A statistician turns messy numbers into trustworthy answers. Most hired statisticians have a master's degree, so this roadmap builds the same foundation for free: calculus and linear algebra, probability, inference and regression, then programming in R/Python and SQL, then experimental design, sampling, and communication. You will not need to buy anything. Each stage ends with a portfolio project that mirrors real work: cleaning data, choosing a valid method, and explaining the result to a non-expert. Take it one stage at a time and check yourself with exercises as you go. By the end you will
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Every stage from beginner to expert, with lessons, practice drills, projects, interview questions and free resources. Free with a Rolearch account.
Stage 1 of 3 · about 14 weeks
Understand probability and descriptive and inferential statistics well enough to explain a confidence interval and a p-value in plain words, and do basic analysis in code.
Math prerequisites: calculus and linear algebra
Distributions are areas under curves (integrals) and regression is matrix algebra. Without these, formulas feel like magic; with them, you can see where they come from.
Probability and distributions
Statistics is probability run backward: you see data and infer the process behind it. You need to know the main distributions to pick sensible models.
Descriptive and inferential statistics
Nearly every task on the job starts here: summarize data, then judge whether a difference is real or noise.
Programming in R or Python for data
Employers expect you to work reproducibly in code. Excel is useful, but scripts can be rerun, checked, and shared.
Then 2 more stages (about 60 weeks in all) with lessons, drills, projects, 6 interview questions with strong answers and free resources.