The doing readings are listed with each project to support what you will need to read to complete the respective project. This list is for reference. The being readings below are suggested. As a class we will pick five to nine of them to read throughout the semester.
Table of contents
{: .text-delta } 1. TOC {:toc}- John Rauser on visualization
- Effectively Communicating Numbers
- Hans Rosling: The River of Myths and Data Scientist Florence Nightengale
- Data Visualization (Chapter 1 - Look at data)
- Questions and data science
- Computational Thinking and Optional Reading for new programmers
- How to Become a Data Scientist, The Self-Starter Way and What’s The Best Path To Becoming A Data Scientist?
- What do people do with new data
- The art of structured thinking and analyzing and Tools for improving structured thinking (for analysts)
- Hadley on Tidy Data (skim read)
- Quartz Reference for How to deal with data issues (optional)
- Statistical Concepts in Presenting Data
- What charts say and What charts do
- Chapter 4: The Truthful Art: Data, Charts, and Maps for Communication
- Issues with Spatial Aggregation
- Gelman on p hacking
- Of beauty, sex, and power: Statistical challenges in estimating small effects
- NoSQL vs. SQL: The Future of Data
- Ethics of a Data Scientist
- An ethical code can’t be about ethics and Ethical codes vs. ethical code
- What insurance allready does
- Big Data and Civil Rights
- The ethics of web scraping
- Big data and the Underground Railroad and Machine Learning, Physiognomy, and Hidden Bias
- Ethics in Data Science (4 episodes)
- How big data is unfair
- Who decides the ethics
- What If Data Scientists Had Licenses Like Lawyers?
- What is an “algorithm”? It depends whom you ask