Top 10 Data Science Books For Your Career

Learning data science can be overwhelming. There are hundreds of books, online courses, and graduate degrees. Where do you start? Data science is an interdisciplinary field which contains methods and techniques from fields like statistics, machine learning, Bayesian etc.

They all aim to generate specific insights from the data. In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science.

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1. Head First Statistics: A Brain-Friendly Guide

Just like other books of Headfirst, the tone of this book is friendly and conversational. The book covers a lot of statistics starting with descriptive statistics mean, median, mode, standard deviation and then go on to probability and inferential statistics like correlation, regression, etc.

If you were a science or commerce student in school, you may have studied all of it, and the book is a great start to refresh everything you have already learned in a detailed manner.

There are a lot of pictures and graphics and bits on the sides that are easy to remember. You can find some good real-life examples to keep you hooked on to the book. Overall a great book to begin your data science journey.


2. Introduction to Probability

If you are from a math background in school, you might remember calculating the probability of getting a spade or heart from a pack of cards and so on.

This is perhaps the best book to learn about probability. The explanations are pretty neat and resemble real-life problems. If you have studied probability in school, this book is a must-have to further your knowledge of the basic concepts. If you are going to learn probability for the first time – this book can help you build a strong foundation in the core concepts, though you will have to work for a little longer with the book.

The book has been one of the most popular books for about 5 decades and that is one more reason why it should definitely be on your bookshelf.


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3. Practical Statistics for Data Scientists

If you are a beginner, this book will give you a good overview of all the concepts that you need to learn to master data science. The book is not too detailed but gives good enough information about all the high-level concepts like randomization, sampling, distribution, sample bias, etc.

Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. The book also surprises one with a survey of ML models.

This book covers all the topics that are needed for data science. It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.


4. Python Data Science Handbook

Recent data shows that Python is still the leading language for data science and machine learning. The Python Data Science Handbook is the perfect reference for boosting your Python skills. As a data scientist, you’ll often be asked to work on numerous tasks, but a majority of your time will be spent on manipulating data and data cleaning. This is a perfect reference to keep close by for those frequent data manipulation tasks using Pandas.

Here’s a number of other important data science topics this book covers:

  • IPython Shell
  • Numpy for computations
  • Data manipulation with Pandas
  • Data visualizations with Matplotlib
  • Machine learning with Scikit-Learn


5. R for Data Science

If you want to make yourself marketable to employers and stay current with your data science skills, you should have a good handle on R.

R is neck in neck with Python as the top programming languages for data science. A recent poll of the data science community indicated that 52.1% of responders use R, only slightly less than 52.6% which use Python. If you want to sharpen your R skills, R for Data Science is the perfect book. It covers the basics for new R users, such as data cleaning, but also gets into more advanced topics as well.

Data scientists can spend up to 80%  of their time cleaning data, so this is a reference you will definitely want to keep close by. This book is a great general R reference from Hadley Wickham and Garret Grolemund, two of the top developers in the R community.

Here’s a number of topics covered:

  • Exploration
  • Wrangling
  • Programming
  • Modelling
  • Communication


6. Introduction to Machine Learning with Python: A Guide for Data Scientists

Introduction to Machine Learning with Python: A Guide for Data ...

This is a book that can get you kick-started on your ML journey with Python. The concepts are explained as if to a layman and with sufficient examples for a better understanding. The tone is friendly and easy to understand.

ML is quite a complex topic, however, after practising along with the book, you should be able to build your own ML models. You will get a good grasp of ML concepts. The book has examples in Python but you wouldn’t need any prior knowledge of either maths or Programming languages for reading this book.

This book is for beginners and covers basic topics in detail. However, reading this book alone won’t be sufficient as you get deeper into ML and coding.


7. Pattern recognition and machine learning

This book is for all age groups, whether you are an undergraduate, graduate or advanced level researcher, there is something for everyone. If you have a Kindle subscription, this book will cost you nothing. Get the international edition that has colorful pictures and graphs making your reading experience totally worth it.

Coming to the content, this is one book that covers machine learning inside out. It is thorough and explains the concepts with examples in a simple way. Few readers could find some of the terms tough to understand but you should be able to get through using other free resources like web articles or videos.

The book is a must-have if you are serious about getting into machine learning, especially the mathematical (data analytics) part is exhaustive in nature. Though you can use the book for self-learning, it would be a better idea to read it alongside some machine learning courses.


8. Mining of Massive Datasets

This is a great book developed from various Stanford courses on large scale data mining and network analysis. The focus is on data-mining very large datasets.

This is important for implementing production level models at scale. Large companies like Google receive hundreds of millions (or more) search queries per day, so they are especially interested in mining very large datasets.

Some topics covered in this book include:

  • Mapreduce
  • Mining data streams
  • Link analysis
  • Recommendation systems
  • Mining social-network graphs
  • Dimensionality reduction
  • Large-scale machine learning


9. Deep Learning

Deep learning is one of the hottest fields in machine learning. Companies like Google, Facebook, and Amazon need highly skilled professionals with expertise in deep learning. What is it that makes deep learning so powerful?

It automates one of the most difficult parts of machine learning, feature discovery. Rather than spending hours of time manually engineering new features in creative ways, deep learning automates the process. If you’re new to deep learning, this book is a must.

Even if you have some experience, those advanced deep learning practitioners will benefit as well. This book is presented in an easy to read slide format with lots of bullets and pictures.

Here are some of the topics covered:

  • Intro and explanation of the importance of deep learning
  • Algorithms – backpropagation, convnets, recurrent neural nets
  • Unsupervised deep learning
  • Attention mechanisms


10. Business analytics: the science of data-driven decision making


This is an awesome in-depth book that explains the theory as well as practical applications to give wholesome knowledge. The author approaches the topics with subtlety and presents many case studies that are easy to understand, comprehend and follow.

The book has everything from economics, statistics, finance and all you need to start learning data science. The book has been written with a lot of effort and experience and the way insights have been presented shows the same.

It includes statistical and analytical tools, machine learning techniques and amalgamates basic and high-level concepts very well. You will also learn about scholastic models and six sigma towards the end of the book.


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