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

    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:

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

    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 sensitivity-specificity and predictive values of medical tests, confidence intervals, medical vs. statistical significance, and chi-square, Student's t and ANOVA F-tests, including multiple comparisons.

    Aim of Course:

    This course covers sampling methods and analyses used to study of the density and abundance of animals and plants, and other important biological variables.

    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 will teach you how to choose an appropriate time series
    forecasting method, fit the model, evaluate its performance, and
    use it for forecasting.

    Aim of Course:

    After successfully completing this course, you will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation.

    Aim of Course:


    This course will explain meta analysis - the methods that are used to assess multiple statistical studies on the same subject and draw conclusions.

    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 will offer an introduction to sample size and power analysis and will show how to use it simply and effectively to plan the appropriate sample size for a study.

    Aim of Course:

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

    Aim of Course:

    The purpose of this online course is to teach you how to extract data from a relational database using SQL, so that you can perform statistical operations.  The focus is on structuring queries to extract structured data (not on building databases or methods of handling big data).

    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:

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

    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.