For 2017, as part of my yearly planning, I tried to make more time to read several books that I had selected from my list (tsundoku, anyone?). It felt like a good challenge, both in planning and execution, and now I can say it was a great initiative to move forward in both my personal and professional life.
From january to december, I had read the following books:
Data science
- Mastering Social Media Mining with R by Sharan Kumar Ravindran and Vikram Garg (goodreads)
- Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil (goodreads)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham and Garrett Grolemund (link | goodreads)
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce and Andrew Bruce (goodreads)
- Analyzing the Analyzers: An Introspective Survey of Data Scientists and Their Work (link)
- Text Mining with R: A Tidy Approach by Julia Silge and David Robinson (link | goodreads)
- Developing Data Products in R by Brian Caffo (link)
- Think Like a Data Scientist: Tackle the data science process step-by-step by Brian Godsey (goodreads)
- Statistics Done Wrong: The Woefully Complete Guide by Alex Reinhart (goodreads)
Project management
- Commitment: A Novel about Managing Project Risk by Olav Maassen, Chris Matts and Chris Geary (goodreads)
Fiction
- The World According to Garp by John Irving (goodreads)
Essay
- Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari (goodreads)
- We Should All Be Feminists by Chimamanda Ngozi Adichie (goodreads)
- The Missionary Position: Mother Teresa in Theory and Practice by Christopher Hitchens (goodreads)
- The Perfect Wagnerite: A Commentary on The Niblung’s Ring by George Bernard Shaw (goodreads)
Reference
Other material I’ve often used as a reference for my data-related activities:
- Advanced R by Hadley Wickham (link | goodreads)
- An Introduction to Statistical Learning: With Applications in R by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (link | goodreads)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman (link | goodreads)
- Computer Age Statistical Inference: Algorithms, Evidence, and Data Science by Bradley Efron and Trevor Hastie (link | goodreads)
- The math refresher chapters of the Deep Learning book by Ian Goodfellow, Yoshua Bengio and Aaron Courville (link | goodreads)
- R Packages: Organize, Test, Document, and Share Your Code by Hadley Wickham (link | goodreads)