Welcome back, Logan 👋
Advanced Excel → Power BI → Power Automate → Python → AI, built around fictional defense program data. About 180 hours over 33 core weeks.
Overall progress
Phases
Setup and Ground Rules (Week 1)
Advanced Excel
Turn Excel from a calculation grid into a repeatable data pipeline: clean data with Power Query, look up and reshape with modern functions, wrap logic in LET/LAMBDA, and summarize with PivotTables on a Data Model.
Power BI
Build governed, refreshable, interactive program dashboards. Get data in shape with Power Query M, model it as a star schema, calculate it with DAX, present it well, and publish it securely.
Power Automate
Replace manual chasing, copying, and emailing with flows that move data between Excel, SharePoint, Teams/Outlook, and Power BI, and that alert people when program metrics cross thresholds.
Python for Report Automation
Write Python that reads messy program data, transforms it with pandas, and generates finished Excel and Word deliverables (the same kind of document automation your coworker builds), reliably and repeatably.
AI for Analytics
Use AI to speed up every layer you've learned (formulas, M, DAX, flows, Python, narrative) while keeping data security airtight and checking every output.
Portfolio Status Pipeline
The (fictional) PEO-FS Deputy PEO wants a monthly portfolio review that (1) updates itself when program teams submit data, (2) alerts leadership when cost or schedule performance crosses thresholds, and (3) produces the standard Word status report package auto…
📅 Weekly schedule
Assumes about 5–6 hours/week. A typical week: 2 weekday sessions of about 1.5 hours (concepts + exercise) and one weekend block of 2–3 hours (mini-project). Do the quiz at the start of the next week as spaced review.
| Week | Phase | Modules / focus | Hours |
|---|---|---|---|
| 0–1 | M0.1 Setup, M0.2 AI and data ground rules, start M1.1 | 6 | |
| 1 | Finish M1.1 Tables & XLOOKUP. Start M1.2 Dynamic Arrays. | 5.5 | |
| 1 | Finish M1.2. M1.3 LET & LAMBDA (start). | 5.5 | |
| 1 | Finish M1.3. M1.4 Power Query (start). | 6 | |
| 1 | Finish M1.4. M1.5 PivotTables & Data Model. | 6 | |
| 1 | Finish M1.5. M1.6 Dashboards + Phase 1 deliverable | 6 | |
| 2 | M2.1 Power BI orientation. Start M2.2 M language. | 5.5 | |
| 2 | M2.2 M in depth (custom functions, date dimension, schema-proof ingestion) | 6 | |
| 2 | M2.3 Star schema modeling | 6 | |
| 2 | M2.4 DAX fundamentals | 6 | |
| 2 | M2.5 Advanced DAX (fiscal time intelligence, cumulative EVM) | 6 | |
| 2 | Finish M2.5. Start M2.6 Visuals. | 5.5 | |
| 2 | M2.6 Report design (leadership-ready v1) | 5.5 | |
| 2 | M2.7 Publishing, RLS, refresh, alerts + Phase 2 review | 5.5 | |
| 3 | M3.1 Power Automate fundamentals | 5 | |
| 3 | M3.2 Excel & SharePoint flows | 6 | |
| 3 | M3.3 Office Scripts + flows | 5 | |
| 3 | M3.4 Power BI integration, error handling, governance | 5 | |
| 4 | M4.1 Python foundations (part 1) | 5 | |
| 4 | M4.1 (part 2) + start M4.2 pandas I | 5.5 | |
| 4 | M4.2 pandas I: clean the status drops, DQ report | 5.5 | |
| 4 | M4.3 pandas II: Python EVM engine | 5.5 | |
| 4 | Finish M4.3 + M4.4 openpyxl (start) | 5.5 | |
| 4 | Finish M4.4 (template filler) + M4.5 python-docx (start) | 5.5 | |
| 4 | Finish M4.5. Start M4.6 pipeline. | 5.5 | |
| 4 | M4.6 portfolio report run + Phase 4 review | 5 | |
| 5 | M5.1 Governance & security + M5.2 prompting (start) | 5.5 | |
| 5 | Finish M5.2 + M5.3 Copilot | 5.5 | |
| 5 | M5.4 Validation & AI-assisted workflows | 5 | |
| Capstone | Intake, Power Query, model, measures | 5 | |
| Capstone | Report, publish, RLS, alert flows | 5 | |
| Capstone | Python package + hand-off flow | 5 | |
| Capstone | End-to-end test, docs, briefing | 5 | |
| Buffer | Catch-up, revisit weak areas, optional PL-300 (Power BI Data Analyst) prep using Microsoft Learn | 0–15 |
Total core: about 180 hours over 33 weeks (about 7.5 months). With buffer, 36 weeks (about 8 months).
Milestones to celebrate:
- ✅ Week 6: Phase 1 Excel dashboard
- ✅ Week 14: Published, secured Power BI report
- ✅ Week 18: First production-style alert flow
- ✅ Week 26: One-command Python report package
- ✅ Week 33: Capstone briefing to your supervisor
If you fall behind: Skip the stretch tasks, keep the mini-projects (they feed the capstone), and use the buffer weeks. If work lets you, apply each module to a real (properly handled) work task in the same week. That's the fastest way to make it stick.
How this bootcamp works
Every module follows the same structure, so the website can render each one as a page with tabs or sections:
| Section | Purpose |
|---|---|
| Learning Objectives | What you can do when you finish |
| Key Concepts | The ideas and features to learn, with short examples |
| Hands-On Exercise | Guided practice on the fictional dataset. Columns are listed. |
| Mini-Project | A small, open-ended build that looks like a real work product |
| Self-Check Quiz | 3–5 questions. Answers are in collapsible blocks. |
| Estimated Hours | Time budget for the module, including the exercise and mini-project |
Guiding principles
- Work-shaped practice. Every exercise uses the kinds of data you already handle: milestones, EVM, obligations and expenditures, contracts, IMS exports, and risks.
- Same data all the way through. One fictional program portfolio runs through every phase. Your Excel cleanup turns into your Power BI model, then feeds a Power Automate alert, then your Python report.
- Fictional data only. Never practice with real program data, and never put real data into AI tools unless your organization has approved them for that data. (See Module 0.2.)
- Build a portfolio. Save every mini-project. Together they become a portfolio you can show your supervisor.
Module ID convention (for the website): M<phase>.<module>, e.g., M2.4 = Phase 2, Module 4. Quiz IDs are Q<phase>.<module>.<n>.
Program at a glance
| Phase | Focus | Modules | Hours | Weeks (approx.) |
|---|---|---|---|---|
| 0 | Setup and ground rules | 2 | 3 | Week 1 (with 1.1) |
| 1 | Advanced Excel (Power Query, XLOOKUP, dynamic arrays, LET/LAMBDA, PivotTables, Data Model) | 6 | 34 | Weeks 1–6 |
| 2 | Power BI (Power Query M, star schema, DAX, visuals, publishing) | 7 | 46 | Weeks 7–14 |
| 3 | Power Automate (Excel, SharePoint, Power BI flows) | 4 | 21 | Weeks 15–18 |
| 4 | Python (pandas, openpyxl, python-docx, report automation) | 6 | 43 | Weeks 19–26 |
| 5 | AI for analytics (Copilot, LLM prompting, validation, data security) | 4 | 16 | Weeks 27–29 |
| Capstone | Raw data → Power BI → Power Automate alert → Python status report | 1 | 20 | Weeks 30–33 |
| Buffer | Catch-up, review, optional PL-300 certification prep | n/a | 0–15 | Weeks 34–36 |
| Total | 30 | ~183 core | 33 core weeks (~8 months) |
Phase 0 hours (3) are counted inside Week 1. The headline figure is about 180 hours.
Learning path logic
Excel (clean & model data) ──► Power BI (model at scale, DAX, share)
│ │
└──► Power Automate (move data, alert, approve) ◄──┘
│
Python (generate documents, heavy transforms)
│
AI tools (speed up every layer, applied safely)