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

Data Science Basics and Programming in R

• R for Data Science 2e by Garrett Grolemund, Hadley Wickham, and Mine Çetinkaya
• Elements of Data Analytic Style by Jeff Leek
• Fundamentals of Data Visualization by Claus O. Wilke

Softare Engineering and Programming in R

• Hands-On Programming with R by Garrett Grolemund

Data Engineering

• Scaling Up With R and Arrow by Nic Crane, Jonathan Keane, and Neal Richardson
• DuckDB in Action by Mark Needham, Michael Hunger, and Michael Simons
• Fundamentals of Data Engineering by Joe Reis and Matt Housley
• SQL Antipatterns: Avoiding the Pitfalls of Database Programming by Bill Karwin

Data Management

• Data Quality ROI: A Playbook for Business-Driven Data Quality by Gaurav Patole
• Meeting the Challenges of Data Quality Management by Laura Sebastien-Coleman
• Statistical Data Cleaning with Applications in R by Mark van der Loo and Edwin de Jonge
• The Data Warehouse Toolkit 3e by Ralph Kimball and Margy Ross
• DAMA-DMBOK: Data Management Body of Knowledge by DAMA International

Analytics and Data Mining

• Profiting from Your Forecasting Software by Paul Goodwin
• Introduction to Modern Statistics 2e by Mine Cetinkaya-Rundel and Johanna Hardin