The Institute for Statistics Education at             Statistics.com

is the leading provider of online education in statistics.
We offer 100+ courses in introductory and advanced statistical subjects.
Students from around the world study with leading authorities via private discussion boards
on flexible schedules.
Teaching assistants provide individual responses in practical exercises.
Statistics.com confers CEU's, Records of Completion, and Certificates in advanced statistical study.

    Available courses

    Aim of Course:

    You will have a chance to try the first week of our course “Predictive Analytics 1 - Machine Learning Tools.” You will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. The full course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction.

    Aim of Course:

    Try out our introductory statistics series for one week - you can learn stats!

    Aim of Course:

    This course covers the basic statistical principles in the design and analysis of randomized controlled trials.

    Aim of Course:

    This course, the second in a three-course sequence, provides an easy introduction to inference and association through a series of practical applications, based on the resampling/simulation approach.

    Aim of Course:

    This course covers sensitivity-specificity and predictive values of medical tests, confidence intervals, medical vs. statistical significance, and chi-square, Student's t and ANOVA F-tests, including multiple comparisons.

    Aim of Course:

    After successfully completing this course, you will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation.

    Aim of Course:

    This course will provide the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.

    Aim of Course:

    This course will show you how to use R to create statistical models and use them to analyze data.

    Aim of Course:

    In this online course, "Programming 2:Python," you'll learn everything you need to get you started using Python for data analysis.  We'll review basic Python skills and data structures, move on to how to load data from different sources, rearrange and aggregate it, and finally how to analyze and visualize it to create high-quality products.

    Aim of Course:

    This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

    Aim of Course:

    This course continues the work of Predictive Analytics 1, and introduces you to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

    Aim of Course:

    This course will show you how to use R to create statistical models and use them to analyze data.

    Aim of Course:

    This course will show you how to use R to create statistical models and use them to analyze data.

    Aim of Course:

    The aim of this course is to teach R Programming to those with little or no programming knowledge or experience.

    Aim of Course:

    In this online course, "Programming 2:Python," you'll learn everything you need to get you started using Python for data analysis.  We'll review basic Python skills and data structures, move on to how to load data from different sources, rearrange and aggregate it, and finally how to analyze and visualize it to create high-quality products.

    Aim of Course:

    This course is about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software.

    Aim of Course:

    To provide an easy introduction to ANOVA and multiple linear regression through a series of practical applications.

    Aim of Course:


    This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.

    Aim of Course:


    In this online course, you will learn about the rapidly evolving field of Deep Learning.  At the end of this course you will understand the basic concepts underlying the representations and methods in deep learning and see some applications where deep learning is most effective.

    Aim of Course:

    This is an introductory epidemiology course that emphasizes the underlying concepts and methods of epidemiology.