Background material 链接
1.
[Lead in water] Lead and Your Water information from Scottish Water
Background on why you should worry about lead in your water, from our data provider Scottish Water.
2.
[Lead in water] What We Are Doing About Lead information from Scottish Water
Information on what Scottish Water is doing about lead in your water
3.
[Data wrangling] Tidying the Australian Same Sex Marriage Postal Survey Data with R
Part 1 of 2 of a reproducible data wrangling story, for the ultimate goal of modeling. This blog post is not about the dataset we’re working with, however, it’s a nice write up of a similar task.
4.
[Data wrangling] Combining Australian Census data with the Same Sex Marriage Postal Survey in R
Part 2 of 2 of a reproducible data wrangling story, for the ultimate goal of modeling. This blog post is not about the dataset we’re working with, however, it’s a nice write up of a similar task.
5.
[Paper] Data Organization in Spreadsheets
Attached Files: Data Organization in Spreadsheets.pdf (PDF文件已经放入Background material文件夹里)
Karl W. Broman & Kara H. Woo (2018) Data Organization in Spreadsheets, The American Statistician, 72:1, 2-10, DOI: 10.1080/00031305.2017.1375989.
6.
[Paper] A Data Science Approach to Understanding Residential Water Contamination in Flint
Attached Files: A Data Science Approach to Understanding Residential Water Contamination in Flint.pdf （PDF文件已经放入Background material文件夹里）
Alex Chojnacki, Chengyu Dai, Arya Farahi, Guangsha Shi, Jared Webb, Daniel T. Zhang, Jacob Abernethy, and Eric Schwartz. 2017. A Data Science Approach to Understanding Residential Water Contamination in Flint. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’17). Association for Computing Machinery, New York, NY, USA, 1407–1416. DOI:https://doi.org/10.1145/3097983.3098078
7.
[Book] R for Data Science
The data wrangling section, especially, will be useful for those doing their work in R
8.
[Book] Python Data Science Handbook
The Data Manipulation with Pandas chapter, especially, might be useful for those doing their project in Python