data-analysis
6 free lessons tagged data-analysis across Business, Science. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
The Five Analyses That Answer Most Business Questions
Almost every business question reduces to one of a small number of analytical shapes. This lesson covers trend, breakdown, funnel, cohort and distribution, what each is good for, how each misleads, and what you may and may not conclude from any of them.
Pivot Tables, Joins, and Not Breaking Your Spreadsheet
The handful of spreadsheet techniques that cover most business analysis, and the specific ways they fail silently. Covers pivot tables, lookups and what a join actually does, structuring data so tools can use it, and the errors that produce plausible wrong answers.
Asking a Question the Data Can Actually Answer
Most analysis fails at the start, on a question too vague to answer or one the data cannot address. This lesson covers turning a business question into a measurable one, what your data was actually collected for, and the checks that come before any calculation.
Start From the Decision, Not the Data
Most data presentations fail before a chart is drawn, because they are organised around what the analyst found rather than what the audience must decide. This lesson covers how to identify the actual decision, why the analysis order is the wrong presentation order, and what an audience needs to act.
How Scientists Use AI: Tools Across the Research Cycle
AI touches nearly every stage of research, from reading the literature to analyzing data to writing the paper. Learn the categories of AI tools scientists use, the crucial difference between general assistants and specialized scientific AI like protein-structure predictors, and the caveats, hallucinated citations and reproducibility, that make rigor essential.
AI for Literature Review, Data Analysis, and Coding
A practical guide to the research workflows where AI helps most. Learn how to use AI to search and synthesize the literature (and verify it), write and debug analysis code even without being a programmer, run and interpret statistics, and draft scientific writing, all with the verification and reproducibility discipline that keeps the work rigorous.