Introduction Data Mining Analytics by Jamsa Kris (17 results)

Paperback. Condition: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

paperback. Condition: Acceptable. Used books may not include access codes or one time use codes. Proven Seller with Excellent Customer Service. Choose expedited shipping and get it FAST.

paperback. Condition: Good. No access code. Proven Seller with Excellent Customer Service. Choose expedited shipping and get it FAST.

paperback. Condition: New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

paperback. Condition: New. Brand New! Proven Seller with Excellent Customer Service. Choose expedited shipping and get it FAST.

Paperback. Condition: new. New Copy. Customer Service Guaranteed.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: New. 2020. Paperback. . . . . .

Paperback. Condition: Brand New. pap/psc edition. 668 pages. 9.25x7.50x1.00 inches. In Stock.

Paperback. Condition: New. Introduction to 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. 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.…

Condition: As New. Unread book in perfect condition.

Condition: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Condition: New. In English.

Paperback. Condition: New. Introduction to 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. 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.…