This textbook first appeared in early 2007 and has been used by numerous students and practitioners and in many courses, including our own experience teaching this material both online and in person for more than 15 years. The first edition, based on the Excel add-in Analytic Solver Data Mining (previously XLMiner), was followed by two more Analytic Solver editions, a JMP edition, an R edition, and now this Python edition, with its companion website, www.dataminingbook.com.

This new Python edition, which relies on the free and open-source Python programming language, presents output from Python, as well as the code used to produce that output, including specification of the appropriate packages and functions, the dominant one being scikit-learn. Unlike computer-science or statistics-oriented textbooks, the focus in this book is on data mining concepts, and how to implement the associated algorithms in Python. We assume a basic familiarity with Python.

For this Python edition, a new co-author, Peter Gedeck comes on board bringing extensive data science experience in business. In addition to providing Python code and output, this edition also incorporates updates and new material based on feedback from instructors teaching MBA, MS, undergraduate, diploma, and executive courses, and from their students as well. Importantly, this edition includes for the first time an extended section on Data Ethics (Section 2.9).

A note about the book’s title: The first two editions of the book used the title Data Mining for Business Intelligence. Business Intelligence today refers mainly to reporting and data visualization (“what is happening now”), while Business Analytics has taken over the “advanced analytics,” which include predictive analytics and data mining. In this new edition, we therefore use the updated terms.

This Python edition includes the material that was recently added in the third edition of the original (Analytic Solver based) book:

• Social network analysis
• Text mining
• Ensembles
• Uplift modeling
• Collaborative filtering

Since the appearance of the (Analytic Solver based) second edition, the landscape of the courses using the textbook has greatly expanded: whereas initially, the book was used mainly in semester-long elective MBA-level courses, it is now used in a variety of courses in Business Analytics degrees and certificate programs, ranging from undergraduate programs, to post-graduate and executive education programs. Courses in such programs also vary in their duration and coverage. In many cases, this textbook is used across multiple courses. The book is designed to continue supporting the general “Predictive Analytics” or “Data Mining” course as well as supporting a set of courses in dedicated business analytics programs.

A general “Business Analytics,” “Predictive Analytics,” or “Data Mining” course, common in MBA and undergraduate programs as a one-semester elective, would cover Parts I–III, and choose a subset of methods from Parts IV and V. Instructors can choose to use cases as team assignments, class discussions, or projects. For a two-semester course, Part VI might be considered, and we recommend introducing the new Part VII (Data Analytics).

For a set of courses in a dedicated business analytics program, here are a few courses that have been using our book:

• Predictive Analytics—Supervised Learning: In a dedicated Business Analytics program, the topic of Predictive Analytics is typically instructed across a set of courses. The first course would cover Parts I–IV and instructors typically choose a subset of methods from Part IV according to the course length. We recommend including the Chapter 13 on ensembles in such a course, as well as “Part VII: Data Analytics.”
• Predictive Analytics—Unsupervised Learning: This course introduces data exploration and visualization, dimension reduction, mining relationships, and clustering (Parts III and V). If this course follows the Predictive Analytics: Supervised Learning course, then it is useful to examine examples and approaches that integrate unsupervised and supervised learning, such as the new part on “Data Analytics.”
• Forecasting Analytics: A dedicated course on time series forecasting would rely on Part VI.
• Advanced Analytics: A course that integrates the learnings from Predictive Analytics (supervised and unsupervised learning). Such a course can focus on Part VII: Data Analytics, where social network analytics and text mining are introduced. Some instructors choose to use the Cases (Chapter 21) in such a course.

In all courses, we strongly recommend including a project component, where data are either collected by students according to their interest or provided by the instructor (e.g., from the many data mining competition datasets available). From our experience and other instructors’ experience, such projects enhance the learning and provide students with an excellent opportunity to understand the strengths of data mining and the challenges that arise in the process.

GALIT SHMUELI, PETER C. BRUCE, PETER GEDECK, AND NITIN R. PATEL

2019