Data Science


Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Storytelling with Data: A Data Visualization Guide for Business Professionals
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Python for Data Analysis
The Signal and the Noise: Why So Many Predictions Fail—But Some Don't
Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
Data Science from Scratch: First Principles with Python
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Naked Statistics: Stripping the Dread from the Data
Hands-On Machine Learning with Scikit-Learn and TensorFlow
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
Python Data Science Handbook: Essential Tools for Working with Data
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
The Art of Statistics: How to Learn from Data
Practical Statistics for Data Scientists: 50 Essential Concepts
Should an organization find a data scientist to point out its shortcomings, follow them as you would a guide to hidden riches.
Damian Mingle

Kevin Guyan
Hacking described his research interest ‘in classifications of people, in how they affect the people classified, and how the affects on the people in turn change the classifications.’ Hacking labeled the subjects of these studies ‘moving targets’ because researchers’ investigatory efforts change them in ways so ‘they are not quite the same kind of people as before.
Kevin Guyan, Queer Data: Using Gender, Sex and Sexuality Data for Action

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#DS4HS A group for the data friendly and those who want to be
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