Available courses

Item Response Theory

An introduction to the mechanics and applications of Item Response Theory, a popular technique of estimating a latent variable (e.g., proficiency levels) from survey or testing data.

Introduction to Quantitative Social Science

A great place to start building a solid foundation! This course introduces you to basic concepts in quantitative social science studies. Topics include:

  • Discussion of what the social sciences are and how they differ from natural sciences and humanities
  • A brief history of the social sciences
  • An introduction to how theories underpin our empirical designs and our interpretations of our quantitative data
  • A presentation of introductory descriptive statistics
  • A discussion of contemporary issues in the social sciences


Inferential Statistics

This course examines how we can apply the basic statistical building blocks, measures of central tendency and variance, to data samples in order to draw inferences about larger populations. Topics include:

  • Frequentist vs. Bayesian Inference
  • Confidence intervals and p-values
  • Threats to valid inference making (e.g., "p-hacking", multiple testing)
  •  Extending inferences to real-world decision making

Statistics II: Under the hood

Let's take a closer look in order to fill in the missing gaps! Topics include: 

  • Loose ends: a closer look at degrees of freedom and unbaised estimators
  • Solving for regression coefficients
  • OLS and ML estimation techniques
  • Dealing with violations of regression assumption
    • Non-parametric inference
    • Hierarchical linear models
  • ANOVA and how it relates to regression

Building Skills: R

This course uses a task-based approach to developing R programming skills. No programming experience necessary.

Measurement in the Social Sciences

What good are statistical models if you can't trust your data? This course discusses the concept of measurement -- what it is and what it isn't. Various factor analytic and Rasch measurement models are introduced, as well as how to apply them to real data in the R programming environment. 

Interactive Online Analysis with R

An introduction to how you can share your R analyses online. A focus on creating interactive dashboards and web apps. Specific tools that are introduced:

  • R Markdown
  • R Shiny
  • R Plumber
  • R Serve
  • R Apache

Introduction to Data Science

Data Science aims to solve real-world problems using quantitative datasets. It incorporates statistics, research methods, and computer programming.

An introduction to collecting and exploring data, statistical inference and hypothesis testing, building and testing models, machine learning, and presenting research findings. This course takes a constructivist approach where students are challenged to integrate the scientific concepts and statistical tools presented in class into a critical research article on the topic of their choice. Students are encouraged to decide for themselves how content is applicable for investigating their specific research questions and sharing their results in a convincing way. Weekly guidance will be provided by the instructors as well as classmates. Students are holistically assessed on the effectiveness of their final analysis, the extent to they communicate the discoveries and challenges of the research process in a series of blog posts, and their participation in the class and R programming lab sessions.


Web 101

Do you use the internet every day for your job and personal life yet have no idea how it works. Does this make you uncomfortable? If so, this is the course for you! 

Join us to discover how to the entire world somehow manages to display in your web browser! The course format of "Getting Comfortable with the Internet" is more exploratory and social than most that you will find online. We are a small group, and there is an emphasis on sharing and learning from our own misconceptions and uncertainties.