Data Mining and Exploration

This book will introduce both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are a couple of unique elements that can set the book apart from its rivals.

Most students in social sciences, engineering, and business have taken at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between these two schools of thought, and as a result learner are disoriented by this seemingly drastic paradigm shift. As such, some traditionalists reject data science altogether while some new data analysts employ data mining tools as a "black box", without a comprehensive view of the foundational differences between traditional and modern methods (e.g. dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation...etc.). To remediate this problem, this book will provide the readers with the details of the similarities and differences between classical methods and data science, as well as the path for the transition (e.g. from p value to LogWorth, from resampling to ensemble methods, from content analysis to text mining...etc.).

Furthermore, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem into the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g. SAS) are indispensable. For a mid-size and small company, open source packages such as Python and R become handy. If the research goal is to create an executive summary quickly, the logical choice is rapid predictive modelling. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro and Tableau is a better option. This book will use concrete examples to explain the pros and cons of various software applications.