Did you know that we send and receive approximately 18.1 million messages per minute globally and watch 4.5 million YouTube videos in a minute? To simply put it, we generate around 2.5 quintillion bytes of data per day!
With the increase in data pile each second, naturally, it creates a demand for a professional who can analyse it and make it understandable. Such a professional is a data analyst who translates figures, statistics, numbers etc., into an understandable form.
For aspiring data analysts, it is crucial to understand the basics of data analysis, and what better way to do so than some reading? Take a digital detox with these data analytics books before beginning your journey. This list covers ten must-read data analysis books, including AI, Python, Big Data, machine learning, etc.
Top 10 Data Analytics Books
Data Analytics Made Accessible, written by Dr Anil Maheshwari
Published back in 2014, the book covers several important topics like artificial intelligence, data privacy, etc., and offers career advice in data science. What makes this book even more interesting is its organisation. The book has an organic structure, just like an introductory course in your college. Apart from the high-level synopsis of the important concepts, this book further covers:
- Case studies which can be carried out in your portfolio
- Real-world examples by putting data analysis to use
- Python and R tutorials made for beginners
- A set of review questions to help learners check their growth
Too Big to Ignore: The Business Case for Big Data, written by Phil Simon
This is one of the classics of Big Data analysis books. The author has curated the content with real-life examples drawn from Big Data applications in the local government and private companies to explain Big Data is crucial. In the content, Phil Simon has explored the surge of Big Data usage in recent times, simplified the content, and made it understandable using case studies. This book is on this list because of its easy readability and crystal clear implementation of Big Data in real-life.
Artificial Intelligence: A Guide for Thinking Humans, written by Melanie Mitchell
The book explores the turbulent history of artificial intelligence, its success and even the fears surrounding its emergence. This book is a must-read for data analysts because Mitchell has raised many urgent questions related to AI throughout the content, which probes the reader to think about whether they should be worried about this discovery. Another reason to dive into this book is the clear differentiation between the hype and real achievements of artificial intelligence while weaving narratives about science and the people behind it.
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Naked Statistics: Stripping the Dread from the Data, written by Charles Wheelan
If you’re looking for a fresh perspective on the statistics you’ve learned so far, this is the book to pick up. Dive into this book if mathematical concepts are something you steer clear of if presented as strings of symbols and numbers. In this book, the author has explained the core statistical concepts like regression, correlation etc., in an entertaining and enlightening style. The author has humorously defined why you should learn statistics not simply because you’re a professional but rather a commoner.
Python for Data Analysis, written by Wes McKinney
This is an ideal book to learn intricate Python concepts if you’re new to Python. The book offers a learning opportunity on how to perform operations on Python datasets, including crunching, manipulation of data, processing, and cleaning. This book also provides knowledge on creating interactive, static visualisations paired with a treasure-trove of Python library.
SQL QuickStart Guide: The Simplified Beginner’s Guide to Managing, Analysing, and Manipulating Data with SQL, written by Walter Shields
This book introduces Structured Query Language or SQL, one of the most common tools for data analysis. This book is in our list of top 10 data analyst books because:
- It gives access to SQL browser applications and sample databases, helping learners put their theory to action.
- Lifelong access to various digital tools, where reference guides and workbooks are only a few among them.
- Teaches the use of SQL for communication with relational databases.
- Provides advice to the learners on the correct way to pitch the newly acquired SQL skills to their employers.
Top Data Science Skills to Learn
|SL. No||Top Data Science Skills to Learn|
|1||Data Analysis Programs||Inferential Statistics Programs|
|2||Hypothesis Testing Programs||Logistic Regression Programs|
|3||Linear Regression Programs||Linear Algebra for Analysis Programs|
Creating Value with Social Media Analytics: Managing, Aligning, and Mining Social Media Text, Networks, Actions, Location, Apps, Hyperlinks, Multimedia, & Search Engine Data, written by Gohar F Khan
This is ideal if you’re searching for data analyst books that will teach you about optimal data usage on social media platforms. The author has explained the theories, strategies, concepts and techniques behind lead generation in social media. The book also gives perspective on how businesses can increase customer loyalty, boost their web page traffic and what to keep in mind before making vital business decisions. The book offers tutorials, tools and case studies that are fruitful for brands and are a must-read for beginners because complex concepts of social media analytics are depicted with simplicity.
Developing Analytic Talent: Becoming a Data Scientist, written by Vincent Granville
This book is a must-read for budding data analysts seeking a perspective on developing detailed analytics to meet business objectives. Granville has explained the core data science aspects and the skills you need to acquire them. Furthermore, this book offers the much-needed questions to crack your job interview, resume samples, instances of job listings and salary surveys.
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, written by Eric Topol
The book surfs through the potential of artificial intelligence to revolutionise the medical world. It describes how AI can empower doctors and physicians by metamorphosing everything they do- from scanning or diagnosing diseases to suggesting treatments and even notetaking. Not only does this book explain how to reduce medical costs, but also about bringing down the mortality rates significantly. Medical learners inclined towards data analysis must pick this book.
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Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, written by Cathy O’Neil
The last book on our list of top ten data analytics books is a book by O’Neil, which opens doors to data’s darker sides. It explains data’s potency and potential to work as an instrument for irresponsible usage. This book warns about the reckless use of data describing the outcomes of machine-made decisions and nudges the reader’s mind about the power of algorithms to reinforce discrimination. Although every reader might not be on the same page as the author, this book is a must-read for awareness, restricting its use to ensure benefits through responsible use.
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