Last September I started work as a data specialist for a local toy company. It’s been fun, but busy enough that I held off on beginning my grad program until after Christmas, and then until after end of year reporting and retrospectives, and now that I’ve been promoted (wee!) I’m postponing again to sidequest some material in data engineering, causal modeling, forecasting, and regression.

The curriculum I’ve already planned out is ultimately relevant to my position, but in a high-octane, theoretically supercharged kind of way. Right now what I need is to round out my fundamentals.

So, with that in mind, I present my sidequest!

Sidequest Business Curriculum

• R for Data Science by Garrett Grolemund, Hadley Wickham, and Mine Çetinkaya
• Fundamentals of Data Engineering by Joe Reis and Matt Housley
• DAMA-DMBOK: Data Management Body of Knowledge by DAMA International
• SQL Antipatterns: Avoiding the Pitfalls of Database Programming by Bill Karwin
• Tidy Modeling by Max Kuhn and Julia Silge
• Causal Inference in R by Malcolm Barrett, Lucy D’Agostino McGowan, and Travis Gerke
• Forecasting Principles and Practice 3e by Rob J. Hyndman and George Athanasopoulos
• Text Mining with R: A Tidy Approach by Julia Silge and David Robinson
• Beyond Multiple Linear Regression by Paul Roback and Julie Legler

Written on: April 10, 2025