Before, entrepreneurs would usually pay more attention to their gut rather than trust data. But times have changed and many of the people building businesses today are putting analytics and data science at the center of their strategy and operations.
Well, if you’re one of them, then you should take a look at this! We’ve rounded up the best 15 data science books that will help you develop a deeper understanding of the subject and its applications to business and entrepreneurship. These are arranged from the least to the most technical, giving you a good step-by-step overview of the field as a whole, in the context of your business needs.
15 must-read books in Data Science
From Amazon
The Lean Startup: How Today’s Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses: This isn’t exactly a data science book, but it contains many insights that could drive your business forward through innovation and technology. Written by Eric Ries, this book reveals some proven strategies for implementing new technology in business situations and maintaining flexibility in the face of technological change.
From Amazon
Lean Analytics: Use Data to Build a Better Startup Faster: Whether you’re a startup founder aiming to disrupt an industry or an intrapreneur hoping to bring change from within, this book can help you by teaching the right way to take your business from initial idea to market, through the use of analytics and data.
From Amazon
Big Data at Work: Dispelling the Myths, Uncovering the Opportunities: It could be hard to understand how important big data can be in the workplace—but this data science book gives a solid overview of the technology and its implications. It also provides a more business-focused explanation on success factors in implementing big data projects and hiring a good team of data scientists.
From Amazon
Keeping Up with the Quants: Your Guide to Understanding and Using Analytics: No matter your interests or the industry you’re working in, the world is full of data. To be a successful manager, it’s more important than ever to harness and make sense of this data. This book, inspired in part by the Moneyball story, discusses the quantitative analysis for decision making, and the application of data science in business.
From Amazon
Numsense! Data Science for the Layman: No Math Added: Everyone has to start somewhere, and this book on data science is perhaps the best way for a layman to get some knowledge of the industry and the science itself. This book promises an absence of math, a gargantuan task in the algorithm-based industry. However, by dedicating each chapter to the works of every important algorithm in data science, it allows for a very practical understanding of the knowledge that will require later on in your journey.
From Amazon
Data Analytics Made Accessible: 2017 Edition: The topic of data science can often be dense, locked behind walls of chunky and unreadable text—but not with this data science textbook. Concise and conversational, this is an easy read that is still filled to the brim with important knowledge, notably the concrete real-life case studies displaying how the science can be applied in business situations. It even includes a short R tutorial. This particular edition also gives some valuable insights and suggestions based on the response of reviewers of previous editions, making for an updated and modern view of the advancements in data science.
From Amazon
The Art of Data Science: It can be tough for some to view data analytics as anything other than a rigid and difficult-to-acquire skill, given its focus on a fundamental knowledge of mathematics and statistics. However, in reality, not only can the interpretation of data produce a wide range of useful business insights, but the very analysis of data is much more commonsensical than you might expect.
From Amazon
Data Science for Dummies: Not surprisingly, this book is a fantastic starting point for anyone seeking to pick up this skill. It gives a quick overview of all things data science, with a broad focus on a variety of business cases, giving you a good idea of what to expect when making use of your budding company’s databases. This textbook-like resource will help you decide whether your startup could benefit from further exploration of data, even going down into the details of which type of analysis can be applied to certain business cases.
From Amazon
The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists: Now that you have a good idea of what to expect from the science and backend aspects of data science, it’s also important to have a grasp on the more “human” aspect of the job. This book takes a different approach from most others, interviewing veterans of the industry, like Uber’s former data science chief Kevin Novak, and getting their input on how data analytics relates to business and society at large. This book won’t just equip you with the rough technical know-how, but will also give you industry tips and tricks to launch your learning journey.
From Amazon
Data Science for Business: Written with the everyday businessman in mind, this data science book is a great way to dive into big data analytics as it relates to your business needs, introducing useful data analysis principles. Through this book, the authors aim to provide enough knowledge and instill enough confidence in any business-person, so that they can make the most efficient use of their data scientists and analytics teams.
From Amazon
Data Science from Scratch: First Principles with Python: Python is perhaps the most important tool that an aspiring data scientist can learn, and it’s a skill that any entrepreneur should consider picking up, even if it’s just to understand what your team of supporting data scientists is doing. This book is the first step into the actual industry of data science, providing readers with a good overview of the technical details of data science.
From Amazon
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics: Aside from Python, R is another important programming platform to understand. Quick and efficient, this textbook for data science will help you produce solutions in R in no time, allowing you to address general statistics, from graphics to linear regressions, explaining each solution and offering excellent insights into not only the data, but the recipes to obtain that data. This will give you a good sense of the kind of analytics your company will need to build in order to establish a good analytical backbone.
From Amazon
Big Data For Business: Your Comprehensive Guide to Understand Data Science, Data Analytics and Data Mining to Boost More Growth and Improve Business: Even with the rough knowledge of what data science is, and how to make use of programming languages like R and Python to apply your knowledge to real life, in order to be a successful entrepreneur in the age of big data it’s also important to understand how best to apply all this information into a real business situation. Enter this data science textbook, which teaches readers how to go through the entire business data science process, from processing data to analyzing it and then acting on it. It identifies growth areas and opportunities, nudging readers toward the exploration of data science as a practical business tool.
From Amazon
Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies: As the industry of data analytics progresses, machine learning has also begun to become more popular. By using machine learning and similar predictive technologies, the field of predictive data analytics is rising, especially in the business world. This data science textbook provides some keen insights into the approach, without the bombardment of technical terms common in other resources.
From Amazon
Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python: Data science can be used in many different contexts, and examining and predicting human behavior is perhaps one of the most interesting. Written for both managers and students, Northwestern University’s Thomas W. Miller introduces some of the real-world applications of data science in marketing, whether it may be in target marketing, brand positioning, or demand estimations. Whether your business deals in marketing or not, a good company will have a good marketing position, and that’s where this book comes in.
No matter the field, reading books is a must for all entrepreneurs, the most effective of whom are constantly seeking inspiration and new ways to improve their businesses. As understanding data becomes more important to the success of businesses of all sizes, more books on data science are flooding the market. That can make it tough to find the best resources. We hope this list gives you a good place to start.
