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Clinical Trials & Pharmacovigilance Informatics

Trial and post-market safety informatics: CDISC SDTM/ADaM standards, EDC workflows, statistical programming for submissions, and pharmacovigilance signal detection on FAERS / EudraVigilance.

CDISCSDTMADaMFAERS
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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/Python + CDISC + openFDA FAERS 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 · documentation
CDISC standards (SDTM / ADaM)
CDISC
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)
Dataset · dataset
openFDA — FAERS API
FDA
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)
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/Python + CDISC + openFDA FAERSData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on openFDA FAERS + synthetic CDISC SDTM datasets

Pick one study from openFDA FAERS + synthetic CDISC SDTM datasets, 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/Python + CDISC + openFDA FAERS tutorial end-to-end on real data
  • Download and explore one full dataset from openFDA FAERS + synthetic CDISC SDTM datasets
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · documentation
EudraVigilance — EMA pharmacovigilance
EMA
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)
Dataset · tool
R Consortium pharmaverse
pharmaverse
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)
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

Disproportionality signal-detection notebook

Compute PRR/ROR/IC signals on a FAERS slice for a chosen drug class and write a short PV-style assessment.

Deliverable: Reproducible repo + signal table + 4-page PV report

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with PHUSE and ISoP (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · course
Coursera · Design & Interpretation of Clinical Trials
Coursera · Johns Hopkins
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)
Dataset · tool
DisProportionality Analysis (PRR/ROR) — openEBGM
CRAN
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)
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 · documentation
ICH Good Clinical Practice (GCP) E6
ICH
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)
Dataset · dataset
openFDA — FAERS API
FDA
Why: Exact sector match (Clinical Trials & Pharmacovigilance Informatics)

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

Coursera · Design & Interpretation of Clinical Trials
Coursera · Johns Hopkins
Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

CDISC standards (SDTM / ADaM)
CDISC
EudraVigilance — EMA pharmacovigilance
EMA
ICH Good Clinical Practice (GCP) E6
ICH
Git & GitHub fundamentals
GitHub Docs

Tools

R Consortium pharmaverse
pharmaverse
DisProportionality Analysis (PRR/ROR) — openEBGM
CRAN

Datasets

openFDA — FAERS API
FDA

Books

R for Data Science (2e)
Hadley Wickham et al.

Communitys

PHUSE working groups
PHUSE

Typical career paths

Clinical Programmer / Stats Programmer

Builds SDTM/ADaM datasets and TLFs for regulatory submissions.

Pharmacovigilance Data Scientist

Performs signal detection on spontaneous-reporting databases and supports ICSR workflows.

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