Data Mining for Business Analytics
by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Nitin R. Patel
Foreword by Gareth James
The field of statistics has existed in one form or another for 200 years, and by the second half of the 20th century had evolved into a well-respected and essential academic discipline. However, its prominence expanded rapidly in the 1990s with the explosion of new, and enormous, data sources. For the first part of this century, much of this attention was focused on biological applications, in particular, genetics data generated as a result of the sequencing of the human genome. However, the last decade has seen a dramatic increase in the availability of data in the business disciplines, and a corresponding interest in business-related statistical applications.
The impact has been profound. Ten years ago, when I was able to attract a full class of MBA students to my new statistical learning elective, my colleagues were astonished because our department struggled to fill most electives. Today, we offer a Masters in Business Analytics, which is the largest specialized masters program in the school and has application volume rivaling those of our MBA programs. Our department’s faculty size and course offerings have increased dramatically, yet the MBA students are still complaining that the classes are all full. Google’s chief economist, Hal Varian, was indeed correct in 2009 when he stated that “the sexy job in the next 10 years will be statisticians.”
This demand is driven by a simple, but undeniable, fact. Business analytics solutions have produced significant ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access