Dothang Truong

Data Science and Machine Learning for Non-Programmers (eBook, ePUB)

Using SAS Enterprise Miner

• Format: ePub

Produktbeschreibung

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.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 589
• Erscheinungstermin: 23. Februar 2024
• Englisch
• ISBN-13: 9781003835639
• Artikelnr.: 69742204

Autorenporträt

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.

Inhaltsangabe

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.