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:

    In this course you will work through a customer analytics project from beginning to end, using R.  You will start by gaining an understanding of the problem and the context, and continue to clean, prepare and explore the relevant data.

    In this course, you'll learn everything you need to get you started using Python for data analysis. - See more at: http://www.statistics.com/course-catalog/python/#syllabus

    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:

    This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

    Aim of Course:

    This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

    Aim of Course:

    This course will introduce you to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining.

    Aim of Course:

    This course extends the Bayesian modeling framework to cover hierarchical models, and to add flexibility to standard Bayesian modeling problems.

    Aim of Course:

    In this course, you will learn how to make decisions in building a factor analysis model - including what model to use, the number of factors to retain, and the rotation method to use.

    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 fundamental concepts and theory of Structural Equation Modeling: model specification, model identification, model estimation, model testing, and model modification.

    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:

    In this course, you'll learn everything you need to get you started using Python for data analysis

    In this course, you'll learn everything you need to get you started using Python for data analysis. - See more at: http://www.statistics.com/course-catalog/python/#syllabus

    Aim of Course:

    Regression, perhaps the most widely used statistical technique, estimates relationships between independent (predictor or explanatory) variables and a dependent (response or outcome) variable.

    Aim of Course:

    This course will introduce basic concepts in computer programming via R - it is for those who have had little or no experience in programming.

    Aim of Course:

    This course covers modeling technique making decisions in the presence of risk or uncertainty. Specific topics include risk analysis using Monte Carlo simulation for risk simulation, queuing theory for problems involving waiting lines, and decision trees for analyzing problems with multiple discrete decision alternatives.

    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:

    To provide an easy introduction to statistical inference for a single variable.

    Aim of Course:

    This course describes how the safety of clinical trials in the pharmaceutical industry is assured through Data and Safety Monitoring Committees (DMC's).

    Aim of Course:

    This course will explain the basic theory of linear and non-linear mixed effects models, including hierarchical linear models. It will outline the algorithms used for estimation, primarily for models involving normally distributed errors, and will provide examples of data analysis. The course aims at providing a basic understanding and knowledge of the mixed effect models that will allow you to use them in practice.

    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.