AI For Data Analysis

If you already work with data in Excel, Google Sheets, or SQL, AI tools can dramatically accelerate every part of your workflow — from cleaning messy datasets to generating insights that would have taken hours to surface manually. This four-session course is designed for working professionals who want to move beyond the basics and start using AI practically: writing better queries with AI assistance, automating repetitive data tasks, building analysis frameworks, and turning raw data into clear, communicable findings. Less time staring at cells, more time actually thinking about what the data means.

From Data Wrangler to Insights Generator — Faster

Most professionals who work with data spend a disproportionate amount of time on the prep work: cleaning messy imports, fixing formatting inconsistencies, building repetitive formulas, and generating the same summary reports week after week. AI tools can handle much of this work — and this course teaches you exactly how to put them to use. Designed for adults who already have working familiarity with Excel, Google Sheets, or SQL, AI for Data Analysis focuses on the practical integration of AI into real workflows, not introductory concepts.

AI-Assisted Data Cleaning and Preparation

The first thing most AI can help you do is the most tedious: cleaning data. Students learn how to use AI tools to identify and correct formatting inconsistencies, handle missing or null values, deduplicate records, and standardize messy imports. They also explore how to use AI to generate data preparation code — Python or SQL — for more complex transformations, dramatically reducing the time between "raw data" and "ready to analyze."

Smarter Querying and Formula Building

Writing a complex SQL query or a deeply nested Excel formula from scratch is slow and error-prone. AI tools can draft these for you from a plain-language description — and more importantly, explain what they're doing so you can modify and verify the output. Students practice writing precise AI prompts for query and formula generation, learning how to get reliable output and how to troubleshoot when the results aren't quite right.

From Analysis to Insights to Communication

Data analysis doesn't end with a spreadsheet — it ends with a decision or a communication. The course closes with a focus on using AI to generate summaries, narratives, and visualizations from completed analyses: turning a table of numbers into a paragraph a stakeholder can act on. Students also develop a critical framework for AI output verification — because AI will sometimes confidently generate plausible-sounding analysis that's factually incorrect, and knowing how to catch those errors is as important as knowing how to generate the analysis in the first place.