether you’re looking for comprehensive courses or those centered on particular topics in the subject, there is a range of sites and courses on the net to cater to your demands. Additionally, most of them are free and quite excellently structured, so learning depends on your willingness, pace and schedule. 

Remember, just because they’re great courses, doesn’t mean you’ll end up learning statistics as a default; your own effort matters, too. Put in the recommended learning hours for each course to see the best results!