Data science and machine learning for non-programmers : using SAS Enterprise miner / Dothang Truong.

By:

Truong, Dothang [author.]

Material type: Text

TextPublisher: Boca Raton : CRC Press/Taylor & Francis Group, 2024Edition: First editionDescription: xii, 577 pages : illustrations ; 27 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780367755386
9780367751968

Subject(s):

LOC classification:

QA 76.9 T78 2024

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.

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, 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 utilise 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"-- Provided by publisher.

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Holdings
Item type	Current library	Shelving location	Call number	Status	Date due	Barcode
Book	TUP Manila Library	General Circulation Section-GF	QA 76.9 T78 2024 (Browse shelf(Opens below))	Available		P00034207

Includes bibliographical references (pages 555-559) and index.

"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 utilise 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"-- Provided by publisher.

Truong, D. (2024). Data science and machine learning for non-programmers: Using SAS Enterprise miner (1st ed.). CRC Press/Taylor & Francis Group.

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