Data Science at Scale - Capstone Project

About this course: 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.

Created by:  University of Washington

• Taught by:  Bill Howe, Director of Research

  Scalable Data Analytics

Basic Info Course 4 of 4 in the Data Science at Scale Specialization Commitment 6 weeks of study, 3-4 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.0 stars Average User Rating 4.0See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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University of Washington Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.

Syllabus


WEEK 1


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.


2 readings expand

1. Reading: Get the Data
2. Reading: Understand the Domain
3. Discussion Prompt: Milestone: Discuss the Problem and Approaches

WEEK 2


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.


1 reading expand

1. Reading: Milestone: Create a list of "buildings" from a list of geo-located incidents
2. Peer Review: Reflecting on defining "buildings"

WEEK 3


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.


1 reading expand

1. Reading: Milestone: Derive labels for each building
2. Peer Review: Reflecting on the labeling scheme

WEEK 4


Week 4: Train and evaluate a simple model
Use a trivial feature set to train and evaluate a simple model


1 reading expand

1. Reading: Milestone: Train a Simple Model
2. Peer Review: Reflecting on a trivial initial model

WEEK 5


Week 5: Feature Engineering
Derive additional features and retrain to improve the efficacy of your model.


1 reading expand

1. Reading: Milestone: Adding more features
2. Peer Review: Reflection on your proposed features

WEEK 6


Week 6: Final Report
Enter your final report for grading.

Graded: Final Report