Endocrinology & Diabetes
Endocrinology now integrates CGM, closed-loop insulin, and multi-omic metabolic phenotyping to manage diabetes, thyroid and pituitary disorders.
Default learning track
4 phases · ~16–22 weeksA 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.
Phase 1 — Foundations
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
Hands-on projects
Walk through a published tutorial in Python + CGM analytics libs 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
Phase 2 — Core tools & datasets
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
Hands-on projects
Pick one study from OpenAPS / T1D Exchange / NHANES, 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 Python + CGM analytics libs tutorial end-to-end on real data
- Download and explore one full dataset from OpenAPS / T1D Exchange / NHANES
- Document a clean QC + analysis pipeline that another person could rerun
Cited resources for this phase
Phase 3 — Applied projects
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
Hands-on projects
Analyse a public CGM dataset, compute glycaemic metrics and propose a personalised feedback dashboard.
Deliverable: Notebook + dashboard mockup + clinical brief
Milestones
- Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
- Engage with the ADA / EASD tech community (post a question, answer one, or share a notebook)
- Get peer feedback on at least one project and iterate
Cited resources for this phase
Phase 4 — Portfolio & career launch
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
Hands-on projects
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
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.
Typical career paths
Clinical Endocrinologist
Manages diabetes and endocrine disease; interprets CGM and hormonal panels.
Diabetes Data Scientist
Builds models on CGM/EHR data for personalised glycaemic control.
Ready to make this your own?
Answer a short profile and the AI builder will tailor every phase — pace, hours, tools, and resources — to your background and goals in this sector.
Build my personalized roadmap