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Data Science & Analytics in Practice

The practitioner's course. Learn to turn messy data into confident decisions โ€” from cleaning and exploration to statistics, A/B testing, visualization, SQL, and complete ML workflows. You'll build a portfolio-ready end-to-end analysis on a real dataset and earn a certification.

Machine Learning Intermediate Data Science
  • 9 lessons
  • Updated 09/05/2026
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Portfolio project: an end-to-end analysis

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Capstone: an analysis you can show off

Deliver a complete, real-dataset project from question to recommendation โ€” the piece that proves you can do the job.

What great looks like

  1. A clear question tied to a decision.
  2. Reproducible cleaning & EDA with commentary.
  3. Sound analysis โ€” stats and/or a model, honestly evaluated.
  4. Compelling visuals that carry the story.
  5. A crisp recommendation a stakeholder could act on.

Deliverable

A notebook + a short written summary (and ideally a dashboard). Publish it โ€” this is the centerpiece of your data portfolio.

Congratulations โ€” you can now turn raw data into decisions end-to-end.

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