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01 - Roadmap: Zero Experience -> Python SME (Automation and Dashboards)

Home: README

Who this is for

  • You have zero coding, scripting, or programming experience.
  • You need practical outcomes in a real operations environment.
  • You learn best by doing real work, not passive studying.

What you will build

  • Reliable Python automations for files, Excel, SQL, and monitoring data.
  • Repeatable ETL workflows using SQL databases as a reporting backbone.
  • Monitoring API data ingestion jobs.
  • Browser-based dashboards for non-technical stakeholders.

Prerequisites

  • One supported platform: Windows, macOS, Linux, Android, or iOS (desktop strongly recommended for full path).
  • Permission to install Python and VS Code.
  • Database credentials for your SQL database (SQLite for learning, PostgreSQL for production).
  • Read-only API access to your monitoring platform to start.

Program overview (8-10 hrs/week default)

  • Phase 0 (Week 1): environment setup and first script.
  • Phase 1 (Weeks 2-6): Python foundations.
  • Phase 2 (Weeks 7-10): quality tooling and team-ready workflow.
  • Phase 3 (Weeks 11-16): file and Excel automation.
  • Phase 4 (Weeks 17-22): SQL-first ETL pipelines.
  • Phase 5 (Weeks 23-28): monitoring API integration.
  • Phase 6 (Weeks 29-38): dashboard delivery for browser users.
  • Phase 7 (Weeks 39+): release process, governance, and handoff maturity.
  • Phase 8+ (Advanced): full-stack expert path and infinite mastery loop.
  • Phase 9+ (Elite): formal exams, architecture defenses, platform hardening, and world-class evidence loop.

Step-by-step lab pack

Phase 0 - Setup (Week 1)

Weekly outcome:

  • Local Python environment works reliably.

Minimum deliverables:

  • python --version works.
  • .venv created and activated.
  • First script runs.
  • First test passes.

Done means done:

  • You can repeat setup in a fresh folder without guessing.

Fail/recover guidance:

Phase 1 - Foundations (Weeks 2-6)

Weekly outcomes:

  • Week 2: variables, types, conditionals.
  • Week 3: loops and collections.
  • Week 4: functions and modular thinking.
  • Week 5: file IO and paths.
  • Week 6: debugging and code reading.

Minimum deliverables:

  • 15 micro-scripts.
  • One debugging diary file.

Done means done:

  • You can explain each script out loud in plain language.

Fail/recover guidance:

  • If stuck, reduce problem size and rebuild with toy data.

Phase 2 - Quality and workflow (Weeks 7-10)

Weekly outcomes:

  • toolchain setup, formatting, linting, tests, logging.

Minimum deliverables:

  • reusable project template with pyproject.toml, tests, logging, and README.

Done means done:

  • Any teammate can run your tool using documented steps.

Fail/recover guidance:

  • If tooling feels heavy, keep features tiny and run checks per feature.

Phase 3 - Files and Excel automation (Weeks 11-16)

Weekly outcomes:

  • robust ingestion of multiple spreadsheets with validation.

Minimum deliverables:

  • Capstone A baseline complete.

Done means done:

  • malformed inputs are rejected safely with clear logs.

Fail/recover guidance:

  • Start with a 2-file sample dataset and scale gradually.

Phase 4 - SQL ETL (Weeks 17-22)

Weekly outcomes:

  • clean table design and idempotent pipeline loads.

Minimum deliverables:

  • staging and reporting tables + ETL job + daily summary query.

Done means done:

  • rerunning ETL does not duplicate records.

Fail/recover guidance:

  • freeze schema changes until test dataset passes end-to-end.

Phase 5 - Monitoring API integration (Weeks 23-28)

Weekly outcomes:

  • read-only ingestion from monitoring APIs into cache tables.

Minimum deliverables:

  • one daily ingestion job from each source.

Done means done:

  • data contract documented, ingestion stable, errors logged.

Fail/recover guidance:

  • enforce read-only endpoints first and short polling windows.

Phase 6 - Dashboard delivery (Weeks 29-38)

Weekly outcomes:

  • browser-consumable dashboard with filters and exports.

Minimum deliverables:

  • working dashboard with data freshness indicator.

Done means done:

  • non-technical user can answer core ops questions without SQL access.

Fail/recover guidance:

  • fallback to SQL-only cache mode when source APIs are slow.

Phase 7 - Shipping and governance (Weeks 39+)

Weekly outcomes:

  • release process, support runbook, handoff standards.

Minimum deliverables:

  • release checklist and operational runbook.

Done means done:

  • another engineer can operate and troubleshoot your tools.

Fail/recover guidance:

  • capture every incident and convert it into checklist updates.

Milestone gates

  • Gate A: setup + first script + first passing test.
  • Gate B: Capstone A supports safe reruns and rejects.
  • Gate C: SQL ETL is idempotent and logged.
  • Gate D: Monitoring API ingestion proof into database cache.
  • Gate E: browser dashboard available to stakeholders.

Project ladder mapping (practice by skill level)

  • Use ./projects continuously while progressing through phases.
  • Suggested mapping:
    • Levels 0-2 during Phase 0-1
    • Levels 3-5 during Phase 2-3
    • Levels 6-7 during Phase 4
    • Levels 8-9 during Phase 5-6
    • Level 10 during Phase 7 and capstone hardening
  • Project index:

Screenshot and checkpoint workflow

  • Capture proof screenshots and reflections while learning:
  • Use this after each session to improve retention and speed up troubleshooting.

If you fall behind (catch-up plan)

  1. Keep only one active project at a time.
  2. Finish minimum deliverables before adding features.
  3. Switch to 45-minute sessions with explicit goals.
  4. Use Hybrid mode until momentum returns.

Learning-style options (Play/Build/Dissect/Teach-back)

  • Play: tweak example scripts and observe behavior changes.
  • Build: implement full milestone checklists exactly.
  • Dissect: read unfamiliar scripts and annotate line-by-line intent.
  • Teach-back: explain one concept weekly to another person or a written journal.

Expected output

Break/fix drills

  • Break path assumptions by renaming input folders.
  • Break schema assumptions by removing required columns.
  • Break API assumptions by forcing timeout values.

Troubleshooting

  • If learning stalls: reduce scope, keep daily continuity, and ship smaller increments.
  • If project complexity spikes: return to the previous gate and stabilize.

Mastery check

You are ready to advance when you can:

  • describe your current phase deliverable in one sentence,
  • run it end-to-end,
  • explain where it fails and how to recover.

Primary Sources

Optional Resources

Next

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