The fields of data science, analysis and its neighbouring disciplines have risen to prominence in the last two decades or so. In the US alone, data-related roles are expected to increase from some 364,000 in 2017 to in excess of 2.5 million by 2028.
Jobs involving keywords related to data analysis, data science, data engineering and programming seem to be forever rising and the supply deficit is only ever increasing as employers struggle to roles quickly enough.
There is great demand for all types of data practitioners and the field itself is moving at an unprecedented speed.
This is a field of considerable intellectual and practical depth, a field that could shape the future of our species – some will argue that it already has.
Like any well-studied subject, the fields of data analysis, data science and data engineering are well-documented in literature.
Here is just a small selection of 15 of the best books for data analysis and data science.
These books are suitable for a wide spectrum of data professionals, data practitioners and anyone else who has interest from inside or outside the world of IT and computer science.
Table of Contents
Data Analytics Made Accessible: 2021 Edition – Anil K. Maheshwari
The field of data does not need to be an entirely esoteric subject and this introductory work proves that many aspects of data and its relevant disciplines are indeed accessible. By providing a concise but rounded overview of data, what it is, how it works and why it’s useful, this book’s bitesize approach is suitable for beginners and novices alike.
It serves as a great way for more experienced practitioners to evaluate their knowledge of the fundamentals, but is particularly useful to budding data professionals who are looking to gain greater insight into key analytical concepts.
This superb book is part of university reading lists worldwide, not just in data-specific fields but also in statistics, IT and computer science in general.
It’s also worth mentioning that this book is updated each year to reflect the first-changing nature of the field. A true must-have.
Data Genres:
- Data analysis
- Data modelling
- Mining, transforming, cleaning
- Business intelligence
Suitable For:
- Everyone looking for a solid foundational knowledge in data analysis
Big Data: A Revolution That Will Transform How We Live, Work, and Think – Viktor Mayer-Schönberger and Kenneth Cukier
Another quality book on Big Data. This book provides a solid mixture of opinion and objective information. It has a solid focus on the data analytics side of Big Data, and inspires readers as to how data can be used in their field.
There’s a fair bit of conjecture and abstraction involved in this book, but it’s right to also question the pros and cons of Big Data, AI and neighbouring technologies. A great book that investigates the cause and effect of the big data revolution, and its probable course for the future.
Data Genres:
- Data analysis
- Big Data
- Data futurism and philosophy
- Business intelligence
Suitable For:
- Anyone looking for practical yet philosophical insights into Big Data and its future
Artificial Intelligence: A Guide for Thinking Humans – Melanie Mitchell
Melanie Mitchell is a renowned computer scientist and award-winning author – this book is a captivating and gripping work. Exploring the history of AI, its exponential growth and success and the emergence of genuine and well-founded fears, Artificial Intelligence: A Guide for Thinking Humans is an excellent combination of articulate prose and technical sensibility on a complex topic.
This book also provides a semi-biographical account of key actors in AI development such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize-winning author of Gödel, Escher, Bach.
This is a great book for those working either inside or outside of the data industry. It’s pragmatic, superbly written, non-esoteric but crammed with information and humorous but genuine insight from a top professional in data and AI.
Data Genres:
- AI
- Machine learning
- Data futurism and philosophy
- Data analysis and engineering
Suitable For:
- Anyone inside or outside of the data or AI industry that is looking for a thorough but captivating look back in history and ahead into the future
The Hundred-Page Machine Learning Book – Andriy Burkov
Machine learning in 100 pages?! This much revered ML and AI textbook attempts exactly this and writer Andriy Burkov executes it with great excess. This Artificial Intelligence PhD holding author has decades of practical experience within the AI and ML industries, both in terms of R&D and theoretical development.
This superb book can be read cover-to-cover in a matter of a few hours or less. It’s easy to access for those with only beginner to moderate experience in AI and/or ML.
There is a decent helping of maths equations too, which readers shouldn’t shy away from. A top-quality, compact introduction to machine learning’s key tenets, principles, concepts and ideas.
Data Genres:
- AI
- Machine learning
- Mathematical and computational modelling
- Data analysis and engineering
Suitable For:
- Machine learning beginners and novices
Business unIntelligence: Insight and Innovation Beyond Analytics and Big Data – Barry Devlin
This book is more specifically angled towards data analytics. Dr Barry Devlin was a key figure in the development of data warehousing architecture throughout the 1980s. This is his reflection upon development in the field as well as a systematic overview of data engineering and analysis in the current era.
This is a business-centric book that focuses on providing practical advice and insight to professionals working within the field. It advises data practitioners on what businesses want, and why, as well as guidance on how to stay competitive. An excellent book for current and prospective professional data practitioners.
Data Genres:
- Data analysis
- Business intelligence
- Data storage and analysis
- Data engineering for business
Suitable For:
- Professionally minded data practitioners who are looking to improve their business knowledge
Creating Value With Social Media Analytics: Managing, Aligning, and Mining Social Media Text, Networks, Actions, Location, Apps, Hyperlinks, Multimedia, & Search Engines Data – Gohar F. Khan
Social media is absolutely fundamental to the success and growth of modern businesses. This book packs as much info as its verbose title suggests; it’s a very complete insight into how to develop social media strategies with data techniques.
This is another solid professionally-oriented book. It’s targeted at those who want/need to integrate modern social media techniques into their business arsenal.
It contains a multitude of information on customer loyalty, engagement, lead generation, traffic, etc. An excellent practical guide into the field of social media data analytics.
Data Genres:
- Social media
- Data analysis
- Business intelligence
- Digital strategy
Suitable For:
- Data practitioners who work on/with social media analytics
Too Big to Ignore: The Business Case for Big Data – Phil Simon
Big Data is one of the key data buzzwords of the 21st-century. Whilst Big Data has a now-ubiquitous familiarity to many data engineers and analysts, a big question remains as to how and why businesses should take it seriously. This book uses case studies to get to the bottom of how data is being used on a massive scale, not just for commercial or corporate purposes, but also in public or state-owned organisations.
This book is well-contextualised in our modern world; it doesn’t contain too much theory or overly dense content. It’s a grounded, realistic portrayal of the Big Data era that helps any aspiring data engineer or analyst to think creatively when it comes to solving problems with data.
Data Genres:
- Big Data
- Business intelligence
- Data futurism and philosophy
- Data analysis and engineering
Suitable For:
- Those looking for a better understanding in Big Data in a modern context
The Quick Python Book – Naomi Ceder
Whilst not strictly centred around data analysis at all, for those looking into Python for data purposes, this is a superb introductory book.
Naomi Ceder is the chair of the Python Software Foundation and this book is extensive but easy to grasp for beginners and novices alike. The introductory sections include information on syntax, control flow, and data structure before turning to code management, object-oriented programming and web development.
An excellent feature of this book is workable exercises that readers can follow whilst they read.
Data Genres:
- Python
- Coding
- Data modelling
- Data analysis and engineering
Suitable For:
- Anyone learning or using Python
A Practitioner’s Guide to Business Analytics: Using Data Analysis Tools to Improve Your Organization’s Decision Making and Strategy – Randy Bartlett
Another book that is well-targeted at data analysis and science specifically. This book delves into the principles of business analytics, intelligence and how they’ve grown with data techniques. It offers a suite of theoretical and practical advice on how to develop analysis techniques for business.
Targeted specifically at businesses and data practitioners, this book helps new and established professionals stay contemporary in their approach to digital strategy. It’s designed to enhance the use of data in specific corporate contexts.
Data Genres:
- Data analysis
- Digital strategy
- Data engineering
- Business intelligence
Suitable For:
- Those who desire to integrate business analytics into their current business strategy (or client strategy)
Developing Analytic Talent: Becoming a Data Scientist – Vincent Granville
As the title suggests, this book is naturally targeted towards data scientists. It’s designed to provide a detailed analytical understanding that is fundamentally important in the field. Data science is broken down in all its nuanced glory alongside the skills and insights that professionals must learn to grasp its most important concepts.
This book even contains job interview information and resume samples to help prospective professionals learn about the field and its expectations. There are also ample case studies into how data is used in professional spheres, including on Wall Street, in digital marketing and advertising, etc. A pragmatic book with plenty of practical advice for budding data scientists and analysts.
Data Genres:
- Data analysis
- Data science
- Digital strategy
- Data engineering
Suitable For:
- Those wishing to consolidate their knowledge of data science
Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions – Matt Taddy
Another book focussed on business-centric data science, ex-Professor of Econometrics and Statistics at the University of Chicago Booth School of Business Matt Taddy developed this book to assist the implementation of top-class business data science strategy.
This is a superb professionally-oriented book that is sure to benefit seasoned data pros as well as prospective beginners and novices. This book also delves into ML, but ties concepts together with economic rigour so as to not lose sight of how data should drive value for businesses.
It provides a solid overview of machine learning for business, highlighting real-world approaches to solving modern business problems.
Data Genres:
- Data science
- Data analysis
- Digital strategy
- Machine learning and AI
Suitable For:
- Anyone looking for a business-centric approach to using data to solve real-world problems
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking – Foster Provost and Tom Fawcett
The renowned data aficionados Foster Provost and Tom Fawcett created this superb work on data mining and data analysis. The chapters and content are based on the MBA course Provost taught at New York University.
It follows plenty of real-world examples and is extremely well-grounded to the realities of the field. This is a pragmatic book that avoids theory-bloat, instead focussing on real-world problems and problem solving with data. It’s one of the data science bestsellers on Amazon and is highly recommended to anyone with an existing or prospective career in data science and/or analysis.
Data Genres:
- Data engineering and mining
- Data analysis
- Digital strategy
- Business intelligence
Suitable For:
- Those who wish to improve their business-centric data science knowledge
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again – Eric Topol
AI and ML have risen to prominence in the healthcare industry – we’ve written an article about the uses and benefits of machine learning in healthcare here.
This book explores this in great detail. AI is transforming healthcare, changing everything from drug development and treatment to operation automation, diagnostics and public health. A major component of this is automation and the ability for AI to work with colossal open-access datasets.
A top-quality book for anyone interested in furthering their knowledge of AI-led healthcare tasks and methods.
Data Genres:
- AI
- Machine learning
- Data futurism and philosophy
- Data analysis and engineering
Suitable For:
- Those who want to further their understanding into healthcare-specific AI and ML
The Model Thinker: What You Need to Know to Make Data Work for You – Scott E. Page
An extensive investigation into theory and computational models, this is an excellent work for those wanting to flex their knowledge of data model theory. It covers everything from linear regression to random walks.
This book really goes under the skin of why data should be used, and who can take advantage of it, which Page argues is everyone from businesspeople to scientists, salespeople, SEOs, pollsters and much more.
The end result is a well-grounded but theory-packed guide to using data across any number of modern contexts. It’s a conceptual toolkit that marries model theory and practical guidance in perfect harmony.
Data Genres:
- AI
- Machine learning
- Data modelling
- Data analysis and engineering
Suitable For:
- Those who want to further their use of data in business or personal contexts
Rebooting AI: Building Artificial Intelligence We Can Trust – Gary Marcus and Ernest Davis
Two highly esteemed individuals in data science and AI, Gary Marcus and Ernest Davies, authored this compelling take on the field of modern data science. This book explores the all-pervasive potential of AI, asking how we can build AI that we can trust, both on an individual, cultural and societal level.
This is a solid philosophical introduction to the field of AI that contextualises its key modern arguments and debates. It’s suitable for anyone inside or outside of the industry.
Data Genres:
- AI
- Machine learning
- Data futurism and philosophy
- Data analysis and engineering
Suitable For:
- Anyone seeking a greater knowledge of AI for both now and future
Summary
A by no means an exhaustive list of top data analysis books and data science books. There are always new titles being added to a burgeoning quantity of literature on these data-related subjects.
There’s plenty of topics covered here, both in a technical and more abstractive sense. Of course, as techniques develop constantly, readers will have to bear in mind how this is one of the fastest-changing fields in the world.
Always pay attention to the latest developments in the field and stay up-to-date with the latest and great developments in the fields!
FAQ
What Books Should I Read for a Career in Data?
It’s probably best to start off in the realms of statistics and probability. Some solid introductory books include
- Head First Statistics: A Brain-Friendly Guide
- Practical Statistics for Data Scientists
- Introduction to Probability
- Introduction to Machine Learning with Python: A Guide for Data Scientists
- Python Machine Learning By Example
- Python for Data Analysis
What are the Best Books on Data Science?
New titles are being added all the time, it’s important to remember that data science is a modern discipline. There are many superb Amazon bestsellers out there. Check out our guide to the best books on data science and data analysis here for some books you can start reading today!
How Can I Learn About Data Analysis?
Consider first learning about the statistical fundamentals behind data. Consider learning Python. Some excellent courses on data science include:
- Learn Python and Learn SQ, Codecademy.
- Introduction to Data Science Using Python, Udemy.
- Linear Algebra for Beginners: Open Doors to Great Careers, Skillshare.
- Introduction to Machine Learning for Data Science, Udemy.
- Machine Learning, Coursera.
- Data Science Path, Codecademy.
