Data Science Books

Data Science Books

Data Science

Machine Learning: The Art and Science of Algorithms that Make Sense of Data by Peter Flach

  • If you need a ML book as a teacher, this book is definitely the one you need. It covers most ML algorithms, divided by genre (tree, rule, ensemble, etc.).
  • From a teaching point of view, the book is quite comprehensive. From a practical point of view, some chapters can be skipped as too theoretical.
  • I have purchased 5 books on Machine Learning - and this is the best one. Of course you need some mathematical background, but this book is highly readable and explains concepts in a great way
  • What an amazing book, I got it about a month ago for a self-study routine and every page of this book has been a joy.
  • Clearly written. Shows the theory and practice of machine learning. An invaluable tutorial.

Learning From Data by Yaser S. Abu-Mostafa

  • It is one of the best introduction books to the heart of machine learning. This book is excellent to use as complement to MOOC
  • Very well written book that is short and sweet with a good coverage.
  • Excellent introductory resource for understanding Machine Learning.
  • This book is a very good place to get started in understanding machine learning.
  • The book does a great job at explaining the basic principles of linear models (perceptron, linear regression, logistic regression), non-linear models (kernel tricks) and how they derive from one another.

Introduction to Statistical Learning by Gareth James

  • This is a wonderful book written by luminaries in the field
  • The book provides the right amount of theory and practice
  • It gives a very broad overview of statistical methods that overlap with ML.
  • An excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice
  • It is especially helpful for getting the fundamentals down without being bogged down in heavy mathematical theory.
  • A great way to kick-off corporate Learning units, or as an aid to help statisticians and learners communicate better.

Elements of Statistical Learning by Trevor Hastie

  • It provides the math/statistics behind a lot of commonly used techniques in data science.
  • Very comprehensive, sufficiently technical to get most of the plumbing behind machine learning.
  • The text is full with the equations necessary to root the methodology without engaging the reader with long proofs that would tax those of us employing these techniques in the business world.
  • The Elements of Statistical Learning is a comprehensive mathematical treatment of machine learning from a statistical perspective.

Statistical Inference by George Casella and Roger L. Berger

  • This is a good graduate-level textbook on the theoretical foundation of statistics.
  • This book does require a pretty high level of comfort with math (probability theory is based on measure theory, which is not trivial to understand).
  • It provides a comprehensive introduction to probability theory (without a measure theoretic approach) along with hypothesis testing.
  • This is one of the most popular and classical textbooks regarding probability and statistic.
  • Very comprehensive stats book. However, it can be challenging if you don't have some math/ stat background.Obviously this book is great, but I consider it more as a reference rather than as an instructional text.

Bayesian Data Analysis by Andrew Gelman

  • This book is excellent for Bayesian Techniques
  • It is a good idea to start learning Bayesian statistics with this book, as it covers very wide range of topics.
  • Excellent book. Right from the start it explains everything with good examples from authors' research in a very clear and understandable way.
  • Great expansion of a classic text. Too much to use to teach for a single course, but has many courses in it.

Beautiful Data: The Stories Behind Elegant Data Solutions by Toby Segaran and Jeff Hammerbacher

  • This book is good for thesis part.
  • This book contains twenty case-study like chapters written by people really engaged with real world data analysis problems.
  • It does not contain any mathematics, but explores areas like collecting data, finding practical ways of using data in analyses, scaling and selecting the best solutions very well.

Thinking with Data: How to Turn Information into Insights by Max Shron

  • This is another really interesting book which is not technical (=programming tutorial) either, but covers important topics on how to really use the data science power in decision making and real world problems.
  • I highly recommend this book to anyone who is involved in the development of software products
  • I found this book to be quite good (to the point and perfect for my needs)
  • The book provides a framework for defining the problem to be solved, not just "what can we do with this pile of data".
  • The book provides examples of scoping problems from multiple domains such as higher education, public policy, and retail.
  • This book is for anyone jumping into data science or a role that requires critical thinking or use of data to solve a problem.

Hadoop: The Definitive Guide by Tom White

  • Superb and Apt for beginners. Easy language and Enough examples to Learn. Clarity is also to be appreciated.
  • Excellent book for Hadoop. Consider it as a bible for Hadoop. Very lucid presentation. Highly recommendable.
  • Deep insights and lucid explanations on topics such as MapReduce, Hive and Pig... Go for it..
  • Good book for hadoop learner and having good concepts. I recommend this book to all hadoop learners ! ! ! !
  • The book is great , got everything I needed. Recommended for all the the students who are the neophytes in this field...
  • It's a very helpful book for Hadoop beginners and experts. It's good for both Programming and theory. Definitely suggestible for all Hadoopers.

Pattern Recognition and Machine Learning by Christopher M. Bishop

  • Bishop is absolutely clear, and an excellent writer as well.
  • The book has other virtues: best in class diagrams; judiciously chosen; a lot of material, very well organized; excellent stage setting (the first two chapters).
  • I like the book and would recommend it.
  • A satisfying book to have, especially if you use it as course material.

Convex Optimization by Stephen Boyd

  • Fantastic, pedagogical book.
  • Excellent book. Second to none on Convex Optimization problems.
  • I think this is the best book for getting into optimization.
  • It's simple with many examples and figures.
  • Excellent choice for engineers.
  • Very well organized.
  • This book is great! The writers tend to capture the theory of convex optimization in a concise way and further illustrate it by showing their applications. The book also has a rigorous set of exercises.

Bayesian Reasoning and Machine Learning by David Barber

  • This is one of the easy to read books with nice coverage. It accompanies a toolbox which gives an idea of implementations.
  • The first part of this book (I believe the first 7-8 chapters) is dedicated to carefully explaining all the theoretical underpinning of Bayesian analysis, graphical models and machine learning. This is the hardest part to cracking machine learning for anyone and I feel this book does a great job at that.

Probabilistic Graphical Models by Daphne Koller

  • This is a great book on the topic, regardless of whether you are new to probabilistic graphical models or have some familiarity with them but would like a deeper exploration of theory and/or implementation.
  • This is a stunning, robust book on the theory of PGMs. If you want the maths, the theory, all the full glory, then this book is superb.
  • Content is Very comprehensive.
  • Very useful book, and the best companion for the course.

Neural Networks for Pattern Recognition by Christopher M. Bishop

  • Great introduction to simple neural networks.
  • Mr Bishop's book is very well written and contains a lot of useful information on neural networks.
  • This book is more about pattern recognition than neural networks.
  • This book has the fundamentals covered very well.
  • Classic work in the NN field.
  • A book that worth reading. A very good reference. I used it for a class and also for future study.

Machine Learning: A Probabilistic Perspective by Kevin P. Murphy

  • An astonishing machine learning book: intuitive, full of examples, fun to read but still comprehensive, strong and deep! A great starting point for any university student―and a must have for anybody in the field.
  • This is a wonderful book that starts with basic topics in statistical modeling, culminating in the most advanced topics.
  • A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from big data.

Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig

  • This is indeed one of the best books on Artificial Intelligence (AI)
  • It's very well written and organized
  • Great content
  • It covers and effectively explains concepts and practices
Open chat
Powered by