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Biostatistics

Biostatistics: experimental design, hypothesis testing, regression, survival analysis, longitudinal models, Bayesian inference and causal methods applied to clinical, epidemiology and lab data.

RStansurvival analysismixed models
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Default learning track

4 phases · ~16–22 weeks

A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.

1

Phase 1 — Foundations

3–4 weeks 30–40 hours

Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.

Prerequisites

  • Comfortable with basic biology / health-science vocabulary
  • Ability to install software and use a terminal

Skills you'll build

Python or R basicsLinux shellGit/GitHubReading scientific literature

Hands-on projects

Reproducible analysis notebook

Walk through a published tutorial in R + tidyverse + Stan and reproduce its results on the provided sample data.

Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README

Milestones

  • Set up reproducible Conda/Mamba environment and a public GitHub repo
  • Read and summarize 3 review papers covering the sector landscape
  • Complete an intro statistics or scripting course end-to-end
Citation · course
Biostatistics in Public Health Specialization
Coursera · Johns Hopkins
Why: Exact sector match (Biostatistics)
Dataset · tool
survival R package
CRAN
Why: Exact sector match (Biostatistics)
2

Phase 2 — Core tools & datasets

4–6 weeks 60–80 hours

Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.

Prerequisites

  • Completed Phase 1 reproducible notebook
  • Working Python/R environment

Skills you'll build

R + tidyverse + StanData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on NHANES + open clinical-trial data

Pick one study from NHANES + open clinical-trial data, reproduce the headline result, and write a short technical note on what you found.

Deliverable: GitHub repo + 3-page PDF write-up

Milestones

  • Run the official R + tidyverse + Stan tutorial end-to-end on real data
  • Download and explore one full dataset from NHANES + open clinical-trial data
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · course
PH525x — Data Analysis for Life Sciences
edX · HarvardX
Why: Exact sector match (Biostatistics)
Dataset · dataset
NHANES — National Health and Nutrition Examination Survey
US CDC
Why: Exact sector match (Biostatistics)
3

Phase 3 — Applied projects

6–8 weeks 80–120 hours

Move from tutorials to original analyses on real questions. Start showing your work publicly.

Prerequisites

  • Phase 2 dataset deep-dive complete
  • Comfortable with the sector's primary tooling

Skills you'll build

End-to-end pipeline designStatistical interpretationScientific writingReproducible reports

Hands-on projects

End-to-end biostatistics analysis

Pick a real public health dataset, pre-register an analysis plan, fit regression/survival/Bayesian models and report results reproducibly.

Deliverable: Quarto report + GitHub repo with pre-registered SAP

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with the R-Ladies, Bioconductor and Stan communities (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · book
OpenIntro Statistics (free textbook)
OpenIntro
Why: Exact sector match (Biostatistics)
Dataset · tool
survival R package
CRAN
Why: Exact sector match (Biostatistics)
4

Phase 4 — Portfolio & career launch

3–5 weeks 30–50 hours

Package your work, target real roles, and prepare to interview in the sector.

Prerequisites

  • Phase 3 capstone project shipped
  • 2+ public repos under version control

Skills you'll build

Technical CVPortfolio siteInterview prep (case studies + technical questions)Networking

Hands-on projects

Job-ready portfolio package

Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.

Deliverable: Live portfolio URL + PDF CV + cover letter template

Milestones

  • Publish a portfolio site or pinned GitHub README linking to 2–3 projects
  • Tailor CV to 3 real job ads in the sector and submit applications
  • Practice 5 mock technical interviews with sector-specific case studies
Citation · book
Modern Statistics for Modern Biology
Holmes & Huber
Why: Exact sector match (Biostatistics)
Dataset · dataset
NHANES — National Health and Nutrition Examination Survey
US CDC
Why: Exact sector match (Biostatistics)

Curated learning resources

Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.

Courses

Biostatistics in Public Health Specialization
Coursera · Johns Hopkins
PH525x — Data Analysis for Life Sciences
edX · HarvardX
Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

Stan documentation & tutorials
Stan Project
Git & GitHub fundamentals
GitHub Docs

Tools

survival R package
CRAN

Datasets

NHANES — National Health and Nutrition Examination Survey
US CDC

Books

OpenIntro Statistics (free textbook)
OpenIntro
Modern Statistics for Modern Biology
Holmes & Huber
R for Data Science (2e)
Hadley Wickham et al.

Communitys

International Biometric Society
IBS

Typical career paths

Biostatistician

Designs studies and analyses data for clinical trials, cohort studies and lab experiments.

Statistical Programmer

Implements analyses in R/SAS for regulated clinical and epidemiology projects.

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