“Big data is at the foundation of all the megatrends that are happening.” – Chris Lynch, big data expert
We live in a world surrounded by data. At present, 2.7 Zettabytes of data exist in the digital universe, and through the ability to understand, quantify, and use it to our advantage, it’s possible to gain insights and intelligence like never before. The ever-evolving discipline of data science extends to almost every sector and industry imaginable, on a global scale. Gaining an astute understanding of data science as a concept as well as a practice comes with a wealth of advantages.
Why You Need To Read Data Science Books
In 2013, less than 0.5% of all available data was being analyzed and used – even today, there are unfathomable amounts of data yet to be explored. For savvy data scientists, the potential to unlock this seemingly infinite ocean is colossal.
Data science, also known as data-driven science, covers an incredibly broad spectrum. This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining.
One more time, we will go over some must-have books to add to your business intelligence bookshelf. After our top 10 data analytics books, top 12 data visualization books, top 9 SQL books, we’re back at it with our top 8 data science books!
If you would like to gain a sound comprehension of data science and take your understanding of the world to the next level, reading the best books for data science is a must. That said, these books will get your journey into the immersive world of big data off to the best possible start.
1) Machine Learning Yearning, by Andrew Ng
Best for: Someone who has become all too aware of the machine learning and artificial intelligence craze but needs to get a grip on the subject.
Driven by the acquisition and processing of complex information, machine learning is an area of data science that has emerged monumentally in recent years. In fact, 20% of C-level executives worldwide are currently in the process of mastering machine learning to make it a core part of their business.
With artificial intelligence changing the face of both our personal and professional lives, understanding the concept of machine learning and how silos of big data can be used to create autonomous, self-evolving machine learning systems is essential if you want to grasp the importance of data and how it’s used in the modern world.
Written by renowned computer scientist Andrew Ng, this gripping read not only offers an accessible introduction to machine learning and big data, but it also proves an excellent resource on collecting data, utilising the power of deep end-to-end learning, and facilitating the sharing of key insights with a machine learning system.
2) Python for Data Analysis: Data Wrangling With Pandas, NumPy and IPython, by Wes McKinney
Best for: Someone with a sound working knowledge of Python who wants to understand how to use the language to enhance their data insights.
As one of the world’s most revered and widely used high-level programming languages, Python is a robust and versatile tool, particularly in the modern age.
The brainchild of American statistician and data scientist Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython takes the reader deep into the realms of the language and its enormous potential for manipulating, processing, cleaning, and crunching data in Python.
If you’re looking to use Python as an effective means of solving a broad set of data analysis problems that will enhance the intelligence and productivity of your business, this book boasts a host of actionable tips and thought-provoking takeaways. A must-read for those wrestling with Python.
3) The Signal And The Noise: Why So Many Predictions Fail – But Some Don’t, by Nate Silver
Best for: The CEO, Chief Digital Officer, Chief Information Officer, or business owner looking to seriously enhance their predictive analytics skills, both practically and theoretically.
A New York Times Best Seller – and for good reason – The Signal and the Noise is a masterclass in using the power of big data analytics to make valuable predictions in an informed and potent way. It’s also one of the best books on data science around.
Crafted by American statistician Nate Silver, a spokesperson famed for successfully predicting the 2012 US Presidential election results, this book uncovers the genuine art and science of making predictions from data. Peppered with real-world case studies, interesting examples of data prediction, and citations of epic data-based failures, this book shows the reader how to filter out the noise and hone in on the right insights to make projections that not only matter, but also ensure sustainable levels of success.
4) Automate This: How Algorithms Came To Rule Our World, by Christopher Steiner
Best for: The technically-minded wizard or digital tech enthusiast looking to bridge the gap between big data analytics, complex algorithms, and the way these elements will shape our future lives.
Data science is largely about predictions, but a significant part of this ever-expanding discipline also boils down to sophisticated algorithms.
In this thought-provoking and, in many ways, timeless work of data science prose, author, and prolific programmer Christopher Steiner explains how algorithms are increasingly being used to take on high-level pursuits that were once tackled only by human beings with niche training – areas including medical diagnosis and foreign policy analysis.
Once you pick it up, Automate This: How Algorithms Came to Rule Our World is nigh on impossible to put down, gripping you from start to finish with its intuitive style and host of stunning observations on how, in today’s world, algorithms have far exceeded the expectations of their creators. A must for any budding data scientist’s home library.
5) Storytelling With Data: A Data Visualization Guide for Business Professionals, by Cole Nussbaumer Knaflic
Created by storytelling expert Cole Nussbaumer Knaflic, this methodical handbook is not only entertaining, but it also provides deep-rooted insights into a branch of data science that is often overlooked: the art of storytelling through metrics.
One of the best books for data science you’re likely to read this decade, Cole explains approaches to getting rid of unnecessary data that obscures clear communication and using these insights to build an effective narrative that connects with users on an undeniably personal level. The modern age content writer’s dream ticket.
In the meantime you get a hand on that book, you can have a look at our dashboard storytelling tips, to know how to tell a great story after learning how to make a dashboard in 9 easy steps.
6) Inflection Point: How the Convergence of Cloud, Mobility, Apps, and Data Will Shape the Future of Business, by Scott Stawski
Best for: The budding data manager or data miner with a desire to make sense of information in the modern age and beyond.
As far as books on data science go, this one is perhaps one of the most forward-thinking one in existence.
Penned by Scott Stawski, a data management leader at Hewlett Packard, Inflection Point focuses on how swift changes in cloud computing, big data, mobile devices, and apps are morphing the way businesses compete. With mind-blowing observations, astute predictions, and valuable takeaways, this data science book is a must-read for anyone trying to sift through silos of information and get ahead in today’s – and tomorrow’s – world.
7) Hadoop, the Definitive Guide: Storage and Analysis at an Internet Level, by Tom White
Best for: The wide-eyed, up and coming Apache Hadoop warrior with a hunger for building scalable systems from data.
In one of the best books on data science regarding processing language, Tom White takes his readers on a data-based journey to help them understand the importance of Hadoop and how, if used wisely, it can do a multitude of incredible things.
These incredible things include the ability to build and manage scalable systems with Hadoop and successfully running large Hadoop clusters. As it’s so well-formatted and digestible, dipping in and out of the various chapters of the book is as simple as it gets.
8) Doing Data Science: Straight Talk from the Frontline, by Cathy O’Neil and Rachel Schutt
Best for: The budding data scientist looking for a comprehensive introduction to the field.
One of the general best books on data science available, Doing Data Science: Straight Talk from the Frontline serves as a clear, concise, and engaging introduction to the field.
Based loosely on Columbia University’s definitive Introduction to Data Science class, this book delves into the popular hype surrounding big data. Written with confidence and a clear, practical understanding of the topic, this essential guide to data science will help you hit the ground running and bestow you with the knowledge you need to thrive in this ever-growing field of expertise.
A collaborative effort from mathematician Cathy O’Neil and News Corp’s Rachel Schutt, this book is cohesive and easy to digest – the go-to resource for any up and coming data scientist.
Let’s get started!
When it comes to data science, there is an incredible amount to learn. In our opinion, these best data science books will help you gain the knowledge you need to embark on your long and rewarding journey towards data enlightenment. Thanks to that understanding, you’ll be able to tap into the potential of data analytics and create strategic advantages for your business with the help of online BI tools.
If you’re looking to make your business smarter, savvier, and more productive, these top 8 business intelligence books will make a great start.