From bestsellers to must-reads, books embody the benefits of reading: they can provide comfort, knowledge, challenge, and inspiration. Consider again what famous author Margaret Fuller once said about her reading. She says, “Today I'm a reader, tomorrow I'm a leader.'' In any field, good reading can set you on the path to a successful journey to your desired destination and beyond.
The field of data analytics is currently growing so rapidly that the U.S. Bureau of Labor Statistics predicts that employment for data analysts will increase by more than 23% between 2020 and 2030. Nevertheless, replenishment of data professional talent could lead to this level of rapid employment growth. Best resource.
Skip through the must-read data analyst books for beginners and experienced professionals, one of the best resources for success in the data field.
Top Data Analysis Books of 2024
Books for data analysts are a great way to learn about subject matter, development, and useful skills for professionals who want to work in data analysis.
We have collected recommended books on data analysis, from basics to specialized subjects, such as big data, AI, and statistical programming languages.
Storytelling with Data: A Data Visualization Guide for Business Professionals – Cole Nussbaum Knaflic, 2015
Cole Nussbaummer Knaflic, CEO and founder of Storytting With Data, wrote this remarkable data analyst book.
SWD is a book that emphasizes the importance of data storytelling in data analysis. Rather than simply placing graphs on a report page, data analysts must carefully choose the right graphs and create a compelling story that engages the audience.
This work is one of the must-read data analysis books for beginners and provides six steps to help you with data storytelling.
Big Data: A revolution that will change the way we live, work and think – Victor Mayer-Schönberger, 2013
Field experts Viktor Mayer and Schönberger discuss the impact of big data on our world. Their book also focuses on potential positive or negative changes in big data.
This book will give you a good understanding of data analytics and its impact on various industries. Readers can prepare for the coming big data revolution. This book delves into the far-reaching impact of big data on social aspects. It highlights the potential risks associated with digital technologies. The book also provides a theoretical overview of the importance of big data in different life stages.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython – Wes McKinney, 2011
The authors of the Pandas library's comprehensive book Python for Data Analysis teach students the fundamentals of using Python to manipulate, manipulate, clean, and process data. Real-world case studies are featured, along with an introduction to data science tools and instructions on how to build useful visualizations using Matplotlib. Other techniques include loading, cleaning, manipulating, combining, and reshaping data.
Naked Statistics: Taking the Fear Out of Data – Charles Wheelan, 2012
The field of statistics is rapidly evolving into a “sexy” discipline, with applications in fields as diverse as politics, game shows, and medical research. Charles Wheelan's book Naked Statistics focuses on the intuition behind statistical analysis and explains important concepts such as inference, correlation, and regression analysis. The book also highlights how data can be manipulated by biased parties and how creative researchers use natural experiment data to tackle complex problems. A valuable resource for those who missed Stats 101.
Data Science for Business: What you need to know about data mining and data analytical thinking – Tom Fawcett, 2013
Written by Foster Provost and Tom Fawcett, this book introduces the basic concepts of data science and data analytical thinking. This data analysis book allows readers to extract valuable knowledge and business value from their data. Educate readers on how to use data science techniques to support business decisions and how to think analytically about data.
Business UnIntelligence: Insights and Innovation Beyond Analytics and Big Data – Barry Devlin, 2013
This book examines the past, present, and future of business intelligence, highlighting the strengths and weaknesses of traditional methods. Dr. Devlin will discuss how big his data and analytics are revolutionizing business intelligence today, highlighting proven techniques to improve how people, processes, and information interact and compete. Provides insight into what creates superiority and drives enterprise success. Furthermore, we propose new frameworks and models for companies to strengthen their future.
100 Page Machine Learning Book – Andriy Burkov, 2019
This book provides a concise introduction to machine learning in just 140 pages, making it suitable for readers with no prior knowledge of programming or statistics. Important ideas are covered, including neural networks, cluster analysis, and supervised and unsupervised learning. The book is short enough to be read in one sitting, and the accompanying Wiki provides resources and suggestions for further reading.
Artificial Intelligence: A Guide to Human Thinking – Melanie Mitchell, 2019
Computer scientist Melanie Mitchell wrote this book to explore the historical background and people behind artificial intelligence. This book gives special attention to difficult ideas such as neural networks, computer vision models, and NLP. It helps readers who don't need a full understanding of AI to understand how AI impacts data analysis.
Develop your analytical talent: Become a data scientist – Vincent Granville, 2014
Granville has a background in big data, business analytics, and predictive modeling, and provides useful information in his handbook on data science and data scientists. This book explains the importance of critical information for data scientists in big data organizations. It is divided into three sections covering technical applications, case studies, tutorials, career opportunities, and the relationship between data science and other fields.
Educating decision makers about specialized solutions and their applications also helps develop stronger analytical teams. Granville's over 20 years of industry experience provides a quick recommendation for companies wishing to establish a data science company.
Learning R: A step-by-step function guide for data analysis – Richard Cotton, 2013
This book provides a step-by-step introduction to the R language and is a valuable tool for non-technical learners. Describes environments, loop structures, packages, and data structures. The book then covers data analysis processes such as data loading, cleaning, and transformation. The second section is a valuable resource for those new to programming languages, as it provides further insight into exploratory analysis and modeling.
Weapons of Mathematics Destruction – Cathy O'Neill, 2016
Cathy O'Neill's book on data bias emphasizes the importance of using big data responsibly. We also discuss the consequences of machines making decisions about our lives and how algorithms often reinforce discrimination. Despite the differences of opinion, insights into ensuring that future data is used for the benefit of all people, not just the privileged, are crucial for data science novices.
Data Science and Big Data Analytics: Discover, Analyze, Visualize, and Present Data, 2014
Big data analytics integrates real-time data feeds and queries to provide deeper insights and support your business. This book, published by EMC Education Services, introduces the key techniques and tools of big data analysis and guides readers from basic techniques to advanced techniques such as classification, regression analysis, clustering time series, and text analysis. Masu. Suitable for business analysts, database professionals, and university graduates interested in data his science or data analysis as a career field.
Too Big to Ignore: The Business Case for Big Data – Phil Simon, 2013
Phil Simon's book Too Big to Ignore: The Business Case for Big Data examines the use of big data by businesses and local governments. Featuring case studies and quotes from experts around the world, it provides valuable insights to transform data into intelligence and put it into action.
Elements of Statistical Learning – Trevor Hastie, 2001
This book provides a thorough introduction to statistical thinking in a variety of industries, including marketing, biology, finance, medicine, and more. Use color photography as an example and prioritize concepts over formulas. This book covers classification trees, neural networks, support vector machines, boosting, and other subjects related to supervised and unsupervised learning, making it an invaluable tool for statisticians and data mining players .
Nonsense! Data Science for Laymen: No Mathematics Added – Kenneth Soo, 2017
This book is a comprehensive introduction to data science suitable for non-technical people. Avoid complex calculations and provide clear language and visual explanations of algorithms. It is useful for data scientists and beginners as a refresher to communicate their work to their business partners. The book's explanations of algorithms are useful for field communication.
Head First Data Analysis: A Learner's Guide to Big Numbers, Statistics, and Better Decision Making – Michael Milton, 2009
Head First Data Analysis is a book that teaches you how to manage and analyze different types of data, including product development, marketing, sales, and entrepreneurship. We offer a unique approach to learning how to transform raw data into critical business tools. This book uses the latest research in cognitive science and learning theory to create visually rich formats that respond to how the brain works and provide an efficient way to transform raw data into valuable business tools. is.
SQL Quick Start Guide: A simplified beginner's guide to managing, analyzing, and manipulating data using SQL – Walter Shields, 2015
This book includes a complete introduction to Structured Query Language (SQL), digital resources such as workbooks and reference guides, and sample databases and SQL browser software. Covers subjects such as relational database communications, database structures, critical SQL queries, and marketing your SQL expertise to prospective employers. The book also offers suggestions on how to market her newly acquired SQL abilities to potential employers.
Microsoft Excel Data Analysis and Business Modeling – Wayne L. Winston, 2004
Wayne Winston, a renowned consultant and business professor, has been teaching clients in the corporate sector and MBA students how to use Microsoft Excel for data analysis, modeling, and decision-making for over a decade. This practical guide provides real-world examples and hands-on exercises to enhance your data analysis and modeling expertise. This book is available as a searchable e-book and his CD file for download.
FAQ
1. What are the four pillars of data analysis?
The four pillars of data analysis are descriptive, diagnostic, predictive, and prescriptive. Each pillar strengthens a company's understanding of its data and its ability to drive its goals through insights.
2. Is data analysis a difficult job?
Data analysis tasks can be difficult, especially if you have no programming, statistical, or data processing experience. However, if you work at it consciously, it can be fun.
With dedication, the right resources (data analyst books, courses on good learning platforms), stress management, and a strategic approach, data analysts can have a rewarding and rewarding career.
3. What is the salary of a data analyst in India?
The average annual salary for a Data Analyst in India is INR 6.4 Lakh, based on 971K salary estimates, ranging from INR 1.8 Lakh to INR 12.8 Lakh, with 0 to 6 years of experience.
4. Will AI replace data analysts?
While AI provides useful tools, it can only support the efforts of data analysts. In the future, companies will continue to invest in data analysts who can safely and confidently implement artificial intelligence technologies. Rather than completely replacing data analysts, AI will evolve, enhance them, and make them more efficient.
5. Is there still a demand for data analysts?
Data analysts are in high demand in India, with 97,000 unfilled jobs annually. Due to demand, jobs for data analysts in India have increased by 45%. The US Bureau of Labor Statistics predicts that employment of analyst professionals globally will increase by more than 23% between 2022 and 2032.