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Data Science and Machine Learning for Non‑Programmers: Using SAS Enterprise Miner / Dothang Truong.

By: Material type: TextTextPublisher: Boca Raton, FL : CRC Press, 2024Edition: First editionDescription: 577 pages : illustrations ; 26 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780367755386
Subject(s): DDC classification:
  • 23 006.3 T.D.D
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.:contentReference[oaicite:2]{index=2}
Summary: 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.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Books Books Media and mass communication Library B4 006.3 T.D.D C.1 Available MA0002476
Total holds: 0

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]{index=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.

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