courses

where can I find things?

Art by @allison_horst

Most of the course materials are on the dedicated course website. For many classes, there is also a textbook (typically available online for free). All solutions (HW, exams, etc.) will be posted on Canvas.

DS 002R: Foundations of Data Science in R

Foundations of Data Science in R is a first course in data science focused on all aspects of the data science pipeline.

Data Sources

TidyTuesday A weekly data project aimed at the R ecosystem. I’ve written some suggestions and included a lot of resources for getting started with TidyTuesday

ID1: Statistics in the World

Headline: ‘Company Charged with Gender Bias in Hiring.’ Is the company biased? How can we tell? What do we measure? The research supporting the headline is probably less definitive than you’d expect. In this course, we will investigate the practical, ethical, and philosophical issues raised by the use of statistics and algorithmic thinking in realms such as medicine, sports, the law, genetics, and economics. We will explore issues from the mainstream media (newspapers, webpages, TV) as well as scientific articles in peer-reviewed journals. To do all of this, we will consider a wide range of statistical topics as well as encountering a range of uses and abuses of statistics in the world today.

Math 057: Thinking with Data

Thinking with Data is a general audience statistics courses taught as part of the Inside-Out Prison Exchange Program. With half of the students coming from the Claremont Collegea and half coming from the California Rehabilitation Center, students learn statistical ideas side-by-side.

Math 058B: Introduction to Biostatistics

Introduction to Biostatistics is a first course in statistics focused on topics and data found in the life sciences. No biological background is needed, but interest in the life sciences is important.

Math 150: Methods in Biostatistics

Methods in Biostatistics is a second course in statistics focused on topics and data found in the life sciences. No biology background is needed, but interest in the life sciences is important.

Math 151: Probability Theory

Math 151 is an introduction to probability for students with a calculus background and no prerequisites of probability or statistics. Though the course will be focused on the theoretical aspects of the material, there will be some real world applications in class and in the homework assignments. The idea is to have a strong mathematical understanding of the concepts while also understanding how the concepts are applied in the real world.

Math 152: Statistical Theory

Statistical Theory is the course where the Introductory Statistics concepts are developed using first principles, probability theory, calculus, and linear algebra. Optimality of procedures is derived and discussed: which measure of ‘best’ should be used to evaluate a method?

Math 154: Computational Statistics

Computational Statistics can be thought of as an advanced data science course. We will work with many different types of data to become fluent in wrangling, visualizing, simulating, modeling, predicting, and most importantly communicating results.

Math 158: Linear Models

Linear models is a second course in statistics which extends a basic statistical linear model in many ways. The mathematics in the course moves quickly, and there will also be extended focus on good modeling practices and interpretation.

SDS 261: Data Science, the SQL

Data Science, the SQL is a second course in data science focused on using SQL in the data science pipeline.