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.
提供方
大规模数据科学 專項課程华盛顿大学課程信息
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 4 門課程（共 4 門）
完成時間大約為6 小時
英語（English）
字幕：英語（English）
您將獲得的技能
- Python Programming
- R Programming
- Data Analysis
- Data Wrangling
- Statistics
可靈活調整截止日期
根據您的日程表重置截止日期。
可分享的證書
完成後獲得證書
100% 在線
立即開始，按照自己的計劃學習。
第 4 門課程（共 4 門）
完成時間大約為6 小時
英語（English）
字幕：英語（English）
提供方
华盛顿大学
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.
授課大綱 - 您將從這門課程中學到什麼
完成時間為 1 小時
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.
完成時間為 1 小時
2 個閱讀材料
完成時間為 1 小時
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.
完成時間為 1 小時
1 個閱讀材料
完成時間為 1 小時
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.
完成時間為 1 小時
1 個閱讀材料
完成時間為 1 小時
Week 4: Train and evaluate a simple model
Use a trivial feature set to train and evaluate a simple model
完成時間為 1 小時
1 個閱讀材料
關於 大规模数据科学 專項課程
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
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