Data Science and Machine Learning for Non-Programmers - Using SAS Enterprise Miner

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

List of contents

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.

About the author

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.

Summary

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

Product details

• Authors: Dothang Truong
• Publisher: Taylor and Francis
• Languages: English
• Product format: Paperback / Softback
• Release: 01.01.2026
• EAN: 9780367751968
• ISBN: 978-0-367-75196-8
• No. of pages: 577
• Illustrations: farbige Illustrationen, Zeichnungen, farbig, Tabellen, schwarz-weiss
• Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery Series
• Subjects: Natural sciences, medicine, IT, technology > Biology > Ecology; machine learning, COMPUTERS / Programming / Algorithms, algorithms and data structures, Algorithms & data structures, COMPUTERS / Data Science / Machine Learning