Marty Rose, Data Scientist in the Acxiom Product and Engineering group, and an active member of the DMA Analytics Council shared the following list of data science books with the Council this week, and we thought the rest of the DMA family would also benefit.
“I didn’t compile this list and am grateful to Chris the original author, but I personally have spent many hundreds of dollars on hard copies of these books, only to find out you can now get them for free online!” he said. Marty especially recommends the first two books for getting started.
Regardless of your analytics and data background, skills or goals, there’s something for you in this list. Here they are, in no particular order.
- An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie & Tibshirani – This book is fantastic and has helped me quite a bit. It provides an overview of several methods, along with the R code for how to complete them. 426 Pages.
- The Elements of Statistical Learning by Hastie, Tibshirani & Friedman – This is an in-depth overview of methods, complete with theory, derivations & code. I’d definitely consider this a graduate level text. I’d also consider it one of the best books available on the topic of data mining. 745 Pages.
- A Programmer’s Guide to Data Mining by Ron Zacharski – This one is an online book, each chapter downloadable as a PDF. It’s also still in progress, with chapters being added a few times each year.
- Probabilistic Programming & Bayesian Methods for Hackers by Cam Davidson-Pilson – This book is absolutely fantastic. The author explains Bayesian statistics, provides several diverse examples of how to apply and includes Python code. Each chapter is an iPython notebook that can be downloaded.
- Think Bayes, Bayesian Statistics Made Simple by Allen B. Downey – Another great, easy to digest introduction to Bayesian statistics. The author’s premise is that Bayesian statistics is easier to learn & apply within the context of reusable code samples. It includes a number of examples complete with Python code. 195 Pages.
- Data Mining and Analysis, Fundamental Concepts and Algorithms by Zaki & Meira – This title is new to me. It’s a text book that looks to be a complete introduction with derivations & plenty of sample problems. 599 Pages.
- An Introduction to Data Science by Jeffrey Stanton – Overview of the skills required to succeed in data science, with a focus on the tools available within R. It has sections on interacting with the Twitter API from within R, text mining, plotting, regression as well as more complicated data mining techniques. 195 Pages.
- Machine Learning by Chebira, Mellouk & others – This is an introduction to more advanced machine learning methods. It includes chapters on neural networks, discriminant analysis, natural language processing, regression trees & more, complete with derivations. Each chapter is downloadable as a PDF. 422 Pages.
- Machine Learning – The Complete Guide – This one is new to me. It’s a collection of Wikipedia articles organized into chapters & downloadable in a number of formats. I didn’t realize they did this, but its a great idea. Because its a collection of individual articles, it covers quite a bit more material than a single author could write. This is an incredible resource.
- Bayesian Reasoning and Machine Learning by David Barber – This is an undergraduate textbook. It includes an overview, derivations, sample problems and MATLAB code. 648 Pages.
- A Course in Machine Learning by Hal Daumé III – Another complete introduction to machine learning topics. Each chapter is individually downloadable. 189 Pages.
- Information Theory, Inference and Learning Algorithms by David J.C. MacKay – Nice overview of machine learning topics, including an introduction and derivations. One nice feature of this book is that it has a chart that shows how various topics are related to one another. 628 Pages.
- Modeling with Data by Ben Klemens – Surprisingly, all of the code in this book is C, Klemens includes a section to defend this choice. The book includes plenty of code samples. 454 Pages.
- Mining of Massive Datasets by Rajaraman & Ullman – This book covers concepts and includes several domain specific examples. It includes plenty of derivation and little code. 493 Pages.
