Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking has been used successfully as a textbook for a very wide variety of data science courses. It is used in support of a variety of data science–related programs: the original MBA and MSIS programs, undergraduate business analytics, NYU MS in Business Analytics program, and as the Introduction to Data Science for NYU’s new MS in Data Science.
This book introduces a collection of the most important fundamental concepts of data science. The concepts span the process from envisioning the problem, to applying data science techniques, to deploying the results to improve decision-making. The concepts also undergird a large array of business analytics methods and techniques.
The concepts fit into three general types:
- Concepts about how data science fits in the organization and the competitive landscape, including ways to attract, structure, and nurture data science teams; ways for thinking about how data science leads to competitive advantage; and tactical concepts for doing well with data science projects.
- General ways of thinking data-analytically. These help in identifying appropriate data and consider appropriate methods. The concepts include the data mining process as well as the collection of different high-level data mining tasks.
- General concepts for actually extracting knowledge from data, which undergird the vast array of data science tasks and their algorithms.
A traditional algorithm-oriented book might present each of these tasks in a different chapter, under different names, with common aspects buried in algorithm details or mathematical propositions. Instead, Data Science for Business book focuses on the unifying concepts, presenting specific tasks and algorithms as natural manifestations of them.
Release: August 19th, 2013.
Pages: 409 (in PDF)
Size: 17 MB
Authors: Foster Provost, Tom Fawcett
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