A unique top 10 list of book recommendations, for each of 10 categories this list provides a top paid and top free book recommendation. If you're interested in books on data, this diverse list of top picks should be right up your alley.
The true data enthusiast has a lot to read about: big data, machine learning, data science, data mining, etc. Besides these technology domains, there are also specific implementations and languages to consider and keep up on: Hadoop, Spark, Python, and R, to name a few, not to mention the myriad tools for automating the various aspects of our professional lives which seem to pop up on a daily basis. There are a lot of topics to keep abreast of. Fortunately (unfortunately?) there is no shortage of books available on all of these subjects.
There are a lot of lists available of the top books in particular categories related to data. In fact, KDnuggets has previously, and rather recently, put together such lists on data mining, databases & big data, statistics, AI & machine learning, and neural networks. But these were based on Amazon top sellers in narrow categories, without editorial discretion or consideration for freely-available content and e-books.
First off, let's get this out of the way: the title of this post is misleading. This inclusive list of essential books for the data enthusiast (or practitioner) recommends a top paid and free resource in each of 10 categories. Let's face it: though we may work or be otherwise directly involved in a limited number data avenues, we generally tend to have an understanding of a greater number of these avenues, as both a practical matter and one of interest.
So, while a Hadoop expert may not need expert-level insight into deep learning, chances are that they have a more-than-passing interest in the subject. This post is a chance to solidify these interests and provide material suggestions for the data enthusiast looking to widen their knowledge base.
Editor's note: It is important to point out that KDnuggets receives no incentive, financial or otherwise, related to any of these recommendations, nor does it take part in any affiliate sales programs. These recommendations are made solely in the interest of our readers.
Keep in mind that there may be overlap in many of these categories, which is inevitable (see: The Data Science Puzzled, Explained). Often the focus of the material determines its categorization, as opposed to simply the material itself.
Data Science
Top Paid Recommendation: Data Science for Business
When trying to learn about a new field, one of the most common difficulties is to find books (and other materials) that have the right "depth". All too often one ends up with either a friendly but largely useless book that oversimplifies or a heavy academic tome that, though authoritative and comprehensive, is condemned to sit gathering dust in one's shelves. "Data Science for Business" gets it just right.
Top Free Recommendation: The Art of Data Science
This book describes the process of analyzing data in simple and general terms. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.- Official Website
Big Data
Top Paid Recommendation: Big Data: Principles and Best Practices of Scalable Realtime Data Systems
I have rarely seen a thorough discussion of the importance of data modelling, data layers, data processing requirements analysis, and data architecture and storage implementation issues (along with other "traditional" database concepts) in the context of big data. This book delivers a refreshing comprehensive solution to that deficiency.
Top Free Recommendation: Big Data Now: 2015 Edition
In the four years that O’Reilly has produced its annual Big Data Now report, the data field has grown from infancy into young adulthood. Data is now a leader in some fields and a driver of innovation in others, and companies that use data and analytics to drive decision-making are outperforming their peers.- Official Website
Apache Hadoop
Top Paid Recommendation: Hadoop: The Definitive Guide
I appreciate that this book covers high-level concepts as well as dives deep into the technical details that you will need to know for the design, implementation and day-to-day running of Hadoop and its various associated technologies.
Top Free Recommendation: Hadoop Explained
Hadoop is one of the most important technologies in a world that is built on data. Find out how it has developed and progressed to address the continuing challenge of Big Data with this insightful guide.- Official Website
Apache Spark
Top Paid Recommendation: Learning Spark
The information that is available on the Internet is great, but this book brings much of it together in one place. If you want to learn to think like a Spark programmer--*not* the same as thinking like a programmer--this is the place to begin.
Top Free Recommendation: Mastering Apache Spark
This collections of notes (what some may rashly call a "book") serves as the ultimate place of mine to collect all the nuts and bolts of using Apache Spark. The notes aim to help me designing and developing better products with Spark.- Official Website
Theoretical Machine Learning
Top Paid Recommendation: Pattern Recognition and Machine Learning
The author is an expert, this is evidenced by the excellent insights he gives into the complex math behind the machine learning algorithms. I have worked for quite some time with neural networks and have had coursework in linear algebra, probability and regression analysis, and found some of the stuff in the book quite illuminating.
Top Free Recommendation: Elements of Statistical Learning
The good news is, this is pretty much the most important book you are going to read in the space. It will tie everything together for you in a way that I haven't seen any other book attempt.
Practical Machine Learning
Top Paid Recommendation: Python Machine Learning
This is a fantastic book, even for a relative beginner to machine learning such as myself. The first thing that comes to mind after reading this book is that it was the perfect blend (for me at least) of theory and practice, as well as breadth and depth.
Top Free Recommendation: An Introduction to Statistical Learning with Applications in R
This book provides an introduction to statistical learning methods. It is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences. The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.- Official Website
Deep Learning
As the selection of paid deep learning books is slim at the moment, here are a pair of free selections.
Top Free Recommendation #1: Neural Networks and Deep Learning
Neural Networks and Deep Learning is a free online book. The book will teach you about:
- Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
- Deep learning, a powerful set of techniques for learning in neural networks
- Official Website
Top Free Recommendation #2: Deep Learning
The in-preparation, likely to-be definitive deep learning book of the near future, written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The development version is updated monthly, and will be freely available until publication.
Data Mining
Top Paid Recommendation: Data Mining: Concepts and Techniques, Third Edition
Data Mining is a comprehensive overview of the field, and I think it is best for a graduate class in data mining, or perhaps as a reference book. The book's focus is on technique (i.e., how to analyze data, including preparation), and it addresses all the major topics in the field including data storage and pre-processing. However, the book is really about classification methods, and the 2 chapters on cluster analysis are particularly strong and thorough.
Top Free Recommendation: Mining of Massive Datasets
The book, like the course, is designed at the undergraduate computer science level with no formal prerequisites. To support deeper explorations, most of the chapters are supplemented with further reading references.- Official Website
SQL
Top Paid Recommendation: Learning SQL, Second Edition
If you're writing any type of database driven code and you think that you don't need to understand SQL, read this book. You do need to understand it, and this book teaches it very well.
Top Free Recommendation: Learn SQL The Hard Way
This book will teach you the 80% of SQL you probably need to use it effectively, and will mix in concepts in data modeling at the same time. If you've been fumbling around building web, desktop, or mobile applications because you don't know SQL, then this book is for you. It is written for people with no prior database, programming, or SQL knowledge, but knowing at least one programming language will help.- Official Website
Statistics for Data Science
Top Paid Recommendation: Statistics in Plain English, Third Edition
I work as a Data Analyst and deal with statistics on a daily basis. I am expected to know all the models and algorithms. Although statistical software does everything for me, figuring out the numbers the software chews out becomes the tricky part. I majored in Biotechnology and was alien to these statistics for the major part of my life. Long story short, I required a solid foundation guide that would help me get acclimatized to the concepts.
Top Free Recommendation: Think Stats: Probability and Statistics for Programmers, Second Edition
- Official WebsiteThink Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets.
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