Text courseintermediate34 min4 chaptersEnglish
Data and Analysis With AI
A written course in four chapters on getting trustworthy numbers out of messy data. Cleaning without silently losing rows, asking questions that change a decision, charts that state a finding, and the specific ways AI gets numbers wrong.
Instructor: HIMEXA Editorial
What you'll learn
- Clean data with an inspectable script instead of an opaque cleaned file
- Turn vague curiosity into a question with a comparison, a population and a window
- Pick chart forms that match the comparison and title them with the finding
- Catch prose arithmetic, invented benchmarks and causal claims from correlational data
- Run the checks that catch a wrong number before it reaches a slide
Chapters
Read in order, or jump to the chapter you need. Nothing is locked — this course is free to read.
- Getting Data Into a Shape You Can Trust9 min readCleaning is most of analysis, and it is where AI helps most and can quietly destroy the most. How to use it without losing rows you never notice.
- Asking Questions Worth Answering8 min readThe quality of an analysis is set by the question, not the technique. How to move from vague curiosity to something answerable, and how to avoid confirming what you already believed.
- Charts That Tell the Truth8 min readGenerated charts default to decoration. How to choose the right form, what quietly misleads, and how to write a title that says the finding.
- Where AI Gets Numbers Wrong9 min readThe specific failure modes to check for: arithmetic done in prose, causal language from correlational data, invented context, and confidence that does not track accuracy.
Requirements
- Comfortable with spreadsheets; no programming required
- A dataset of your own to practise on — even a messy export