A 6-month, self-paced, project-driven learning journey. Participants build a real, deployable data project using free and open-source tools.
Cohort: June – November 2026 | Time: ~5 hrs/week | Cost: Free
By the end of the program, every participant will have:
- A public GitHub repo with a clean, documented data project
- An end-to-end data pipeline (ingest → clean → analyze → deploy)
- An analysis notebook with charts, statistics, and written insights
- A live deployed dashboard (GitHub Pages)
| Duration | 24 weeks (~5 hours/week, ~120 hours total) |
| Weekly Rule | 1 primary resource + 1 optional max; every week produces a project artifact |
| Design Principles | Project-first · milestone-driven · public accountability · low-overwhelm resource curation |
| Resource Rule | Prefer official docs, interactive tools, or one proven course. Avoid multiple full courses in the same week. |
| Tool Stack | Free tools only: GitHub, Python, SQL, HTML. Optional tools (Tableau, etc.) are learner-driven. |
This is the program hub — it contains the curriculum, weekly resources, and milestone guides.
Builders: Follow the phase folders in order. Each week folder has resources, tasks, and links.
Volunteers: See docs/VOLUNTEER_GUIDE.md for your role and responsibilities.
The GitHub Pages onboarding site lives in docs/. Run it through a local static server so browser fetch() calls can load files such as docs/data/builders.json.
python3 -m http.server 4173 -d docsThen open:
http://localhost:4173/
Avoid opening docs/index.html directly with file://; the builder dashboard may show its fallback state because the browser can block local JSON requests.
If you have spent more than 2 hours on one problem without progress:
- Write down exactly what you tried
- Post in the DEP community channel with your error message and code snippet
- Tag your moderator
Do NOT skip ahead. Moderators flag stuck participants for Ops Lead review within 48 hours. You may not advance to the next milestone while a blocker is unresolved.
| Phase | Weeks | Focus | Output |
|---|---|---|---|
| 01 — Foundations | 1–4 | Problem framing, data source discovery, GitHub + Python basics | Problem statement + first raw data pull |
| 02 — Data Collection | 5–6 | API fundamentals, alternate ingestion paths (scraping / manual) | Ingestion script + raw data in /data/raw |
| 03 — Data Processing | 7–12 | Storage/data modeling, SQL, Pandas cleaning, data quality, pipeline structuring | Clean, schema-defined dataset + reproducible pipeline |
| 04 — Analysis & Insights | 13–16 | Descriptive stats, EDA, visualization, insight writing | Insights notebook with 3–5 charts |
| 05A — Predictive Layer (Path A — conditional) | 17–20 | Regression, classification, feature engineering, ML pipeline integration | Predictive model + evaluation metrics |
| 05B — Non-Predictive Alt Track (Path B — conditional) | 17–20 | Advanced segmentation, KPI framework, stakeholder narrative, repo integration | Advanced analysis + stakeholder brief |
| 06 — Deployment | 21–24 | Dashboard design + build, GitHub Pages deploy, documentation polish, presentation | Live project URL + portfolio-ready repo |
Progress is tracked through 7 milestones (M0–M6). Each one has a clear output and a submission form.
| Milestone | When | Output |
|---|---|---|
| M0 — Problem Statement | End of Week 1 | Specific question + audience + possible data source + README in learner's own words |
| M1 — Data Source Identified / Repo Initialized | By Week 3–4 | Working repo + chosen source + README data section complete |
| M2 — Data Ingestion Script | By Week 6 | Raw data in /data/raw via API, scraping, or manual timestamped save |
| M3 — Clean Dataset | By Week 12 | Processed dataset + schema plan + cleaning notes + validation checks |
| M4 — Initial Insights | By Week 16 | 3–5 charts + written interpretations + one cautious inference section |
| M5 — Public Repo / Predictive Component | By Week 20–23 | Professional repo + predictive layer (Path A) OR advanced EDA + stakeholder brief (Path B) |
| M6 — Live Deployment | By Week 24 | Live GitHub Pages URL + presentable final project |
Gates: Milestones are sequential, and M0/M1 are hard progression gates. Learners may record the next submission while a prerequisite review is pending, but they must not proceed until the prerequisite is marked
passed. The issue stays queued and releases automatically after approval. Target deadlines remain visible; a late submission is flagged but continues through normal evaluation.
Full checklist: docs/MILESTONE_CHECKLIST.md
- Join the community — Join the DEP Discord
- Set up your project repo — copy the DEP Starter Kit scaffold into your own GitHub repo
- Start Phase 1 — go to 01-foundations/ and begin Week 1
- 2026 Cohort — June–November 2026 (current)
See docs/VOLUNTEER_GUIDE.md for role descriptions, responsibilities, and the operating rhythm.
Built by Data Engineering Pilipinas. Free and open. Always.