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.
Data Science at Scale - Capstone Project
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Data Science at Scale - Capstone Project
This course is part of Data Science at Scale Specialization
Instructor: Bill Howe
2,583 already enrolled
Included with 
Gain insight into a topic and learn the fundamentals.
3.8
(25 reviews)
6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Taught in English
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This course is part of the Data Science at Scale Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Develop job-relevant skills with hands-on projects
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There are 6 modules in this course
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.
What's included
2 readings1 discussion prompt
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.
What's included
1 reading1 peer review
Construct a training set by associating each of your buildings with a ground truth label derived from the permit data.
What's included
1 reading1 peer review
Use a trivial feature set to train and evaluate a simple model
What's included
1 reading1 peer review
Derive additional features and retrain to improve the efficacy of your model.
What's included
1 reading1 peer review
Enter your final report for grading.
What's included
1 peer review
Instructor
Offered by
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University of California, Davis
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3.8
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