We offer 100+ courses in introductory and advanced statistical subjects.
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Available courses
Aim of Course:
This course covers how to read, understand, manipulate, and use data. There is no prerequisite knowledge for this course, but it does require access to Excel.
Aim of Course:
This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
Aim of Course:
This course will teach you how to build and monitor a machine learning pipeline, given a predictive model that has been provided by a data science team.
- Teacher: Andrew Stewart
- Teacher: Saul Yeaton
- Teacher: Ramzi Ziade
Aim of Course:
This course covers the crafting of survey questions, the design of surveys, and different sampling procedures that are used in practice. Longstanding basic principles of survey design are covered, and the impact of the trend toward increased respondent resistance is discussed.
- Teacher: Anthony Babinec
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.
- Teacher: Anuja Kulkarni
Aim of Course:
To provide an easy introduction to statistical inference for a single variable.
- Teacher: Meena Badade
Aim of Course:
This course will show you how to use R to create statistical models and use them to analyze data.
- Teacher: Shweta Jadhav
- Teacher: Poonam Tribhuwan
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.
- Teacher: Abhaya Indrayan
Aim of Course:
This course will introduce you to the basic ideas of Bayesian Statistics. You will learn how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.
- Teacher: Bill Bolstad
Aim of Course:
This course covers key multivariate procedures such as multivariate analysis of variance (MANOVA), principal components, factor analysis and classification.
- Teacher: Robert LaBudde