9781284180909-1284180905-Introduction to Data Mining and Analytics

Introduction to Data Mining and Analytics

ISBN-13: 9781284180909
ISBN-10: 1284180905
Author: Jamsa, Kris
Publication date: 2020
Publisher: Jones & Bartlett Learning
Format: Paperback 668 pages
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Book details

ISBN-13: 9781284180909
ISBN-10: 1284180905
Author: Jamsa, Kris
Publication date: 2020
Publisher: Jones & Bartlett Learning
Format: Paperback 668 pages

Summary

Acknowledged authors Jamsa, Kris wrote Introduction to Data Mining and Analytics comprising 668 pages back in 2020. Textbook and eTextbook are published under ISBN 1284180905 and 9781284180909. Since then Introduction to Data Mining and Analytics textbook was available to sell back to BooksRun online for the top buyback price of $ 4.28 or rent at the marketplace.

Description

Data Mining and Analytics provides a broad and interactive overview of a rapidly growing field. The exponentially increasing rate at which data is generated creates a corresponding need for professionals who can effectively handle its storage, analysis, and translation. With a dual focus on concepts and operations, this text comprises a complete how-to and is an excellent resource for anyone considering the field.

No programming experience is necessary to make the most of this resource. Case studies and hands-on activities incorporate real-world data sets and allow students the opportunity to exercise their new skills. Our Cloud Desktop integrates popular data mining tools, giving students a valuable familiarity with industry-standard applications.

After defining the concepts of data mining and machine learning, Data Mining and Analytics delves into the types of databases, their respective relevance and popularity, and the trends that affect their use. The importance of data visualization for communication purposes is explored, as are the processes of data cleansing, clustering, and classification. Excel, SQL, NoSQL, Python, and R programming all receive in-depth treatments, supplemented with hands-on exercises. Operations covered in earlier chapters are given real-world context through a practical application to the current issues of “big data” and of text and image data mining. The text concludes by describing an analyst’s steps from planning through execution, ensuring that readers gain the technical know-how to launch, lead, or support a data project in the workplace.

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