Data Scientist

Combine coding, statistics and machine learning to explore patterns and build useful predictive systems.

In short— Data ikiongea in riddles, wewe ndio translator wake—with maths kidogo mingi.

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What you'll learn

Click any stage kufungua full details.

01Stage 01Coding and dataPython, NumPy and pandas • SQL and database basics

Use Python and SQL to load, transform and explore datasets efficiently.

Focus on

  • Python, NumPy and pandas
  • SQL and database basics
  • Notebooks, Git and reproducibility
Build check: Complete an exploratory analysis from raw data.

Ukimaliza hapa, usirush next stage—build kitu kwanza.

02Stage 02Math and statisticsProbability and distributions • Linear algebra and calculus intuition

Understand the assumptions behind models instead of treating them as magic.

Focus on

  • Probability and distributions
  • Linear algebra and calculus intuition
  • Inference, experiments and regression
Build check: Explain and test a statistical hypothesis.

Ukimaliza hapa, usirush next stage—build kitu kwanza.

03Stage 03Machine learningSupervised and unsupervised learning • Feature engineering and preprocessing

Train, compare and evaluate models without leaking information.

Focus on

  • Supervised and unsupervised learning
  • Feature engineering and preprocessing
  • Validation, metrics and hyperparameter tuning
Build check: Build and compare three baseline models.

Ukimaliza hapa, usirush next stage—build kitu kwanza.

04Stage 04CommunicationVisualization and storytelling • Causal thinking and limitations

Turn model output into conclusions people can act on.

Focus on

  • Visualization and storytelling
  • Causal thinking and limitations
  • Stakeholder communication
Build check: Present a model result to a non-technical audience.

Ukimaliza hapa, usirush next stage—build kitu kwanza.

05Stage 05Advanced pathsDeep learning fundamentals • NLP or computer vision

Explore deeper methods only after the foundations are solid.

Focus on

  • Deep learning fundamentals
  • NLP or computer vision
  • Time series and recommender systems
Build check: Complete one specialized capstone.

Ukimaliza hapa, usirush next stage—build kitu kwanza.

06Stage 06Production data scienceExperiment tracking and pipelines • Model serving and monitoring

Make experiments repeatable and models observable after launch.

Focus on

  • Experiment tracking and pipelines
  • Model serving and monitoring
  • MLOps, drift and responsible AI
Build check: Deploy and monitor a small prediction service.

Ukimaliza hapa, usirush next stage—build kitu kwanza.

Now build kitu real.

Pick one project that solves an actual problem, ship it, then explain what you learned. Hapo ndio portfolio inaanza kuhit different.

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