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Top 13 Most Popular Data Analytics Books

The ever-growing field of Data Analytics is vast with huge scope and potential in various domains. If you are one of those who love numbers, are passionate about the world of data, and have a good analytical capacity, irrespective of whether you are from a technical or non-technical background, Data Analytics is a very lucrative career option that you could pursue. On one hand, there is an ever-growing demand for analytical skills and there is a huge deficit on the supply side globally. To launch yourself in this field, it would be wise to supplement your learning with Data Analytics Books, a wide range of which is available by accomplished authors with domain experience.

List of best data analytics books

In this article, we have curated a list of thirteen popular Data Analytics Books that will cultivate your understanding of this field and help you tap its potential to your advantage.

Here is a guide to Data Analytics and Data Science

Top 13 Data Analytics Books for Budding Analysts and Professionals:

Depending on your level of proficiency – beginner, intermediate, or advanced, or your specific area of interest – be it a particular data analytics tool you want to enhance your knowledge or skill set on, you can make your choice from among the wide variety of Data analytics Books available.

Take a digital detox, get hold of one of the books mentioned below, and be on the path to expanding your knowledge and becoming more competent.

 

1. Data Analytics Made Accessible – 2023 Edition

Author: Anil Maheshwari

The original version of the book “Data Analytics” was published in 2017 and subsequent revisions have been made to the same. The 2023 revised edition deals with the growing field of Data Science and has a wealth of information in a concise, easy-to-follow, and digestible format.

It has concrete real-world examples and stories at the beginning of every chapter as a result of which readers at any skill level will be able to understand and follow. It is highly intuitive and also provides a solid toolset of the major data mining techniques and platforms.

It appeals to students across various academic disciplines as well as professionals in various domains who are interested in gleaning actionable insights from data that comes their way. One can finish Data Analytics books in one sitting or can return to them time and again as a reference for insights and techniques.

This constantly evolving book has been so well received internationally and has been adopted by dozens of universities as required reading for their analytics courses, earning it the reputation as the Number 1 read for Data Analysts.

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2. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 

 Authors: Foster Provost and Tom Fawcett

Written by celebrated data science specialists, this book deals with the fundamental principles of data science while acquainting readers with data analytic thinking in order to mine useful knowledge and gain business value from the data collected. It gives the reader an understanding of the various data mining techniques and how they can support business decision-making to gain a competitive advantage.

The Data Analytics Books takes on a high-level approach in explaining several complex methods (support vector machines, logical regression, liner classifiers, tree approaches, etc) in simple terms.

 

By providing real-world examples the reader is guided on how to enhance communication with stakeholders, how to treat data as a business asset that requires careful investment, and how to approach business problems analytically in order to gain real value. It also teaches how data science principles can be applied when interviewing data science job candidates.

You should know the most important Data Analyst Interview Questions

3. Developing Analytic Talent: Becoming a Data Scientist

Author: Vincent Granville

This book is a one-of-a-kind guide that provides an understanding of the intricate aspects of data science skills such as statistics and data visualization techniques and how to acquire them.

It also talks about what companies look for, how the growing reliance on big data has strengthened the demand for data professionals, the skill set that distinguishes a quality data scientist from other talented IT professionals and features tips and tricks to ace job interview questions, provides sample resumes, salary surveys, examples of job ads and is replete with case studies.

This book appeals to both those who aspire to be data analysts or scientists and employers who are looking for ideal candidates.

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4. Storytelling With Data: A Data Visualization Guide for Business Professionals

Author : Cole Nussbaumer Knaflic

This has earned the reputation as one of the best Data Visualisation books. It teaches the fundamentals of data visualization and how one can effectively communicate with data by going beyond conventional tools to tell an engaging, informative, and compelling story.

 

This Book provides a sound theoretical foundation, combined with numerous real-world examples, helping the learner to recognize context, choose the right visualization for the right situation, remove clutter, and project the most important parts of the data.

It enables one to think like a visual designer and tell an impactful story by building presentations using multiple visuals. It leverages the power of storytelling, how to make data an essential point in the story, and how to create engaging, informative, and compelling visual stories that will get the audience’s attention.

 

5. Learning R: A Step-by-Step Function Guide to Data Analysis

Author: Richard Cotton

This is a simple and easy-to-understand Book that guides the learner on how to perform data analysis with the R language and software environment, even with little or no programming experience. The tutorials in this hands-on guide teach how to use the essential R tools to analyze data types and programming concepts.

The second part of the book covers everything from importing data to publishing results. Including a quiz in each chapter and concluding with exercises most of which involve writing R code, it helps to re-enforce learning.

The book covers the use of data types such as vectors, arrays, lists, data frames, and strings, how to execute code conditionally or repeatedly with branches and loops, cleaning data imported from various sources, visualization and summary statistics, statistical models for quantitative judgments and predictions and also how to correct mistakes while writing data analysis code.

 

Looking for the best practical-oriented courses to become a professional data analyst? Check here the top-ranked:

 

6. Business UnIntelligence: Insight & Innovation Beyond Analytics & Big Data

Author: Dr. Barry Devlin

This is a highly advocated and one of the best Books that provides an overview of Business Intelligence, discussing the many traditional approaches to BI, and its flaws as well as offering recommendations for the future. It provides an array of new models that business and IT can use to design support systems for successful organizations in the future.

The author gives one a comprehensive view of the past, present, and future of business intelligence while presenting new conceptual and physical models for transforming information into insights and action. The book explores how information, processes, and people need to be reinvented and modified to deliver value, insights, and innovation essential for modern businesses.

It takes one through the process of discovery to analysis, to decision making to action taking, thus defining a fully cohesive, closed-loop environment. Highly recommended if one needs practical advice on how to advance within the field of business analytics.

 

7. Data Analytics: A Comprehensive Beginner’s Guide to Learn About the Realms of Data Analytics From A-Z 

Author: Benjamin Smith

With this book, the author helps one discover the various realms of Data Analytics. This is also one of the best data analytics books for beginners which pens down the path to learning analytics from A to Z in simple language, without the reader getting overwhelmed with technical jargon or alarming formulas.

It highlights that collating and gathering data is one thing and processing data is an entirely separate issue and cannot be accomplished hastily. The book helps to understand the data that is gathered, the approaches required, and the appropriate techniques for analyzing the same.

The reader will also find simple, yet comprehensive strategies to gain valuable insights from the data generated. It helps uncover the nuances of data and big data, the data structures employed in Data Analytics, the essentials of Machine Learning, cleaning data for analysis, Data visualization tools, analytical and statistical packages in R, and much more. Handy tips and practical exercises are an added bonus.

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8. SQL QuickStart Guide: The Simplified Beginner’s Guide to Managing, Analyzing, and Manipulating Data With SQL 

Author: Walter Shields

This Data Analytics book is considered the best SQL guide which teaches exactly what needs to be known to successfully use SQL programming language to prepare for a data-driven future. SQL continues to remain the most reliable and widely used database management language that forms the backbone of modern data management and interpretation.

The author draws on his considerable knowledge of relational database management to make the topic easy to understand and highly actionable. The book will appeal to beginners with zero experience, those seeking to expand mastery in database management and enhance their career prospects, as well as to Managers, decision-makers, and business owners.

The SQL QuickStart Guide aided by numerous images and examples covers the basic structure of databases and how to successfully navigate them, the most important SQL queries, when to use them, and how to use SQL to retrieve and understand data no matter the scale of a database.

It also teaches one how to sell the new SQL skills learned at the workplace, along with other career-enhancing recommendations. Each book comes with free lifetime access to tons of exclusive online resources to help master SQL. These include workbooks, cheat sheets, and reference guides.

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9. Data Analytics for Absolute Beginners: A Deconstructed Guide to Data Literacy

Author: Oliver Theobald

This deconstructed guide to data analytics gives an introduction to Data, Data visualization, Business Intelligence, Statistical tools, and Machine Learning for effective decision-making. Using the “Lego set” approach, each chapter in the book adds to the blocks of knowledge derived from the previous chapters to build data literacy.

Teaching the vocabulary, tools, and basic algorithms, helps one think like a Data Scientist and aids in confidently analyzing data problems. This exploratory book is a hands-on approach to learning and tackling real data problems with practical examples, visual examples, coding exercises in Python, and also free video content to help with the exercises.

 

How to recognize common data types, where to store data, new trends in data analytics, the distinction between data analytic terms like Data Mining, Machine Learning, and Analytics, and how to make of use statistical tools, Data visualization, and Business Intelligence to arrive at better business decisions are all dealt with in the book.

It appeals to anyone interested in making sense of data analytics, without assuming that one understands data science terminology or advanced math.

10. Numsense! Data Science for the Layman: No Math Added

Authors: Annalyn Ng and Kenneth Soo

If you want to get started with Data Science without Math added, this is the book for you. This book is a recommended reference text in top universities like Stanford and Cambridge. It is written in simple and easy-to-understand language with an introduction to data science and algorithms.

With dedicated chapters for each algorithm, it provides an explanation of how each works accompanied by examples of real-world applications. By sticking to intuitive explanations and visuals, key concepts are grasped better. The concepts covered include A/B Testing, Anomaly Detection, Association Rules, Clustering, Decision Trees and Random Forests, Regression Analysis, Social Network Analysis, and Neural Networks.

Added bonuses are point summaries at the end of each chapter, reference sheets comparing the pros and cons of algorithms, and a glossary list of commonly used terms. The book gives a practical understanding of data science so that one can leverage its strengths to make better business decisions.

 

11. Data Analytics: The Ultimate Beginner’s Guide to Data Analytics

Author: Edward Mize

Multi-time best-selling information technology and mathematics author, Edward Mize has compiled the perfect analytics guide for any beginner. This extremely affordable and one of the best Data Analytics books helps one understand the importance that business intelligence and data analytics play in the real world today and how to use data analytics to improve efficiency, drive growth, and boost sales.

Written in an easy-to-understand style, it introduces the reader to the different types of data analytics and how they can be applied for better quality and more powerful results. By helping to gain mastery of the subject, this book will help one learn to solve business problems quickly and work faster. It helps one leverage the power of data analytics in one’s job, business, or school.

 

12. Getting Started with Data Science: Making Sense of Data with Analytics 

Author: Murtaza Haider

This is one of the best data analytics books that introduces the realm of data analytics by adopting the storytelling or narration methodology. The author offers a very informative, jargon-free coverage of basic theory and techniques for drawing inferences from underlying data.

This, also one of the in-demand Data Analytics books is supported with numerous clear, simple examples and practice opportunities. It speaks of the vital ingredients for practical success with data analytics, gives a clear direction on how to conduct formal research, creates powerful visual narratives to describe inferences, and translates them into actions.

Everything in the book is software and platform-independent and the author through his storytelling method teaches a fundamental skillset that most data science books ignore – that the ability to tell a captivating narrative to supplement the data analysis and visualization is as important as the analytical skill requirement for any Data Scientist.

The beauty of the book is that every chapter is built around real research challenges which makes learning more meaningful.

 

13. The Quick Python Book

Author: Naomi Ceder

This is one of the popular Books providing a comprehensive guide to the Python Language. The author offers a clear, crisp updated introduction to the elegant Python programming language in very concise terms and brilliantly balances details of Python with insights and advice needed to handle tasks.

It is written for programmers new to Python and is filled with a wide array of relevant examples and practical exercises that help master each and every concept on the first go. It is appreciated for its clarity, focus, and attention to detail.

It gives a clear coverage of Python 3, Core libraries, packages, and tools, and this updated third version includes five new data science-related chapters. A must-read not only for those wanting an in-depth understanding of the Python language but also a good reference for those who already know the language.

 

FAQs:

Below are the FAQs about data analytics books.

1. Do I need Mathematics to understand Data Analytics?

Ans: A foundational knowledge of Math and Statistics will be helpful. However, since much of Data analysis is just following a set of logical steps, people can succeed in this field without advanced mathematical knowledge. Of course, if you are interested in developing new methods, approaches, and algorithms, then it requires that you be an expert in both maths and statistics. However, most data scientists aren’t creating algorithms or new methods, they are just using them. They are the “applied” type and you can be one of them.

2. Can I become a Data Analytics expert by learning only from data analytics books?

Ans: Practical experience is essential to develop the skills needed to make a good data scientist. There are no theoretically correct answers to solving data-related issues and a good data scientist should develop practical intuition to solve these issues. Data Analytics Books provide theoretical explanations and help to assist and supplement learning, but according to experts, they cannot replace your own empirical experiences.

3. Can data analysts work remotely?

Ans: The job is well suited to working remotely. Much of the day for a data analyst is spent on the computer and the actual analysis part of the job does not require much collaboration, very often it’s none at all. For the most part, you will be working independently once you have worked with the stakeholders to define the problem. However, be ready to convince employers that you’ve got what it takes to work remotely as a data analyst.

4. What are the basic skills required for data analysts?

Ans: Some of the basic technical and soft skills required to become a data analyst include:

  1. SQL
  2. Microsoft Excel
  3. Statistical programming languages like R, Python, Matlab
  4. Data Visualization
  5. Machine Learning
  6. Probability and Statistics
  7. Critical Thinking
  8. Communication
  9. Problem-Solving
  10. Intellectual curiosity
  11. Strong Business Acumen

5. What do I look for in Data Analytics Books?

Ans: Good data analytics Books should not only deal with theoretical concepts in a clear and concise manner but also provide real-world examples, case studies, practical exercises, and review questions to check comprehension. Since the field is rapidly changing with technological advancements, worthwhile Books should be updated with current information- newer versions should be made available as and when changes occur, or else some of the concepts/methods could prove redundant.

In Conclusion:

With swift technological advancements in the field of data science, it is imperative that professionals are quick to adapt to the changing environment. Keeping pace with this rapidly evolving technology sector can prove to be an enormous challenge. Apart from undertaking refresher courses from time to time, another way to get acquainted and keep abreast of changes in the industry is to delve into Data Analytics Books and publications by top experts. These will encourage you to be a lifelong learner with tremendous potential to excel in the field.

There is no dearth of excellent Books available. We’re sure the books listed above will provide the much-needed impetus for you to venture into the world of data more confidently. So, go ahead and make use of them to enhance your learning curve, increase your expertise, accelerate your career, transform your business and stay relevant in this digital and data-driven age.

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