20 Best Books on Data Analytics

“Analytics: Business Intelligence, Algorithms and Statistical Analysis (Predictive Analytics, Data Visualization, Data Analytics, Business Analytics, Decision Analysis, Big Data, Statistical Analysis)” Book Review: This book provides a comprehensive examination of analysis and its related elements, including business intelligence, algorithms, and statistical analysis. It begins by discussing the challenges and risks associated with analytics before delving into the different types of analytics, such as predictive, descriptive, and prescriptive analytics. The book also covers integrating business and analytics, the benefits of business intelligence, data mining, data integration, and data visualization. It is an essential read for self-employed entrepreneurs, data analysts, and developers.

“Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance” Book Review: his book provides practical guidance on how to leverage big data for achieving real-world business outcomes. It offers a step-by-step approach to building a customized big data strategy, emphasizing the importance of defining the most critical business questions, collecting relevant data, and measuring the right metrics. The inclusion of real-world case studies helps readers understand the concept of big data in a practical manner. This book is particularly valuable for data analysts, researchers, and engineers who work with big data and related subjects.

“Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data” Book Review: This book provides an in-depth understanding of big data technologies and what it takes to succeed in this field. It offers real-world examples of successful and failed data practices from startups, online firms, and large companies. The book also covers visualizing and presenting data in a more effective manner, as well as the discovery and analysis of data science and big data analytics. It is an essential resource for students, researchers, and data analysts seeking to expand their knowledge of big data.

“Tableau Your Data: Fast and Easy Visual Analysis with Tableau Software” Book Review: This book is a comprehensive guide on effectively utilizing Tableau Software toolset for data visualization. It covers both core and advanced features of data analytics and provides various techniques for creating and sharing impactful visualizations that support timely business decision-making. The real-world case studies used to illustrate these features offer practical insights into the subject matter. This book is a valuable resource for software developers, engineers, students, and researchers seeking to enhance their data visualization skills using Tableau.

“Big Data Analytics” Book Review: This book provides a thorough introduction to Big Data Analytics and its important aspect, data mining and modeling. It further covers distributed high dimensional data clustering and machine learning, along with the application perspective of big data mining. The book concludes by discussing the practical usage of big data analytics in the modern business world. It is an essential resource for software developers, engineers, students, and researchers seeking to expand their knowledge of big data analytics.

“Python for Data Science for Dummies” Book Review: This book provides a comprehensive guide on using Python programming for acquiring, organizing, processing, and analyzing large amounts of data. It emphasizes the use of basic statistical concepts to identify trends and patterns and covers Python development, data manipulation, and designing compelling visualizations. By the end of the book, readers will be equipped to solve scientific computing challenges. The book also delves into the role of objects, functions, modules, and libraries in data analysis. It is a valuable resource for anyone interested in learning about data analysis using Python.

“Learning Predictive Analytics with R” Book Review: This beginner’s guide to data analytics covers various aspects of Big Data, including the features of unstructured data mining and practical applications. The book starts with an introduction to the subject and explores data mining methods and models, followed by discussions on clustering, incremental learning, multi-label association, and knowledge representation. The book concludes with a discussion of areas for further research. It is intended for senior-level undergraduate and postgraduate students in computer science and engineering.

“Data Analytics : The Complete Beginner’s Guide – Step By Step Instructions” Book Review: This comprehensive guide to data analytics is designed for beginners and includes real-world examples at the beginning of each chapter. The book focuses on techniques that add value in business environments and provides a blend of theoretical and practical knowledge of underlying data analytics principles. It is intended for senior-level undergraduate and postgraduate students of computer science and engineering.

“Data Analytics : A Quick-start Beginner’s Guide” Book Review: This beginner’s guide to data analytics covers unstructured data mining and the practical aspects of Big Data. The book introduces data mining methods and models, and their applications, and delves into other aspects of Big Data analytics such as clustering, incremental learning, multi-label association, and knowledge representation. Each chapter begins with real-world examples to provide readers with an application-based understanding. The book concludes with a discussion of areas where research can be explored. It’s designed for senior-level undergraduate and postgraduate students in computer science and engineering.

“Getting Started with Greenplum for Big Data Analytics” Book Review: This beginner-friendly book offers an introduction to Greenplum by leveraging the use of Data Warehousing and Business Intelligence platforms. It covers Big Data Analytics, database design, and programming, as well as analytics tools such as R and Weka. The book provides a blend of technology and domain expertise to explain the fundamental aspects of Greenplum in an application-oriented manner. It is an ideal resource for data scientists and data analysts seeking to enhance their knowledge of Greenplum.

“Data Analytics” Book Review: This book explains the basic principles of data analytics without requiring any expertise in software tools. It covers topics such as business intelligence, data mining, data processing chains, data warehousing, artificial neural networks, statistical regression modeling techniques, and cluster analysis. The book includes solved examples throughout the chapters and review questions at the end of each chapter. It is suitable for those interested in data-based decision-making for their business and does not include any programming code. This book is mainly designed for a one-semester graduate course,

“DAX: Your Definitive Guide To LEarn And Write Dax (DAX, Big Data, Data Analytics, Business Intelligence)” Book Review: This book provides a comprehensive understanding of DAX with chapters covering basic measures and calculations. It includes topics such as DAX syntax, data types, date and time functions, and various DAX functions such as filter, logical, and text functions. Each chapter also includes DAX exercises and puzzles for practice and innovative learning. Additionally, the book provides tips and troubleshooting techniques. It is a suitable resource for anyone looking to learn DAX.

“Graphesis – Visual Forms of Knowledge Production (metaLABprojects)” Book Review: This book focuses on the analysis of graphical knowledge and how visual formats organize meaningful content. It provides a detailed explanation of the graphical user interface (GUI) format commonly used in most electronic devices. The book explores how visual forms shape knowledge, behavior, and identity. It presents case studies on how graphic languages can serve fields where qualitative judgments take priority over quantitative statements of fact. The book is useful for anyone interested in understanding the principles by which visual formats organize meaningful content.

“Integrierte Business-Informationssysteme: ERP, SCM, CRM, BI, Big Data Analytics – Process Simulation, Rollenspiel, Serious Gaming” Book Review: This book explores the recent advancements in big data techniques and in-memory computing, and how they have transformed the competitive landscape of organizations. It focuses on the adoption of standardized software systems for implementing Supply Chain Management (SCM), Big Data Analytics (BDA) and Enterprise Resource Planning (ERP). The book emphasizes the importance of management-oriented thinking and decision-making to comprehend these topics. To aid comprehension, the book adopts a business perspective, simulating a virtual company for readers to take on the role of executive board members. This approach is particularly useful for students.

“Serious Games Analytics (Advances in Game-Based Learning)” Book Review: This book presents research on the use of gameplay data for performance assessment, measurement, and improvement. It draws upon experimental and conceptual frameworks from computer science, software engineering, and educational data mining statistics. Serious games are explored as a developing field that uses learning theories and instructional design principles to enhance training success. The book also covers how stakeholders can obtain information on what play-learners have done in the game environment. It is a valuable resource for those interested in the intersection of gaming and learning.

“Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data” Book Review: This book presents 20 revised papers from the Third Workshop on Human-Computer Interaction and Knowledge Discovery, held in Maribor, Slovenia in July 2013. The papers were selected from 68 submissions, and cover a range of topics including human-computer interaction and knowledge discovery, knowledge discovery and smart homes, smart learning environments, and visualization data analytics.

“Computer Assisted Assessment — Research into E-Assessment: International Conference” Book Review: The data presented in this book are from the International Conference on Computer Assisted Assessment, which took place in Zeist, The Netherlands in June/July 2014. The book includes 16 reviewed and revised full papers selected from many other submissions, discussing various topics like large-scale testing facilities in higher education, formative assessment for 21st century skills, and future trends for technology-enhanced assessment. It also covers the latest technological advancements and practical experiences related to the field.

“Bayes Theorem : A Quick-Start Beginner’s Guide” Book Review: This book covers the broad range of applications of Bayes’ theorem beyond just finance. Bayes’ theorem calculates the probability of an event based on related information. It can be used to analyze how new information impacts the probability of an event occurring. The book is geared towards beginners and includes numerous solved examples and exercises for practice.

“Data Mining and Learning Analytics: Applications in Educational Research (Wiley Series on Methods and Applications in Data Mining)” Book Review: This book explores the impact of data mining on education, including applications in educational research, teaching, and learning. It addresses challenges and offers solutions for using data mining (DM) within educational frameworks, with a focus on the guiding principles of prediction, clustering, rule association, and outlier detection. The book includes case studies from various disciplines, such as Business, Humanities, Health Sciences, and Physical Sciences, demonstrating the success of DM in education. Written by experts in the field, it includes supplementary resources such as a primer on foundational aspects of educational mining and learning analytics. Suitable for both EDM scientists and teachers seeking to improve education and advance educational research.

“Data Analytics Applications in Education” Book Review: The book explains how big data has led to the rise of data analytics, which allows organizations to make better decisions. Data analytics can be used to improve student learning by giving real-time feedback to teachers. The book emphasizes that data analytics is not just for one field, but is useful for philosophers, computer scientists, and sociologists. The book provides solutions to challenges in the field and shows how data analytics can be a valuable tool in education.