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Data Scientist

Turn messy data into defensible insight.

Intermediate ~12 weeks·Analysts and scientists using Python for data.

Recommended topic order

  1. 1Python Fundamentals
  2. 2Python Data Types
  3. 3Data Science with Python
  4. 4Database Engineering
  5. 5Machine Learning with Python

Weekly learning plan

Weeks 1–3

  • · Python for data
  • · NumPy arrays & vectorization

Weeks 4–6

  • · pandas cleaning & EDA
  • · Missing values & outliers

Weeks 7–9

  • · Statistics & visualization
  • · SQL for analysis

Weeks 10–12

  • · scikit-learn basics
  • · Experiment analysis & reporting

Required projects

  • Sales analysis
  • Customer analytics
  • Experiment analysis
  • Data-quality report

Interview topics

pandas internalsVectorizationStatisticsSQL joinsBias/variance

Portfolio expectations

  • · 2–3 reproducible notebooks with clear narrative and visuals

Job-readiness checklist

  • Cleans and profiles real datasets
  • Communicates findings with visuals
  • Runs and interprets basic statistics
  • Builds a first predictive model
0%ready

Skill-gap analysis

Your live coverage of this path's tracks, from local progress.

Focus areas

Python fundamentalsNumPyPandasSQLData cleaningVisualizationStatisticsScikit-learnNotebooksExperimentation

Skill prerequisites

fundamentals