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:

    This course covers the crafting of survey questions, the design of surveys, and different sampling procedures that are used in practice. Longstanding basic principles of survey design are covered, and the impact of the trend toward increased respondent resistance is discussed.

    Aim of Course:

    This course covers how to develop and implement data science projects in a responsible and ethical way. It includes a review of predictive modeling, but some prior familiarity is helpful.

    Aim of Course:

    This course covers key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis and classification.

    Aim of Course:


    Rasch analysis constructs linear measures from scored observations, such as responses to multiple-choice questions, Likert scales and quality-of-life assessments. This course covers the practical aspects of data setup, analysis, output interpretation, fit analysis, differential item functioning, dimensionality and reporting.

    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 covers how to apply linear programming to complex systems to make better decisions – decisions that increase revenue, decrease costs, or improve efficiency of operations.

    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:

    In this online course, “Predictive Analytics 1 - Machine Learning Tools - with R,” you will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. In both cases, predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. In classification, the target variable is categorical ("purchased something" vs. "has not purchased anything").

    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:

    In this online course, “Predictive Analytics 1 - Machine Learning Tools - with Python,” you will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.