- Version 2013
- Download 10
- File Size 18.74
- File Count 1
- Create Date October 10, 2020
- Last Updated May 28, 2021

# Data Science for Business | Data Science

Data Science for Business | Data Science

Data Science for Business

What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett

Data Science for Business is intended for several sorts of readers:

- Business people who will be working with data scientists, managing data science– oriented projects, or investing in data science ventures,
- Developers who will be implementing data science solutions, and
- Aspiring data scientists.

This is not a book about algorithms, nor is it a replacement for a book about algorithms. We deliberately avoided an algorithm-centered approach. We believe there is a relatively small set of fundamental concepts or principles that underlie techniques for extracting useful knowledge from data. These concepts serve as the foundation for many wellknown algorithms of data mining. Moreover, these concepts underlie the analysis of data-centered business problems, the creation and evaluation of data science solutions, and the evaluation of general data science strategies and proposals. Accordingly, we organized the exposition around these general principles rather than around specific algorithms. Where necessary to describe procedural details, we use a combination of text and diagrams, which we think are more accessible than a listing of detailed algo‐ rithmic steps.

The book does not presume a sophisticated mathematical background. However, by its very nature the material is somewhat technical—the goal is to impart a significant un‐ derstanding of data science, not just to give a high-level overview. In general, we have tried to minimize the mathematics and make the exposition as “conceptual” as possible.

Colleagues in industry comment that the book is invaluable for helping to align the understanding of the business, technical/development, and data science teams. That observation is based on a small sample, so we are curious to see how general it truly is (see Chapter 5!). Ideally, we envision a book that any data scientist would give to his collaborators from the development or business teams, effectively saying: if you really

want to design/implement top-notch data science solutions to business problems, we all need to have a common understanding of this material.

Colleagues also tell us that the book has been quite useful in an unforeseen way: for preparing to interview data science job candidates. The demand from business for hiring data scientists is strong and increasing. In response, more and more job seekers are presenting themselves as data scientists. Every data science job candidate should un‐ derstand the fundamentals presented in this book. (Our industry colleagues tell us that they are surprised how many do not. We have half-seriously discussed a follow-up pamphlet “Cliff ’s Notes to Interviewing for Data Science Jobs.”)

**Our Conceptual Approach to Data Science **

In this book we introduce a collection of the most important fundamental concepts of data science. Some of these concepts are “headliners” for chapters, and others are in‐ troduced more naturally through the discussions (and thus they are not necessarily labeled as fundamental concepts). 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 meth‐ ods and techniques.

The concepts fit into three general types:

- Concepts about how data science fits in the organization and the competitive land‐ scape, including ways to attract, structure, and nurture data science teams; ways for thinking about how data science leads to competitive advantage; and tactical con‐ cepts 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 pro‐ cess 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.

For example, one fundamental concept is that of determining the similarity of two entities described by data. This ability forms the basis for various specific tasks. It may be used directly to find customers similar to a given customer. It forms the core of several prediction algorithms that estimate a target value such as the expected resouce usage of a client or the probability of a customer to respond to an offer. It is also the basis for clustering techniques, which group entities by their shared features without a focused objective. Similarity forms the basis of information retrieval, in which documents or webpages relevant to a search query are retrieved. Finally, it underlies several common algorithms for recommendation. 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. In this book we instead focus on the unifying concepts, presenting specific tasks and algorithms as natural manifes‐ tations of them.

As another example, in evaluating the utility of a pattern, we see a notion of lift— how much more prevalent a pattern is than would be expected by chance—recurring broadly across data science. It is used to evaluate very different sorts of patterns in different contexts. Algorithms for targeting advertisements are evaluated by computing the lift one gets for the targeted population. Lift is used to judge the weight of evidence for or against a conclusion. Lift helps determine whether a co-occurrence (an association) in data is interesting, as opposed to simply being a natural consequence of popularity.

We believe that explaining data science around such fundamental concepts not only aids the reader, it also facilitates communication between business stakeholders and data scientists. It provides a shared vocabulary and enables both parties to understand each other better. The shared concepts lead to deeper discussions that may uncover critical issues otherwise missed.