
Data Science and Machine Learning for Non-Programmers by Dothang Truong
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.
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Data Mining with R
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Biological Data Mining
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Exploratory Data Analysis Using R
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Advanced Data Science and Analytics with Python
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Automated Data Analysis Using Excel
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Privacy-Aware Knowledge Discovery
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Computational Intelligent Data Analysis for Sustainable Development
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Data Mining for Design and Marketing
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Data Classification
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Statistical Data Mining Using SAS Applications
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Mining Software Specifications
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Contrast Data Mining
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Introduction to Computational Health Informatics
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Social Networks with Rich Edge Semantics
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Graph-Based Social Media Analysis
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Industrial Applications of Machine Learning
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Computational Business Analytics
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Event Mining
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Advances in Machine Learning and Data Mining for Astronomy
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Human Capital Systems, Analytics, and Data Mining
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Practical Graph Mining with R
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Large-Scale Machine Learning in the Earth Sciences
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Support Vector Machines
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Geographic Data Mining and Knowledge Discovery
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Demystifying AI
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Knowledge Discovery from Data Streams
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Data Science and Analytics with Python
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RapidMiner, Second Edition
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Feature Engineering for Machine Learning and Data Analytics
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Text Mining and Visualization
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Knowledge Guided Machine Learning
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Healthcare Data Analytics
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Text Mining
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.
| SKU | Unavailable |
| ISBN 13 | 9780367751968 |
| ISBN 10 | 0367751968 |
| Title | Data Science and Machine Learning for Non-Programmers |
| Author | Dothang Truong |
| Series | Chapman And Hall Crc Data Mining And Knowledge Discovery Series |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Taylor & Francis Ltd |
| Year published | 2024-02-23 |
| Number of pages | 616 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |
































