Data analytics

Data Analytics continues to be a sought-after discipline. This second edition of the popular textbook retains the concise and conversational style and extends the discussion in the first edition by including additional topics such as Data Privacy, Artificial Intelligence, and Data Wrangling. With insightful caselets in every chapter, review questions, objective-type questions, and a running case exercise, this book is designed to provide students with application-oriented knowledge of the subject.<br /><br />In addition, this book provides a solid toolset of major data mining platforms, with easy tutorials on data mining techniques using Python, R, and Weka. This edition also includes a sample data analytics student project, that uses all the data mining techniques to solve a real socially relevant problem. <br /><br />This book is designed to meet the requirements of students across a variety of academic disciplines. While primarily designed to meet the needs of students of Computer Science and Information Technology, this book will also serve the needs of students from other engineering disciplines as well as management stream. <br /><br />For the busy reader, this edition also contains main points from the book listed as a summary chapter encapsulating knowledge in just 50 main points.<br /><br /><br /><b>Key Features</b><br />• Application-oriented approach to Data Analytics<br />• Coverage of topics such as Data Mining, Data Wrangling, and Artificial Intelligence<br />• Integrated caselets<br />• Running case exercise <br />• Tutorials on Data Analytics using Weka, R, and Python