Your spreadsheet is still where most analysis starts — so make it faster. Learn to describe what you want in plain English and let AI write the VLOOKUP, INDEX/MATCH, SUMIFS, or nested IF for you, then explain the formula so you understand it. Clean up the mess you inherit — split names, fix inconsistent dates, flag duplicates, and standardize categories — in a fraction of the usual time. You will also plan pivot-table logic by describing the summary you need instead of dragging fields blind. By the end you turn a raw export into an analysis-ready sheet before your coffee gets cold.

SQL is the gateway to real data, and AI is your fastest way in. Learn to describe the question you actually have — top customers last quarter, month-over-month churn, revenue by region — and get working SQL with the right JOINs, GROUP BY, and filters. Just as important, you will learn to read the query back, sanity-check the row counts, and catch the classic traps: double-counted rows, silent NULLs, and the wrong date range. Then refine by conversation — "exclude refunds," "make it weekly" — until it is exactly right. By the end you pull your own answers without waiting on the data team.

A number nobody can read changes no decisions. Learn to hand your data to AI and get a clear chart fast — then pick the right one on purpose: a line for trends over time, a bar for comparing categories, a scatter for relationships, and when a plain table beats them all. You will practice giving AI the context it needs to label axes, sort sensibly, and drop the chartjunk, plus how to spot a misleading visual (truncated axes, dual scales, cherry-picked ranges). By the end you build visuals that make your insight obvious in five seconds.

The best analysis is worthless if nobody acts on it. Learn to feed AI your findings — the numbers, the charts, the "so what" — and get a clear narrative report with a tight executive summary up top. Practice writing for the audience: a one-paragraph headline for leadership, the supporting detail for peers, and the caveats an honest analyst always includes. You will learn to keep AI anchored to your real data (no invented figures), match the tone to your company, and turn a monthly refresh into a template you reuse. By the end your reports get read, understood, and acted on.

One-off answers are useful; a repeatable workflow is a superpower. Learn to chain everything together — clean, query, chart, summarize — into a reliable path you can run again next week or next month without starting from scratch. You will build reusable prompt templates for recurring reports, learn where a human must stay in the loop to catch AI mistakes, and set up simple checks so bad data never sneaks into a decision. See how to document a workflow so a teammate can run it too. By the end you stop reinventing your analysis every cycle and ship insight on a schedule.