Summary
I introduce a three‑week, all‑practicum deep dive that moves students from spreadsheet work to Python programming using a real college basketball dataset (2015‑2019). Week 1 focuses on Google Sheets (or Excel) where I have them download the .xlsx file from Canvas, upload it to sheets.google.com, unmerge the instruction cell, freeze the header rows, and reorganize the Year and Team columns. The core assignment requires them to create derived columns—Losses, WinPCT (as a decimal), and Performance—using array formulas that I demonstrate step‑by‑step. I explain that the array syntax uses ';' to separate rows and ':' to define ranges, and I stress building formulas manually rather than relying on auto‑complete tools. For WinPCT I apply a single array formula across all 1,759 rows, for Losses I subtract wins from games, and for Performance I use IFS to label >0.80 as “Excellent,” >0.60 as “Good,” and anything else as “Average.” I also have them generate a pivot table to calculate adjusted offensive and defensive efficiencies, then visualise the data with histograms and smooth line charts. Completing this spreadsheet assignment early earns them the remainder of the week off. The next class (Week 2) will shift to introductory Python, but before that I assign the creation of a win‑percentage distribution histogram using the same sheet. This prepares students for the upcoming Python analysis in Week 3, where they will replicate and extend these calculations programmatically. The overall goal is to ensure every learner can import, clean, transform, and visualise a sizable sports dataset in a spreadsheet before transitioning to code‑based analysis.