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 is designed to give you an introduction to the algorithms, techniques and software used in natural language processing (NLP).

    Aim of Course:

    In this online course you will learn you how to apply predictive modeling methods, and persuasion (uplift) models in particular.  The focus will be on targeting voters in political campaigns.

    Aim of Course:

    This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data.

    Aim of Course:

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

    Aim of Course:

    This course covers spatial data handling and mapping using QGIS software.  You will learn what you can expect to do with QGIS, and what not to expect.  You will also learn how to bring in data, join data from different sources, and how to edit data in QGIS.

    Aim of Course:

    This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.

    Aim of Course:

    Participants in this course will learn why Bayesian computing has gained wide popularity, and how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using WinBUGS software.

    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 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 teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.

    Aim of Course:


    This course covers the issues that need to be addressed in order for a study to produce statistically valid conclusions. The first session covers different study designs, with an emphasis on designs that can be used with observational data (clinical trials are reviewed briefly, but those seeking a more in depth treatment should take the Institute's sequence of courses in clinical trial design and analysis). Students will learn what types of bias and extraneous factors can endanger studies, and how to avoid or adjust for them.

    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:

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

    Aim of Course:

    This course will teach a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks.

    Aim of Course:

    This course covers clinical trial designs including randomized controlled trials, ROC curves, CI and tests for relative risk and odds ratio, and an introduction to survival analysis.

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

    Aim of Course:

    This course will introduce the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text.

    Aim of Course:

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