1st Edition

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

By Dothang Truong Copyright 2024
    589 Pages 419 Color Illustrations
    by Chapman & Hall

    589 Pages 419 Color Illustrations
    by Chapman & Hall

    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.

    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


    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.