Data engineering, applied analytics, and machine-learning portfolio by Tommi Saltiola.
The portfolio emphasizes reproducible analysis, explicit data contracts, deterministic synthetic fixtures, browser-runnable evidence, and test-driven delivery. GitHub source is the portfolio front door; Molab derives each interactive Marimo app from that reviewed source on demand.
| Project | Run | Engineering evidence | Tests |
|---|---|---|---|
| Data Engineer Certification Case Study | Molab | Four-source wellness pipeline, schema and grain contracts, unit normalization, rejected-record ledger, source profiles, deterministic hashes | 36 |
| Data Scientist Certification Case Study | Molab | Group-aware imputation, three-model validation benchmark, recall-constrained decisions, reserved-test uncertainty, calibration and slice evidence | 36 |
| Public-sector Opportunity Pipeline | Molab | Heterogeneous ingestion, deterministic incremental merge, watermarks, retries, Prefect boundary, transparent scoring | 66 |
All three flagships use deterministic fictional data and pass focused tests, Ruff, strict Marimo checks, executed WASM export validation, and Chromium interaction smoke tests.
The wellness and classifier projects are runnable case studies for broad data-engineering and data-science competencies covered by the certifications. They use public source, tests, documented contracts, and deterministic synthetic fixtures. Private assessment materials are neither needed nor published.
These smaller labs modernize five historical learning themes as independently written Marimo apps. They are supporting analytical demonstrations, not claims of production deployment. Every default path uses deterministic synthetic data and runs without credentials, uploads, or private infrastructure.
| Lab | Run | Analytical focus |
|---|---|---|
| Airline Delay Quality Lab | Molab | Flight-grain delay components, cancellation context, and carrier summaries |
| Synthetic Health Risk Quality Lab | Molab | Duplicate-profile audit and descriptive ordinal associations over fictional data |
| Restaurant Location Quality Lab | Molab | Coordinate validation, accepted records, and an explicit unresolved ledger |
| Streaming Catalog Explorer | Molab | Release-period, genre, and duration summaries at title grain |
| Judo Medal Explorer | Molab | Medal-rate summaries at declared fictional athlete-event grain |
See the Analytics Learning Labs evidence and data provenance.
DataCamp Data Scientist and Data Engineer certificates are published with official verification links. Each credential points to a runnable public case study, its source, and its engineering specification.
Review certifications and competency mapping.
Certificate images do not admit any associated assessment prompts, datasets, solutions, schemas, metrics, outputs, or grader rules into this repository.
Two separate repositories turn deeper search and knowledge-system patterns into public demonstrations using independently implemented source:
- Search Taxonomy Lab — TF-IDF and latent-semantic evidence, cluster discovery, transparent classification, human review, benchmarks, and a hash-chained audit ledger.
- Content Evidence Workbench — retrieval, exact citations, declared-entity context, judged evaluation, and explicit human review over a synthetic corpus.
The DBSCTR Delivery Accelerator adapts an auditable agentic engineering lifecycle to one repository and delivers one bounded pilot through every applicable gate.
Discuss a pilot or inspect the owner-authored, MIT-licensed DBSCTR source.
Each flagship has its own pyproject.toml and uv.lock. From the repository
root, run the matching app path inside each project:
(cd projects/wellness-data-pipeline && uv sync --locked && uv run --frozen pytest -q && uv run --frozen marimo check --strict app.py)
(cd projects/content-performance-classifier && uv sync --locked && uv run --frozen pytest -q && uv run --frozen marimo check --strict src/app.py)
(cd projects/public-sector-opportunity-pipeline && uv sync --locked && uv run --frozen pytest -q && uv run --frozen marimo check --strict app.py)Project-local README files list their complete Ruff and verification commands. The five learning labs share one locked environment:
(
cd projects/analytics-learning-labs
uv sync --locked
uv run --frozen pytest -q
uv run --frozen ruff check .
uv run --frozen ruff format --check .
uv run --frozen marimo check --strict apps/*.py
)The quality workflow runs project gates, builds all eight apps in temporary WASM test surfaces, exercises them in Chromium, scans critical vulnerabilities, and emits an SPDX software bill of materials.
This repository contains no employer code, client data, private credentials, restricted assessment prompts, datasets, solutions or outputs, proprietary SaaS source, or personal datasets. Visitor uploads remain runtime-only and are never admitted to committed session previews.
See DATA_PROVENANCE.md, and DEPENDENCIES.md.
Saltiola7/data-portfolio is the
canonical public portfolio. Separate static hosting is retired. Molab derives
each runnable app directly from reviewed GitHub source. Changes integrate
through pull-request review.
Owner-authored code and documentation are available under the MIT License. Professional credential images are published solely as verification evidence and are not licensed for reuse under the repository's MIT license. Any future public datasets retain their separately recorded source licenses.