Unit 10: SDK Workflows and Data Science in the Real World

The final Unit connects tooling to consequences. A real data-science system is a chain of decisions about the question, source data, preparation, analysis, communication, deployment, and what happens when the evidence is incomplete.

Source lessons

1. Data Science in the Cloud: The Azure ML SDK Way
2. Data Science in the Real World

Use the SDK lesson to study code-driven workflow structure. Use the real-world lesson to examine where data science sits inside larger systems and decisions.

What you should be able to prove

By the end of the Unit, you should be able to:

• connect a data question to provenance, preparation, analysis, and visualization;
• defend a tool/deployment choice;
• state the main finding without hiding uncertainty;
• reject at least one plausible alternative and explain why; and
• identify the next question the evidence creates.

Evidence check

Finish with Evidence Check — Data Science Evidence Dossier. This is the course capstone artifact.

Runtime boundary

No Azure account is required to complete the dossier. A public dataset from an earlier lesson or a teacher-supplied synthetic dataset is sufficient. Executable cloud work remains optional until a specific environment is provisioned and tested.