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 |
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
- 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
- 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
- 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
- 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
- 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
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
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.
Pricing
| Purchase Course | Audit | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Available | Not available |
| Receive a final grade | Available | Not available |
| Earn a shareable Course Certificate | Available | Not available |
Ratings and Reviews
An interesting problem to tackle. I really liked that you started with very raw data and needed to work on many cleaning methods. Good practice for real data science.
This course introduces data science with a emphasis on large scale processing. The project was challenging and very interesting. Applying with I learnt throughout the specialisation to a real problem is very exciting.
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The topic and milestone-based schedule are great; you are free to take many decisions but still get enough guidance to avoid straying away from the project goal. Instructor and student commitment is a bit low; there is very little feedback in the forums and submissions are sometimes so shallow that add little value to the reviewer.