Data Analysis


Python for Data Analysis
Storytelling with Data: A Data Visualization Guide for Business Professionals
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists
Naked Statistics: Stripping the Dread from the Data
The Art of Statistics: How to Learn from Data
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
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
Big Data: A Revolution That Will Transform How We Live, Work, and Think
Data Smart: Using Data Science to Transform Information into Insight
Head First Data Analysis: A learner's guide to big numbers, statistics, and good decisions
Information Dashboard Design: The Effective Visual Commmunication of Data
Lean Analytics: Use Data to Build a Better Startup Faster
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (O'reilly Cookbooks)
Expected Goals by Rory SmithSoccermatics by David SumpterThe Numbers Game by David SallyScorecasting by Tobias J. MoskowitzSoccernomics by Simon Kuper
Sports Data Analysis
12 books — 1 voter
Algebraic Topology by Allen HatcherTopics in Optimal Transportation by Cédric VillaniHandbook of Discrete and Computational Geometry by Jacob E. GoodmanTopological Data Analysis by Nils BaasThe Structure and Stability of Persistence Modules by Frederic Chazal
Topological Data Analysis
6 books — 2 voters

Introductory Statistics with R by Peter DalgaardAn Introduction to Statistical Learning by Gareth JamesThe Art of R Programming by Norman MatloffExtending the Linear Model with R by Julian James FarawayR for Data Science by Hadley Wickham
Open Syllabus R Books
83 books — 3 voters

Kevin Guyan
“ Are your ways of working open, accessible and transparent? Traditional approaches to quantitative data collection and analysis are misunderstood as an objective account of reality; an assumption that masks decisions made throughout the design process. A queer approach to data is also influenced by biases and assumptions; those engaged in queer data practices therefore need to describe how decisions are made, in accessible language, and its effect on the results presented. Openness about the limi ...more ”
― Kevin Guyan, Queer Data: Using Gender, Sex and Sexuality Data for Action

Kevin Guyan
“ What is your end goal? The collection and analysis of gender, sex and sexuality data is not an objective in itself, nor is the ambition to gather ‘good data’ or fix the numbers. While paying attention to the potential for methods to misrepresent or exclude, such as strategic essentialism, ensure that data about LGBTQ people is ultimately used to construct a social world that values and improves LGBTQ lives. ”
― Kevin Guyan, Queer Data: Using Gender, Sex and Sexuality Data for Action

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