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Full Description
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 IBM 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 IBM 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.
Contents
PART I Introduction to Data Mining
Chapter 1 Introduction to Data Mining and Data Science
Chapter 2 Data Mining Processes, Methods, and Software
Chapter 3 Data Sampling and Partitioning
Chapter 4 Data Visualization and Exploration
Chapter 5 Data Modification
PART II Data Mining Methods
Chapter 6 Model Evaluation
Chapter 7 Regression Methods
Chapter 8 Decision Trees
Chapter 9 Neural Networks
Chapter 10 Ensemble Modeling
Chapter 11 Presenting Results and Writing Data Mining Reports
Chapter 12 Principal Component Analysis
Chapter 13 Cluster Analysis
PART III Advanced Data Mining Methods
Chapter 14 Random Forest
Chapter 15 Gradient Boosting
Chapter 16 Bayesian Networks
Appendix A
Appendix B
Appendix C
