Real-world data is rarely clean, complete, or ready to analyze. This course prepares students to close the gap between raw data and reliable insight. Using Excel as the primary tool, students develop hands-on skills in data wrangling, exploratory analysis, and introductory modeling. You will learn not just how to run an analysis, but how to interpret what it means and for whom the findings matter.

In interactive Zoom sessions, the course moves through progressively complex data challenges, from structured spreadsheets to messier, real-world datasets drawn from multiple contexts. Equal emphasis is placed on technical execution and critical interpretation: students learn to ask whether their findings are meaningful, whether their methods are appropriate, and what assumptions are embedded in their analysis.

An hour of asynchronous work each week will be spent on case studies, applied exercises, and a capstone data project, in which students will practice turning incomplete, imperfect data into clear, defensible conclusions. By the end of the course, students will be equipped to approach unfamiliar datasets with confidence and communicate findings to both technical and non-technical audiences.

Anticipated Credit Equivalencies:

4 - Introduction to Data Analytics