A biostatistician helps answer health questions with data: does a drug work, is a risk factor real, how many patients does a study need. Most people in the job hold a master's degree (58%) or a PhD (29%), so this roadmap builds from math and programming basics to the core methods (regression, survival, mixed models, trial design) and then to the part that gets you hired: writing analysis plans, coding reproducibly in R or SAS, and explaining results to doctors. There are no legally required licenses. Pharma and CRO jobs often list SAS, and academic and public-health jobs favor R. Learn R first
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Stage 1 of 3 · about 16 weeks
Be comfortable with probability, core statistics, and basic coding so you can analyze a small dataset and explain the result in plain words.
Probability and statistical inference
Every method you will use later (confidence intervals, p-values, regression) rests on probability. If you understand why a sample mean varies, a p-value stops being a magic number.
Calculus and linear algebra essentials
Maximum likelihood, regression and many models use derivatives and matrices. You need working fluency, not a pure-math degree.
R programming and data wrangling
Writing code to analyze data is a core task of the job. R is the main free tool in public health and academia, and it is increasingly used in industry.
Descriptive statistics and basic tests
Before modeling, you must summarize and compare groups correctly. This is the 'Table 1' and first-look work in every study.
Version control and the command line
Reproducible work is expected in regulated and academic settings. Git and Bash appear in the real tool list for this job.
Then 2 more stages (about 84 weeks in all) with lessons, drills, projects, 6 interview questions with strong answers and free resources.