Data Science for Business: What You Need to Know About Data by Foster Provost, Tom Fawcett PDF

By Foster Provost, Tom Fawcett

Written by way of popular information technological know-how specialists Foster Provost and Tom Fawcett, Data technological know-how for enterprise introduces the elemental rules of information technology, and walks you thru the "data-analytic thinking" worthy for extracting worthy wisdom and company price from the information you acquire. This advisor additionally is helping you realize the various data-mining thoughts in use today.

Based on an MBA direction Provost has taught at manhattan collage over the last ten years, Data technology for Business offers examples of real-world company difficulties to demonstrate those ideas. You’ll not just how you can increase verbal exchange among company stakeholders and information scientists, but in addition how take part intelligently on your company’s info technology tasks. You’ll additionally become aware of the best way to imagine data-analytically, and entirely savor how facts technological know-how tools can aid company decision-making.
• know how information technological know-how matches on your organization—and how one can use it for aggressive virtue
• deal with information as a company asset that calls for cautious funding if you’re to achieve genuine price
• method company difficulties data-analytically, utilizing the data-mining technique to assemble solid information within the ultimate means
• examine basic recommendations for really extracting wisdom from facts
• follow facts technological know-how ideas while interviewing info technology task applicants

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Additional resources for Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking

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Assume you just landed a great analytical job with MegaTelCo, one of the largest tele‐ communication firms in the United States. They are having a major problem with cus‐ tomer retention in their wireless business. In the mid-Atlantic region, 20% of cell phone customers leave when their contracts expire, and it is getting increasingly difficult to acquire new customers. Since the cell phone market is now saturated, the huge growth in the wireless market has tapered off. Communications companies are now engaged in battles to attract each other’s customers while retaining their own.

Data science needs access to data and it often benefits from sophisticated data engineering that data processing technologies may facilitate, but these technologies are not data science technologies per se. They support data science, as shown in Figure 1-1, but they are useful for much more. Data processing technologies are very important for many data-oriented business tasks that do not involve extracting knowledge or data-driven decision-making, such as ef‐ ficient transaction processing, modern web system processing, and online advertising campaign management.

Tens of millions of customers have contracts expiring each month, so each one of them has an increased likelihood of defection in the near future. If we can improve our ability to estimate, for a given customer, how profitable it would be for us to focus on her, we can potentially reap large benefits by applying this ability to the millions of customers in the population. This same logic applies to many of the areas where we have seen the most intense application of data science and data mining: direct marketing, online advertising, credit scoring, financial trading, help-desk management, fraud detection, search ranking, product rec‐ ommendation, and so on.

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