Об этом курсе: 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.
Автор: University of Washington
Преподаватели: Bill Howe, Director of Research
Scalable Data Analytics
| Основные сведения | Курс 4 из 4 — Data Science at Scale Specialization |
| Выполнение | 6 weeks of study, 3-4 hours/week |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 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 материала для самостоятельного изучения
- Reading: Get the Data
- Reading: Understand the Domain
- Discussion Prompt: Milestone: Discuss the Problem and Approaches
НЕДЕЛЯ 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 материал для самостоятельного изучения
- Reading: Milestone: Create a list of "buildings" from a list of geo-located incidents
- Peer Review: Reflecting on defining "buildings"
НЕДЕЛЯ 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 материал для самостоятельного изучения
- Reading: Milestone: Derive labels for each building
- Peer Review: Reflecting on the labeling scheme
НЕДЕЛЯ 4
Week 4: Train and evaluate a simple model
Use a trivial feature set to train and evaluate a simple model
1 материал для самостоятельного изучения
- Reading: Milestone: Train a Simple Model
- Peer Review: Reflecting on a trivial initial model
НЕДЕЛЯ 5
Week 5: Feature Engineering
Derive additional features and retrain to improve the efficacy of your model.
1 материал для самостоятельного изучения
- Reading: Milestone: Adding more features
- Peer Review: Reflection on your proposed features
НЕДЕЛЯ 6
Week 6: Final Report
Enter your final report for grading.
Оцениваемый: Final Report
Часто задаваемые вопросы
Как это работает
Coursework
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Авторы
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.
Стоимость
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Получите сертификат о прохождении курса, ссылкой на который можно делиться с другими людьми | Доступен |
Рейтинги и отзывы
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.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
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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.