We are surrounded by data. As the pace and amount of new data are growing daily, the ability to extract actionable insights from huge datasets is not just a skill now, it’s a mandatory aspect of a business.
As companies are drawing in data and relying more and more on data analysis, those with expertise in making sense of it will thrive and sustain in the market. Data analysis is not restricted to crunching numbers, but it demands that you tell a story, make informed decisions, and bring about real change.
Whether you are a beginner trying to make your place in the world of data or an experienced professional who wants to hone your skills, finding and choosing the right learning resources can make a huge difference.
One such way to enhance your learning and data analytics journey is books, as they help you learn key ideas, concepts, and terms in detail. We have curated a list of top 10 books on data analytics that cover basic and advanced topics and guide you to master this rapidly changing field.
What is Data Analytics?
Data analytics is the process of assessing informational resources or unstructured data to extract meaningful insights, such as behavior and patterns, to make informed decisions. Many organizations use data analytics tools and techniques to solve real-time problems, which shape business processes and improve decision-making capabilities.
Data analytics use technical procedures and algorithms to employ techniques that modify data for human comprehension and use. The process helps businesses maximize their capacity. One example of data analytics is business intelligence.
Why Are Books Important in a Data Analytics Journey?
Before we look through the list of best data analytics books, it’s important to understand why books are a valuable source of knowledge for professionals.
There is no denying that books are reliable and excellent study material when you prepare for any career. While you can easily find online tutorials, boot camps, and courses, which ensure quick accessibility and interactivity, books are known for providing a deeper level of knowledge.
Also, books are written by industry experts who have years of experience in the field. They are seasoned professionals who leverage their expertise and bring the best to the table.
Top 10 Books on Data Analytics
Although you can find ample data analytics books, choosing the one that fits your learning goals can be tricky. Hence, we have listed the most relevant books perfect for beginners and professionals. You will find a detailed overview of each topic and multiple exercises and questions to improve your practical data analysis skills.
- Data Analytics Made Accessible by Dr. Anil Mahehswari
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett
- Business unIntelligence: Insight and Innovation beyond Analytics and Big Data by Dr. Barry Devlin
- Storytelling with Data: A Data Visualization Guide for Business Professionals by Cole Nussbaumer Knaflic
- Big Data: A Revolution That Will Transform How We Live, Work, and Think by Kenneth Cukier and Viktor Mayer-Schönberger
- Artificial Intelligence- A Guide for Thinking Humans By Melanie Mitchell
- The Hundred-Page Machine Learning Book by Andriy Burkov
- Naked Statistics: Stripping the Dread from the Data by Charles Wheelan
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Jupyter by Wes McKinney
- Learning R: A Step-By-Step Function Guide to Data Analysis by Richard Cotton
1. Data Analytics Made Accessible
– Dr. Anil Mahehswari
If you are taking your initial steps as a data analyst, this is the most comprehensive book for you to learn and master everything from the beginning. The language is easy, and the chapters are organized like an introductory college course. It contains an array of real-world examples to enhance your understanding. Hence, many universities are adopting it in their syllabus.
It discusses data analytics tools, applications, and other basic and advanced concepts. The best part is it perfectly combines theory and practical examples, so you can understand the ‘what’ and ‘how’ of data analytics.
Although it was originally published in 2014, it has gone through several updates over the years to include all the essential topics, such as artificial intelligence, big data, data privacy, and data science career guidance.
Topics Covered
- Business Intelligence Concepts & Applications
- Instances of data analysis in action in the real world
- Data analytics lifecycle, starting from data collection to visualization
- Decision trees, regression models, and cluster analysis
- Data warehousing and Data mining
- Data modeling primer
- Case studies to show the impact of data analysis across different industries
- Review inquiries to help you check your knowledge
Where to Buy- Data Analytics Made Accessible
2. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
– Foster Provost and Tom Fawcett
If you want to understand the mechanics of data-driven decision-making, this is an ideal data analytics book to start with. It will help you lay a strong foundation by delving into the importance of data for business decisions.
As a data analyst beginner, this book introduces you to how data science and business work together and how to make data-driven decisions. It helps you connect the dots between data and business applications. You get an insight into the process that companies use to make informed decisions like customer churn rate and expected sales.
The authors have done an excellent job of explaining key data science concepts in easy-to-understand language for non-technical readers without compromising on details. They have outlined principles and techniques of data science without getting too much into the technical details of algorithms.
This book will teach you to think analytically and use data as a secret weapon to sustain in today’s competitive world.
Topics Covered
- Techniques like decision trees, regression analysis, and clustering
- Business problems and data science solution
- How data science fits into business decision-making
- Predictive modeling: from correlation to supervised segmentation
- Visualizing model performance
- Decision analytic thinking
- Real-world case studies involving data analytics to make a measurable impact
Where to Buy- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
3. Business UnIntelligence: Insight and Innovation beyond Analytics and Big Data
– Dr. Barry Devlin
For readers who want an overview of business intelligence, it’s the perfect find. It explores how the trinity of people, information, and processes come together to ensure business success in this modern world.
It discusses various conventional methods of BI, their weaknesses and strengths, and suggestions for the future. Dr. Devlin is the pioneer of data warehousing and has made substantial contributions to BI.
He has not talked about traditional business intelligence concepts but outlines how BI can fall short while sharing new models and frameworks for improved practices. The book guides you to progress in the business intelligence field, giving an overview of the past, present, and future of BI.
Topics Covered
- The genesis of the business-technology ecosystem
- Data, information, and the hegemony of IT
- Why businesses need logical, data-based, and interactive decision-making
- How applications became apps and other process peculiarities
- Practical tips for using big data
- Innovation in the human and social realm
Where to Buy- Business UnIntelligence: Insight and Innovation beyond Analytics and Big Data
4. Storytelling with Data: A Data Visualization Guide for Business Professionals
– Cole Nussbaumer Knaflic
Any data is as good as the story you choose to tell using it. Numbers on a spreadsheet are of no use if you don’t know how to present them meaningfully to extract actionable insights.
It is one of the best data analytics books for beginners, taking you through the fundamentals of communicating with data through visualization and storytelling. It focuses on a vital aspect of data analytics, i.e., communication.
The author explains how to transform data into a compelling story by combining theory with real-world examples. It also talks about how to use data analytics tools more effectively, making it a must-read for professionals who need to present data to stakeholders.
Topics Covered
- Correct visualization for the right situation
- Best practices for creating clean and impactful data visualizations
- Create presentations using multiple visuals and tell an intriguing story
- The psychology behind how people interpret data
- Thinking like a visual designer
- Eliminating clutter and focusing on the most important aspects of data
- Real-world examples of compelling data storytelling in a business context
Where to Buy- Storytelling with Data: A Data Visualization Guide for Business Professionals
5. Big Data: A Revolution That Will Transform How We Live, Work, and Think
– Kenneth Cukier and Viktor Mayer-Schönberger
Anyone who is involved in data analytics must have heard of big data. Here is the finest book to understand why data analytics is important beyond the corporate sense. Authored by two experts in the field, the book highlights how big data is changing our world for the better (and sometimes worse).
As you read the book, you will get to know how data analytics is used across varied businesses and can affect any industry. It will help you to embrace the impending changes in the business and prepare you for the fact that big data is here to stay.
You will not just find technical content related to big data algorithms but also understand what it is, what it can do, and its possible impact on the future. It contains facts related to how big data can reveal insights that can’t be found otherwise.
Topics Covered
- The obsolescence of sampling
- Shift in industry and government to collect data
- The acceptance of increased measurement error in return for more data
- Move away from the age-old search for causality
- Ways to control the “datafication” of human life
Where to Buy- Big Data: A Revolution That Will Transform How We Live, Work, and Think
6. Artificial Intelligence- A Guide for Thinking Humans
– Melanie Mitchell
Written by Melanie Mitchell, a renowned computer scientist, this book explores the history of artificial intelligence, its achievements, aspirations, and growing concerns. It helps you comprehend challenging concepts like natural language processing, neural networks, and machine vision models while assessing the growth of AI and people who were involved in its development throughout history.
The book blurs the line between the hype around AI and real advances in the field. You will come across various pressing issues in the field of AI today, including its functioning. The book will help you understand if you should be concerned about AI overtaking human tasks by sharing the stories behind the science of AI.
Yes, as a data analyst, you may not need a thorough understanding of AI. Still, it is among the best books for data analysis to get a sneak peek into AI technologies and their impact on the data analytics sector.
Topics Covered
- History of AI
- How the different approaches to AI work, including Deep Learning and Machine Learning
- Approaches to game-playing AI
- Neural networks and deep learning
- Common sense and how it remains a significant challenge for AI systems
- Human vs. Machine Intelligence
- AI projects, such as Cyc’
- Ethical and Societal Implications of AI
Where to Buy- Artificial Intelligence- A Guide for Thinking Humans
7. The Hundred-Page Machine Learning Book
– Andriy Burkov
As evident by the name, this book provides a machine learning overview in just over 100 pages. The author has introduced the subject comprehensively, making it an easy and interesting read for learners with no prior knowledge of programming or statistics.
The book may seem short, but it covers ample information, including topics like neural networks, hyperparameter tuning, and cluster analysis. These terms may sound overwhelming and difficult to beginners, but the book explains them in easy language.
You can complete the book in a few days or hours and gain a wealth of knowledge. It contains concise and clear articles on various machine learning concepts explained with mathematical-related topics used in the data analysis field.
Topics Covered
- Notation and Definitions
- Best machine learning algorithms
- Neural networks and deep learning
- Advanced and other forms of learning
Where to Buy- The Hundred-Page Machine Learning Book
8 Naked Statistics: Stripping the Dread from the Data
– Charles Wheelan
Statistics can be intimidating, but Kudos to Charles Wheelan, who explains tricky statistical concepts in an engaging and educational way. So, if you need to brush up on your college statistics or are struggling with arithmetic ideas presented as a list of numbers and symbols but without context, this is among the best books for data analysis to pick.
For anyone who gets stressed seeing numbers and technical jargon, this book will work wonders by making data analytics easier to understand. The author explains key concepts of statistical analysis, such as regression, correlation, and inference, in an interesting way. You will see Wheelan making a point regarding how everyone, not just data experts, needs to understand statistics in this contemporary world.
The book is light on math but heavy on practical applications, so it appeals to learners more than other books. It may not transform you into a statistics master, but it will add value to your statistics education by helping you learn the underlying ideas and why they are important.
Topics Covered
- Descriptive statistics
- Interpreting data accurately and avoiding common errors
- Correlation, basic probability, inference, and regression analysis
- How statistics is used in various aspects of the real world, such as politics, sports, and business
- Core statistical concepts without indulging in tricky mathematical numbers and terms
Where to Buy- Naked Statistics: Stripping the Dread from the Data
9. Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
– Wes McKinney
If you are familiar with Python and object-oriented programming, this book is for you. It skips the theoretical part and emphasizes hands-on knowledge, guiding you to mastering Python for data analysis.
We know that Python is a go-to language for data analysts, and this book explores why. It educates you about Python libraries, such as pandas, NumPy, Scikit-Learn, and more, through Python tutorials. You will learn to use flexible tools to clean, import, transform, combine, and restructure data.
Topics Covered
- Essential Python libraries like NumPy, pandas, and IPython
- Built-in data structures, functions, and files
- How to use Python to manipulate, analyze, and clean data sets
- Data loading, storage, and file formats
- Plotting and visualization
- Data aggregation and group operations
- Practical examples showing how to apply Python in real-world
Where to Buy- Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter
10. Learning R: A Step-By-Step Function Guide to Data Analysis
– Richard Cotton
Here is a perfect book for learning data analytics using R. The author is competent and has written language that is easy to understand, ensuring that you don’t get bored at all.
The first part of this data analysis book focuses on the intricacy of R programming, its data structures, prerequisite tools, and functions. The second part covers data intake and results. You will also explore data analytics methodologies.
Topics Covered
- Write a simple R program and understand what the language can do
- Vectors, Matrices, and Arrays
- Code execution conditionally or repeatedly
- Apply R add-on packages
- How to clean data imported from a variety of sources
- Understand data through visualization and summary statistics
- What to do when things go wrong when you data analysis code
Where to Buy- Learning R: A Step-By-Step Function Guide to Data Analysis
FAQs about Data Analytics Books
Data analytics has become more relevant than ever across most industries. It is an essential skill, considering the importance of analytics. As businesses rely more on data, the demand for skilled data analytics will grow. Here are a few reasons to study data analytics:
– Lucrative job opportunities for data analytics
– High demand for data analytics professionals
– Best chances to work as a freelancer
– Data analytics is used everywhere
The right and informative books on data analytics can provide valuable skills, knowledge, and insights that help you grow in your career and explore more opportunities. These books help you master various techniques, concepts, and tools to improve your career prospects and stay relevant in the industry.
The frequency of publishing new books can vary based on several factors. However, in the case of rapid technological advancements and the increasing importance of data analytics, new books are published more frequently. So, keep an eye on reliable and reputable articles to know the latest releases.
The best data analytics books will meet the following criteria and standards:
– Concise, easy-to-understand, and straightforward language.
– Easy to navigate and suitable for self-taught programmers.
– A range of practical examples, case studies, exercises, problems, instructional practices, illustrations, and other elements – to keep the learners engaged and ensure active learning.
– Practice exercises and practical applications.
– Updated information, detailed explanation of data analytics fundamentals, and complete instructions.
You can find many books on data analytics, especially written for beginners. These books contain clear and easy explanations of each terminology and concept from scratch. Make sure you read the title and check the book description to see if it’s for beginners. You can also search online to find the best book options for a smooth introduction to the subject.
Most books on data analytics include practical examples and case studies to demonstrate various concepts and real-world applications. Using these examples and case studies, learners can understand how data analytics is used across industries.
You can pick the right book on data analytics by considering a few factors:
– Learning style
– Learning goals
– Current level of knowledge
– Specific interests
– Author’s reputation and credibility
– Reviews, recommendations, and feedback
– Table of content
– Scope and depth
– Assess available recourses
– Practice examples and case studies
