(Spring 2013)


Syllabus

Part 1: Data Manipulation, at Scale


Part 2: Analytics

  • Basic statistical modeling, experiment design
  • Introduction to Machine Learning
    • Supervised Learning: decision trees/forests, simple nearest neighbor
    • Unsupervised learning: K-Means, multi-dimensional scaling


Part 3: Interpreting and Communicating Results

  • Visualization, visual data analytics

Links

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