When the answer to every question is available at the click of a button, reading a book to learn a skill might not be your first choice. Understandably. The Internet gives us instant access to timely and relevant information. You could download your data, write an algorithm, and Google your way through it. Then why bother with data science books? Because they are the perfect complement to a structured online learning program. While the online course might teach you specific methods and techniques, data science books offer a broader contextual understanding. And they stay with you for reference, where you can always go back to brush up your basics.
Top Data Science Books to Read to Launch your Data Science Career
In this blog post, we’ll cover a wide range of data science books about Python, R, data mining, tools, techniques and the overall social, political and economic impact of data science in today’s world.
Book #1: Practical Statistics for Data Scientists: 50 Essential Concepts
If you’re an aspiring data scientist with no background in statistics, this book is for you. A good data scientist has an intuition for maths and stats. But without formal training, most aspirants today struggle to develop a statistical perspective. Not anymore! In this data science book, Peter and Andrew Bruce offer a practical statistics guide for data science — what to use, what not to use, which ones are important, how to avoid common mistakes and more.
Book #2: Data Science from Scratch: First Principles with Python
This book by Joes Grus is a good introduction to data science — both theory and practice. Through the book, the author walks his readers through concepts of mathematics, statistics and data mining before delivering a crash course in Python to help you get your first data science project started. If you’re beginning lightly treading a data science career, this book is a good starting point.
Book #3: Python Data Science Handbook: Essential Tools for Working with Data
Among data scientists, Python is the most commonly used programming language — both for its ease of use and existing libraries. This Python primer collates resources on the entire data science stack including IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and others. If you already have a basic understanding of Python, this book will serve as a guide as you tackle complex data science use cases. It is available to read for free here.
Book #4: R for data science: Import, Tidy, Transform, Visualise, And Model Data
If Python is not your cup of tea, this is a great book to learn R as a programming language for data science projects. In this book, you’ll learn techniques such as data wrangling, data visualisation, exploratory data analysis, modelling, and inference. You can read it for free here.
Book #5: Mining of Massive Datasets
One of the key aspects of data scientist jobs is mining large datasets productively and meaningfully. This book teaches just this. It discusses using Mapreduce, performing link analysis, building recommendation systems, mining social-network graphs and much more. It offers practical algorithms that have been tried and tested in some of the world’s most complex big data, which you can apply to your own. Read the book for free here.
Book #6: Storytelling with Data: A Data Visualisation Guide for Business Professionals
An important and often neglected part of data scientist jobs is to communicate insights impactfully and effectively. In this book, author Cole Nussbaumer Knaflic teaches the fundamentals of data visualisation and how to use it in storytelling. She uses real-world examples to demonstrate possibilities and shows how to think like a designer to engage and educate your audience.
Book #7: Data Science for Business
Strictly speaking, this is not as much a data science book as it is a business book. It doesn’t teach you ‘data science’ but how to apply it to business. If you’re a mid-level IT or business management professional looking to transition to data science and use data analytical thinking in your work, this is a great read.
Book #8: Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
While getting into the practice of data science, it is important to understand the range of possibilities that exist within each set of data. In this book, ex-Google data scientist and economist Seth Stephens-Davidowitz mines data from people’s online behaviour to understand how people think. He explores everything from personal desires to political views from the point of view of data.
Like the back cover says, “There is almost no limit to what can be learned about human nature from Big Data—provided, that is, you ask the right questions.” Remember that it’s not a technical book, but more a sociological one.
Book #9: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy
With great power comes great responsibility. This book by Cathy O’Neil presses on the fact that where there are possibilities, there will also be risks. Data scientists often believe that data — and the insights from it — are unbiased. O’Neil argues that this is false. She shows how they often have the same biases as humans do, how they reinforce discrimination and why this is so. This is a great book to understand the real-world applications of data science and how an algorithm you write can actually change someone’s life.
Book #10: Doing Data Science: Straight Talk from the Frontline
In this book, based on Columbia University’s Introduction to Data Science program, world-renowned data scientists share lessons and techniques that have been battle-tested. It covers a wide range of topics from data analysis, algorithms, data wrangling, modelling and visualisation to social networks and data journalism. If you’ve basic knowledge of maths, statistics and some programming skills, this book will walk you to the next level.
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