Data Science and Machine Learning for Non‑Programmers: Using SAS Enterprise Miner /
Truong, Dothang,
Data Science and Machine Learning for Non‑Programmers: Using SAS Enterprise Miner / Dothang Truong. - First edition. - 577 pages : illustrations ; 26 cm.
Includes bibliographical references and index.
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.:contentReference[oaicite:2]
A hands-on introduction to data science and machine learning for non-programmers, guiding readers through practical projects using two large datasets and SAS Enterprise Miner—with no programming required. Covers full data mining workflow, result reporting, and stakeholder communication, aimed at students and professionals across diverse non-technical fields.
9780367755386
Data mining--Study and teaching.
Machine learning--Study and teaching.
Non‑programmers--Education.
SAS Enterprise Miner.
006.3 / T.D.D
Data Science and Machine Learning for Non‑Programmers: Using SAS Enterprise Miner / Dothang Truong. - First edition. - 577 pages : illustrations ; 26 cm.
Includes bibliographical references and index.
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.:contentReference[oaicite:2]
A hands-on introduction to data science and machine learning for non-programmers, guiding readers through practical projects using two large datasets and SAS Enterprise Miner—with no programming required. Covers full data mining workflow, result reporting, and stakeholder communication, aimed at students and professionals across diverse non-technical fields.
9780367755386
Data mining--Study and teaching.
Machine learning--Study and teaching.
Non‑programmers--Education.
SAS Enterprise Miner.
006.3 / T.D.D