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University of Washington

Data Science at Scale - Capstone Project

University of Washington via Coursera


In the capstone, students will engage on a real world project requiring them to apply skills from the entire data science pipeline: preparing, organizing, and transforming data, constructing a model, and evaluating results. Through a collaboration with Coursolve, each Capstone project is associated with partner stakeholders who have a vested interest in your results and are eager to deploy them in practice. These projects will not be straightforward and the outcome is not prescribed -- you will need to tolerate ambiguity and negative results! But we believe the experience will be rewarding and will better prepare you for data science projects in practice.


  • Project A: Blight Fight
    • In this project, you will build a model to predict when a building is likely to be condemned. The data is real, the problem is real, and the impact is real.
  • Week 2: Derive a list of buildings
    • You are given sets of incidents with location information; you need to use some assumptions to group these incidents by location to identify specific buildings.
  • Week 3: Construct a training dataset
    • Construct a training set by associating each of your buildings with a ground truth label derived from the permit data.
  • Week 4: Train and evaluate a simple model
    • Use a trivial feature set to train and evaluate a simple model
  • Week 5: Feature Engineering
    • Derive additional features and retrain to improve the efficacy of your model.
  • Week 6: Final Report
    • Enter your final report for grading.

Taught by

Bill Howe


3.8 rating at Coursera based on 25 ratings

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