Best Data Mining Courses & Best Data Mining Books 2024

Table of Contents

Best Data Mining Courses 2022

Data Mining Specialization

Data Mining

The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.

Data Engineer

Learn More on Udacity.com

Data Engineering is the foundation for the new world of Big Data. Enroll now to build production-ready data infrastructure, an essential skill for advancing your data career. Learn to design data models, build data warehouses and data lakes, automate data pipelines, and work with massive datasets. At the end of the program, you’ll combine your new skills by completing a capstone project.

Data Mining with R: Go from Beginner to Advanced!

Learn More on Udemy.com

This is a “hands-on” business analytics, or data analytics course teaching how to use the popular, no-cost R software to perform dozens of data mining tasks using real data and data mining cases. It teaches critical data analysis, data mining, and predictive analytics skills, including data exploration, data visualization, and data mining skills using one of the most popular business analytics software suites used in industry and government today.

The course is structured as a series of dozens of demonstrations of how to perform classification and predictive data mining tasks, including building classification trees, building and training decision trees, using random forests, linear modeling, regression, generalized linear modeling, logistic regression, and many different cluster analysis techniques. The course also trains and instructs on “best practices” for using R software, teaching and demonstrating how to install R software and RStudio, the characteristics of the basic data types and structures in R, as well as how to input data into an R session from the keyboard, from user prompts, or by importing files stored on a computer’s hard drive.

All software, slides, data, and R scripts that are performed in the dozens of case-based demonstration video lessons are included in the course materials so students can “take them home” and apply them to their own unique data analysis and mining cases. There are also “hands-on” exercises to perform in each course section to reinforce the learning process. The target audience for the course includes undergraduate and graduate students seeking to acquire employable data analytics skills, as well as practicing predictive analytics professionals seeking to expand their repertoire of data analysis and data mining knowledge and capabilities.

Best Data Mining Books 2022

Data Mining: Concepts and Techniques

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Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems)

Hardcover Book
Han, Jiawei (Author)
English (Publication Language)

Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining.

This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.

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: Practical Machine Learning Tools and Techniques

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Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data...

NEW
Witten, Ian H. (Author)
English (Publication Language)

Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, 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.

Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html.

It contains

Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book

Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More

Russell, Matthew (Author)
English (Publication Language)
426 Pages - 01/14/2019 (Publication Date) - O'Reilly Media (Publisher)

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers.

In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter.

Get a straightforward synopsis of the social web landscape
Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook
Adapt and contribute to the code’s open source GitHub repository
Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect
Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition
Build beautiful data visualizations with Python and JavaScript toolkits

Bestseller No. 1