About

A case lab for data science judgment under pressure.

Data Science Judgment Lab is for the space between knowing a method and making a defensible decision. The cases ask learners to inspect messy evidence, notice uncertainty, resist tidy narratives, and decide what a responsible analyst should say next.

What It Is

The lab is not a coding exam, statistics refresher, or checklist course. It is a practice environment for judgment: metric interpretation, causal caution, operational ML, fairness burden, forecasting under drift, threshold policy, and executive communication.

Each case is intentionally multimedia. Audio notes, charts, memos, logs, tables, and visual panels are treated as evidence. Some artifacts matter, some are distracting, and some are true but insufficient.

Who It Serves

• Students moving beyond syntax into real analytical decisions.
• Analysts and data scientists practicing evidence discipline.
• Managers who need to evaluate data claims without overclaiming.
• Instructors who want discussion cases with built-in calibration.

What It Does Not Claim

The completion record is local and self-attested. It can support class discussion, professional development, or portfolio reflection, but it is not proctored, identity verified, externally auditable, or a formal certification of data science competence.

Open Source

The lab is public by design. The source code is available under the MIT License so instructors and developers can inspect, fork, and adapt the site infrastructure. The case materials and curriculum are licensed separately under CC BY-NC 4.0, which permits noncommercial educational reuse and adaptation with attribution.

See the license page or the GitHub repository for the full source and license files.