Research Data Management Guide

Data is fundamental to the modern world. From economic development, to healthcare, to education and public policy, we rely on numbers to allocate resources and make crucial decisions. But because so much data fails to take into account gender, because it treats men as the default and women as atypical, bias and discrimination are baked into our systems.

Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind?

Daniel J. Levitin shows how to recognize misleading announcements, statistics, graphs, and written reports, revealing the ways lying weasels can use them. It's becoming harder to separate the wheat from the digital chaff. How do we distinguish misinformation, pseudo-facts, and distortions from reliable information?

Highlights the pitfalls of data analysis and emphasizes the importance of using the appropriate metrics before making key decisions.

What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you'll discover how to: Test drive your data to see if it's ready for analysis; Work spreadsheet data into a usable form; Handle encoding problems that lurk in text data; Develop a successful web-scraping effort; Use NLP tools to reveal the real sentiment of online reviews; Address cloud computing issues that can impact your analysis effort; Avoid policies that create data analysis roadblocks; Take a systematic approach to data quality analysis