關於此課程：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.
製作方： University of Washington
教學方： Bill Howe, Director of Research
Scalable Data Analytics
| 基本信息 | 課程 4（共 4 門，Data Science at Scale Specialization ） |
| 承諾學習時間 | 6 weeks of study, 3-4 hours/week |
| 語言 | English |
| 如何通過 | 通過所有計分作業以完成課程。 |
| 用戶評分 |
授課大綱
第 1 週
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 個閱讀材料
- Leyendo: Get the Data
- Leyendo: Understand the Domain
- Cuadro de aviso de la discusión: Milestone: Discuss the Problem and Approaches
第 2 週
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 個閱讀材料
- Leyendo: Milestone: Create a list of "buildings" from a list of geo-located incidents
- Revisión por el compañero: Reflecting on defining "buildings"
第 3 週
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 個閱讀材料
- Leyendo: Milestone: Derive labels for each building
- Revisión por el compañero: Reflecting on the labeling scheme
第 4 週
Week 4: Train and evaluate a simple model
Use a trivial feature set to train and evaluate a simple model
1 個閱讀材料
- Leyendo: Milestone: Train a Simple Model
- Revisión por el compañero: Reflecting on a trivial initial model
第 5 週
Week 5: Feature Engineering
Derive additional features and retrain to improve the efficacy of your model.
1 個閱讀材料
- Leyendo: Milestone: Adding more features
- Revisión por el compañero: Reflection on your proposed features
第 6 週
Week 6: Final Report
Enter your final report for grading.
已評分: Final Report
常見問題解答
運作方式
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
製作方
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.
價格
| 購買課程 | |
|---|---|
| 訪問課程材料 | 可用 |
| 訪問評分的材料 | 可用 |
| 收到最終成績 | 可用 |
| 獲得可共享的課程證書 | 可用 |
評分和審閱
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.
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.