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

This book covers the transition between classical methods and data science, as well as a plethora of software tools to widen the learner's horizon. It uses concrete examples to explain the pros and cons of various software applications, such as SAS, Python, and JMP Pro.

Format: Hardback
Length: 280 pages
Publication date: 27 October 2022
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the cutting-edge techniques of data science, encompassing both conceptual and procedural aspects. It offers a comprehensive exploration of diverse methods, including dynamic data visualization, artificial neural networks, ensemble methods, and text mining. One of the distinguishing features of this book is its ability to bridge the gap between traditional statistics and modern data science. While most students in social sciences, engineering, and business have taken introductory statistics courses, these often fail to address the similarities and differences between traditional statistics and modern data science. As a result, learners may feel disoriented by the drastic paradigm shift. In response, some traditionalists reject data science altogether, while others resort to using data mining tools as a "black box," without a comprehensive understanding of the foundational distinctions between traditional and modern methods. This book aims to clarify the transition between classical methods and data science, covering topics such as p-value to log worth, resampling to ensemble methods, and content analysis to text mining.

Furthermore, this book seeks to broaden the learner's perspective by introducing a wide range of software tools. It is reminiscent of the saying, "When a technician has a hammer, every problem seems to be a nail." Similarly, many textbooks focus solely on a single software package, limiting the learner's ability to adapt the tool to the problem rather than the other way around. To address this issue, a competent analyst should possess a diverse tool set, enabling them to tackle various challenges effectively. For instance, in highly regulated industries like pharmaceutical and banking, where crucial data is involved, having a comprehensive tool set is essential.

In conclusion, this book is a valuable resource for students, professionals, and researchers seeking to excel in data science. Its comprehensive coverage of methods, coupled with its emphasis on bridging the gap between traditional statistics and modern data science, makes it an essential tool for anyone looking to stay ahead in the field. By equipping learners with a diverse tool set and expanding their horizons, this book empowers them to tackle complex data-driven problems with confidence and expertise.

Weight: 700g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367721466