Data Mining and Exploration - From Traditional Statistics to Modern Data Science

This book covers both conceptual and procedural aspects of cutting-edge data mining methods, and the path for transitioning from classical statistics and modern data science. Readers will learn how various open source and commercial software modules can be properly applied to different situations.

List of contents

1. Re-examination of Traditional Statistics 2. Why Data Science? 3. Cutting Edge Data Analytical Tools 4. Exploratory Data Analysis and Data Visualization: Pattern Seeking 5. Generalized Regression: Penalty against Complexity 6. Classification and Model Screening 7. Ensemble Methods: The Wisdom of the Crowd 8. Dimension Reduction: Breaking the Curse of Dimensionality 9. Clustering: Divide and Conquer 10. Neural Networks: Machines Mimic Human Intelligence 11. Text Mining: Structure the Unstructured

About the author

Chong Ho Alex Yu has a Ph.D. in Educational Psychology with a focus on Measurement, Statistics, and Methodological Studies, and a Ph.D. in Philosophy with specialization in History and Philosophy of Science (Arizona State University). He joined Azusa Pacific University in 2012 and has served in various positions, including Director of Data Analytics, Professor of Behavioural and Applied Science, Adjunct Faculty of Mathematics, Quantitative Research Consultant, and Committee chair of the Big Data Discovery Summit.

Summary

This book covers both conceptual and procedural aspects of cutting-edge data mining methods, and the path for transitioning from classical statistics and modern data science. Readers will learn how various open source and commercial software modules can be properly applied to different situations.

Product details

• Authors: Chong Ho Alex Yu
• Publisher: Taylor and Francis
• Languages: English
• Product format: Paperback / Softback
• Released: 04.10.2024
• EAN: 9780367721510
• ISBN: 978-0-367-72151-0
• No. of pages: 280
• Weight: 489 g
• Illustrations: schwarz-weiss Illustrationen, farbige Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Zeichnungen, farbig, Tabellen, schwarz-weiss
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Social sciences, law, business > Business > General, dictionaries; Data Mining, SCIENCE / Life Sciences / General, Artificial Intelligence, BUSINESS & ECONOMICS / Statistics, MATHEMATICS / Probability & Statistics / General, Probability & statistics, Automatic control engineering, Probability and statistics, COMPUTERS / Data Science / Data Analytics