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:

    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:

    After taking this course, you will be able to install and run rjags, a program for Bayesian analysis within R.  Using R and rjags, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data.

    Aim of Course:


    This course covers a number of advanced topics in optimization. Students taking this course will learn to specify and implement optimization models that solve network problems (what is the shortest path through a network, what is the least cost way to route material through a network with multiple supply nodes and multiple demand nodes).

    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:

    This course covers the analysis of data gathered in surveys.

    Aim of Course:

    After taking this course, you will be equipped to introduce natural language processing (NLP) processes into your projects and software applications.

    Aim of Course:

    This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications.

    Aim of Course:

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

    Aim of Course:

    In this online course, “R Programming Intro 1,” you will be introduced to basic concepts in computer programming via R - it is for those who have had little or no experience in programming.

    Aim of Course:

    The goal of this course is to introduce the basics of programming in Python, on either Windows or Mac.  You will use both Jupyter notebooks and standard script editors, and work through simple arithmetic operations, statistical operations, variables, keywords, lists, arrays, and dictionaries.  You'll use conda to install modules and close with some data visualizations.

    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:

    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:

    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.

    Aim of Course:

    This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. The course will include an integration of supervised and unsupervised learning techniques.

    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, 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:

    After taking this course, participants will be able to design appropriate conjoint and choice studies, using surveys, panels, and designed experiments. They will also be able to analyze and interpret the resulting data.

    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:

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