Dothang Truong, PhD, is a Professor of Graduate Studies at Embry Riddle Aeronautical University, Daytona Beach, Florida. He has extensive teaching and research experience in machine learning, data analytics, air transportation management, and supply chain management. In 2022, Dr. Truong received the Frank Sorenson Award for outstanding achievement of excellence in aviation research and scholarship.
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially; however, the abundance of resources can be overwhelming, making it challenging for new learners. This book aims to address this disparity and cater to learners from various non-technical fields, enabling them to utilize machine learning effectively.
Adopting a hands-on approach, readers are guided through practical implementations using real datasets and SAS Enterprise Miner, a user-friendly data mining software that requires no programming. Throughout the chapters, two large datasets are used consistently, allowing readers to practice all stages of the data mining process within a cohesive project framework. This book also provides specific guidelines and examples on presenting data mining results and reports, enhancing effective communication with stakeholders.
Designed as a guiding companion for both beginners and experienced practitioners, this book targets a wide audience, including students, lecturers, researchers, and industry professionals from various backgrounds.
Part I: Introduction to Data Mining. 1. Introduction to Data Mining and Data Science. 2. Data Mining Processes, Methods, and Software. 3. Data Sampling and Partitioning. 4. Data Visualization and Exploration. 5. Data Modification. Part II: Data Mining Methods. 6. Model Evaluation. 7. Regression Methods. 8. Decision Trees. 9. Neural Networks. 10. Ensemble Modeling. 11. Presenting Results and Writing Data Mining Reports. 12. Principal Component Analysis. 13. Cluster Analysis. Part III: Advanced Data Mining Methods. 14. Random Forest. 15. Gradient Boosting. 16. Bayesian Networks.
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming.
Inhaltsverzeichnis
PREFACE
PART I: INTRODUCTION TO DATA MINING
Chapter 1. Introduction to Data Mining and Data Science
Chapter 2. Data Mining Process, Methods, and Software
Chapter 3. Data Sampling and Partitioning
Chapter 4. Data Visualization and Exploration
Chapter 5. Data Modification
PART 2: 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
TERM DEFINITIONS
REFERENCES
APPENDICES
Klappentext
As data continues to grow exponentially, knowledge of data science and machine learning has become more crucial than ever. Machine learning has grown exponentially, however, the abundance of resources can be overwhelming
