New: Roadmaps ordered paths through our cheat sheets and flashcards, so you always know what to study next.
Explore themSee what's new on GitHubFrom your first VLOOKUP to a Tableau dashboard your manager trusts.
A 14-step learning path. Follow it in order, or jump to what you need.
This path is for anyone who wants the entry-level toolkit that lands a first data analyst job, whether you're coming from admin work, a different degree, or just spreadsheets you already know. Plan on about 6 to 9 weeks at a few hours a week, moving from Excel and SQL through statistics, data validation, and dashboard storytelling in Tableau. It does not go deep on Python, machine learning, or inferential statistics, that ground belongs to the Data Scientist path, and it treats Power BI and enterprise semantic models as optional side trips rather than a destination, since the Excel to Power BI and BI Analyst paths cover that ground in full. By the end you can query and clean a real dataset with SQL, build a Tableau dashboard someone else can open on their own, and turn what you found into a story a manager will act on.
No prior experience needed. Start from zero.
Open this first: you can build a working pivot table today, and it's the spreadsheet fluency every later step, from SQL exports to Tableau dashboards, assumes you already have.
Excel only reaches one file at a time; SQL is how step 1's tabular thinking extends into the databases where most real company data actually lives.
Push past the SELECT and JOIN basics from step 2 into window functions and CTEs, the analyst-grade SQL that ranks, runs totals, and compares periods without exporting anything to Excel first.
You can build a working pivot table in Excel and pull exactly the rows you need with SQL, no more waiting on someone else to hand you a spreadsheet. Next up: turning those numbers into something you can actually trust.
Finish this section to unlock.
+100 XP
This is where a column of numbers starts meaning something: expect to come back to mean versus median and standard deviation more than once before spotting a skewed distribution feels automatic.
Pulling clean-looking rows with the SQL from step 3 isn't the same as trusting them; this is the validation habit, catching duplicates, bad joins, and biased samples, that the field now rates as one of its fastest-growing skills.
Zoom out from single queries and spreadsheets to how a whole company's reporting fits together: the KPIs, data models, and dashboards steps 7 onward build on.
You can size up a distribution with real statistics and catch the duplicates and bias that make a dataset lie, and a few minutes of due flashcards keeps step 1's Excel formulas sharp while you build on them. Next up: putting what you now trust in front of people.
Finish this section to unlock.
+100 XP
Learn which chart actually answers a question versus which one just looks busy, the perception principles that decide whether the dashboard you build in step 9 gets read or ignored.
Combine the chart choices from step 7 into a single screen someone checks every morning: layout, KPI placement, and refresh timing are what separate a dashboard from a pile of charts.
Put steps 7 and 8's design principles into a real tool: drag-and-drop fields, calculated fields, and a published dashboard a stakeholder can open without you in the room.
You can choose the right chart, lay it out on a dashboard that reads at a glance, and publish it in Tableau for someone else to open. Next up: picking a specialty and closing the loop.
Finish this section to unlock.
+100 XP
Take this if you're weighing a move toward heavier statistical work: hypothesis testing and confidence intervals go past step 4's descriptive measures into the inferential territory the Data Scientist path covers in full.
Choose this route if your workplace runs on Microsoft's stack instead of Tableau's: the same dashboard instincts from step 8, expressed through DAX and Power Query instead.
Pick this option if you'd rather build BI inside Excel than adopt a separate tool: Power Query and Power Pivot turn step 1's spreadsheet into a real data model, and the Excel to Power BI path picks up exactly here if you want to go further.
A dashboard from step 9 answers questions for people already looking at it; storytelling is how you get the people who aren't looking to act on what it found.
Pull every earlier step into one pass: query the data with step 2 and 3's SQL, summarize it with step 4's statistics, trust it with step 5's cleaning habits, and present it the way steps 7 through 13 taught, the full loop a working data analyst repeats every week.
You can query the data, clean it, size it up statistically, visualize it, and tell a story a manager will act on, the complete loop the Data Analyst badge certifies.
Finish this section to unlock.
+100 XP
Finish every required step, at least 70% of them genuinely done (not skipped), to earn this badge and 500 XP.