Data Science and Machine Learning for Non-Programmers by Dothang Truong (Hardback)

£89.99
Available
Product Ref. (e.g. ISBN, SKU)
9781032740003
Published:
16 Dec 2025
Format:
Hardback
Number of pages:
600 pages
Dimensions:
254mm
Weight:
0.453kgs
Author:
This book is designed to cater to readers who prefer a hands-on guide using SPSS Modeler, a widely popular software that does not require coding or programming skills. It targets a broad audience, including students, lecturers, researchers, and industry professionals.
As artificial intelligence advances at an exponential pace, understanding data science and machine learning has become increasingly essential. Yet, the wide range of available resources can be daunting, posing challenges for beginners. This second book builds on the foundation laid in the first, Data Science and Machine Learning for Non-Programmers: Using SAS Enterprise Miner, providing similar fundamental knowledge of data science and machine learning in an accessible way. It is specifically designed to cater to readers who prefer a hands-on guide using SPSS Modeler, a widely popular software that does not require coding or programming skills. Like the first book, this volume helps learners from various non-technical fields gain practical insight into machine learning but shifts the focus to a different tool for those seeking alternatives to coding.In this book, readers are guided through practical implementations using real datasets and SPSS Modeler, a user-friendly data mining tool. The approach remains consistent with a focus on application, providing step-by-step instructions for all stages of the data mining process using two large datasets, ensuring continuity and reinforcing concepts in a cohesive project framework. This book also offers practical advice on presenting data mining results effectively, aiding readers in communicating insights clearly to stakeholders.Together with the first book, this volume is a companion for beginners and experienced practitioners alike. It targets a broad audience, including students, lecturers, researchers, and industry professionals. It offers flexibility in learning pathways and deepens understanding of data science using easy-to-follow, software-based approaches.

About the Author: Dr. Dothang Truong is the Associate Dean and Professor of Graduate Studies at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, artificial intelligence, data analytics, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for the outstanding achievement of excellence in aviation research and scholarship.

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Number of pages 600

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