Business, analytics, and ops: ML domain track
You already work with tables, metrics, and stakeholder questions. This track orders the shared Road to ML spine for business analytics and operations-style work. It is not an MBA. It does not give investment advice. It does not guarantee revenue growth or a hire.
Core idea. Keep domain judgment about customers, processes, and KPIs. Add Python, SQL, evaluation, and careful forecasting habits. Methods live in Modules 00–25.
Who this is for
- Analysts, operators, and business students who need ML literacy for tables and forecasts
- Switchers who want a Data Analyst emphasis first, then Data Scientist depth
- People tired of “AI will grow revenue” marketing without evaluation habits
What you already bring
- Comfort with spreadsheets, dashboards, or warehouse tables
- Stakeholder questions that need clear error talk
- Sense for seasonality, cohorts, and messy operational labels
ML problem types you will meet
- Classification and regression on business tables
- Forecasting demand, volume, or operational series
- Feature work on joins, lags, and category encodings
- Careful splits so time and cohort leakage does not fake wins
- Explainability when a manager asks which drivers mattered
Gaps this track closes
- Module 01
- Modules 02–05
- Module 19 for warehouse-style tables
- Module 07 when features need care
- Module 15 when the target is ordered in time
- Module 21
Related resources
- Excel Data Analysis Guide. Spreadsheet habits that transfer into Python tables.
- Power BI Guide. Reporting bridge when stakeholders live in dashboards.
- Stakeholder Communication. Plain talk for metrics and wrong predictions.
- Agile Methodologies for Data Science. Iteration habits for messy business projects.
- Data Products Guide. Product thinking beyond a one-off notebook.
- Enterprise Data Tools. Warehouse-style tooling context next to Module 19.
- Ethics in ML. Harm and fairness habits for customer and employee data.
- Data Validation. Catch bad joins and labels before you trust a KPI model.
Role emphasis (not destiny)
| Emphasis | Role | Why |
|---|---|---|
| Primary | Data Analyst | SQL, reporting, and careful slices match many first roles |
| Alternate | Data Scientist | Strong when predictive models are the main job |
See Career Paths and Career Roadmap Guide. Treat times as emphasis maps only.
Intensity maps
Research support (tier B)
- Module 00 if needed
- Module 01
- Module 02
- Module 04 or Module 03 by task
- Module 05
- Module 19
- Module 21
- One Module 16 project you can explain without hype
Optional: Module 15 when forecasts are the real job.
Job-oriented study (tier B)
- Complete Research support
- Add Module 07
- Add Module 15 if the workplace lives on time-ordered KPIs
- Keep deployment modules for later unless the role truly ships models
Ordered study checklist
- Pick intensity map
- Finish Module 01 with a real business-style CSV (public or own)
- Finish Modules 02, 05, and one supervised module with a written split rule
- Finish Module 19 until you can join and filter without guessing
- Finish Module 21 and explain one wrong prediction to a stakeholder
- Write what this model must not claim (investment, guaranteed revenue, hiring outcomes)
Start here
Open Module 00 README if you need math or environment help. Otherwise open Module 01 README.
Honesty and traps
- Educational content only. Not investment, tax, or business-law advice.
- A high accuracy number on shuffled time data is often fake.
- “AI for growth” marketing is not a methods curriculum.
- Finishing modules is not the same as getting hired.
Try next: Open Module 19: SQL and Database Fundamentals after you can wrangle a table in Module 01.