Data Science at Scale - Capstone Project
Description
When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
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.
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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan .
- Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
- Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.
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
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
- Reading: Get the Data
- Reading: Understand the Domain
- 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
- Reading: Milestone: Create a list of "buildings" from a list of geo-located incidents
- 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
- Reading: Milestone: Derive labels for each building
- 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
- Reading: Milestone: Train a Simple Model
- 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
- Reading: Milestone: Adding more features
- Peer Review: Reflection on your proposed features
WEEK 6
Week 6: Final Report
Enter your final report for grading.
Graded: Final Report
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