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:

    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:

    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 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:

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

    This course provides an introduction to the Python programming language for the complete beginner.  We will set up a Python programming environment from download to writing programs, taking small steps along the way.  The programming environment will feature the Jupyter notebook interface.

    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, 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 will cover the analysis of contingency table data (tabular data in which the cell entries represent counts of subjects or items falling into certain categories).

    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:

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

    Aim of Course:

    The aim of the course is to give you the skills to work with a variety of data types and data sources in R.

    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 covers the application of Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

    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!