Are you looking for the best books on Data Mining?… If yes, then this article is for you. In this article, you will find the Best Data Mining Books for Beginners & advanced like Beginner courses, and Practice test courses. So, check these Best Data Mining Books for Beginners and find the Best Data Mining Books for Beginners to Advanced according to your need.
In the previous article, I shared the Best Web Design Books for Beginners in 2022, you can go through the list and enjoy reading.
Best Data Mining Books for Beginners to Advanced to know in 2022
Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)
Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data.
Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today’s most powerful data mining techniques to meet real business challenges.
- Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects.
- Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.
- Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data
Data Mining for Business Analytics: Concepts, Techniques and Applications in Python
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration.
You will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities.
It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:
- A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process
- A new section on ethical issues in data mining
- Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
- More than a dozen case studies demonstrating applications for the data mining techniques described
- End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
- A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions
Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)
Data Mining: Practical Machine Learning Tools and Techniques, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning.
Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today’s techniques coupled with the methods at the leading edge of contemporary research.
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
- Understand how data science fits in your organization—and how you can use it for competitive advantage
- Treat data as a business asset that requires careful investment if you’re to gain real value
- Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
- Learn general concepts for actually extracting knowledge from data
- Apply data science principles when interviewing data science job candidates.
Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods
Excel allows you to work with data in a transparent manner. When you open an Excel file, data is visible immediately and you can work with it directly. Intermediate results can be examined while you are conducting your mining task, offering a deeper understanding of how data is manipulated and results are obtained. These are critical aspects of the model construction process that are hidden in software tools and programming language packages.
This book teaches you data mining through Excel. You will learn how Excel has an advantage in data mining when the data sets are not too large. It can give you a visual representation of data mining, building confidence in your results. You will go through every step manually, which offers not only an active learning experience, but teaches you how the mining process works and how to find the internal hidden patterns inside the data.
- Comprehend data mining using a visual step-by-step approach
- Build on a theoretical introduction of a data mining method, followed by an Excel implementation
- Unveil the mystery behind machine learning algorithms, making a complex topic accessible to everyone
- Become skilled in creative uses of Excel formulas and functions
- Obtain hands-on experience with data mining and Excel
Data Mining: The Textbook
This textbook explores the different aspects of data mining from the fundamentals to the complex data types and their applications, capturing the wide diversity of problem domains for data mining issues. It goes beyond the traditional focus on data mining problems to introduce advanced data types such as text, time series, discrete sequences, spatial data, graph data, and social networks.
Until now, no single book has addressed all these topics in a comprehensive and integrated way. The chapters of this book fall into one of three categories:
- Fundamental chapters: Data mining has four main problems, which correspond to clustering, classification, association pattern mining, and outlier analysis. These chapters comprehensively discuss a wide variety of methods for these problems.
- Domain chapters: These chapters discuss the specific methods used for different domains of data such as text data, time-series data, sequence data, graph data, and spatial data.
Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor.
Appropriate for both introductory and advanced data mining courses, Data Mining: The Textbook balances mathematical details and intuition. It contains the necessary mathematical details for professors and researchers, but it is presented in a simple and intuitive style to improve accessibility for students and industrial practitioners (including those with a limited mathematical background). Numerous illustrations, examples, and exercises are included, with an emphasis on semantically interpretable examples.
And here the list ends. So, these are the Best Data Mining Books for Beginners to Advanced. I will keep adding more Best Data Mining Books for Beginners to advance to this list.
Conclusion
I hope these Best Data Mining Books for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.