By Foster Provost, Tom Fawcett
Written via well known info technological know-how specialists Foster Provost and Tom Fawcett, Data technological know-how for enterprise introduces the basic rules of information technology, and walks you thru the "data-analytic thinking" worthwhile for extracting worthy wisdom and company price from the information you acquire. This advisor additionally is helping you realize the numerous data-mining options in use today.
Based on an MBA path Provost has taught at ny collage over the last ten years, Data technological know-how for Business presents examples of real-world enterprise difficulties to demonstrate those rules. You’ll not just how you can enhance communique among enterprise stakeholders and knowledge scientists, but in addition how take part intelligently on your company’s facts technology initiatives. You’ll additionally realize how you can imagine data-analytically, and completely savor how information technology tools can aid enterprise decision-making.
• know how facts technological know-how matches on your organization—and how one can use it for aggressive virtue
• deal with facts as a enterprise asset that calls for cautious funding if you’re to realize actual worth
• procedure enterprise difficulties data-analytically, utilizing the data-mining method to collect reliable facts within the splendid means
• study basic thoughts for truly extracting wisdom from information
• observe facts technology ideas whilst interviewing information technological know-how task applicants
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Extra resources for Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
Clustering is useful in pre‐ liminary domain exploration to see which natural groups exist because these groups in turn may suggest other data mining tasks or approaches. Clustering also is used as input to decision-making processes focusing on questions such as: What products should we offer or develop? How should our customer care teams (or sales teams) be structured? We discuss clustering in depth in Chapter 6. 5. Co-occurrence grouping (also known as frequent itemset mining, association rule discovery, and market-basket analysis) attempts to find associations between enti‐ ties based on transactions involving them.
This is a classification problem because it has a binary target (the customer either purchases or does not). ” This is also a classification problem, with a three-valued target. ” This is a regression problem because it has a numeric target. The target variable is the amount of usage (actual or predicted) per customer. There are subtleties among these questions that should be brought out. For business applications we often want a numerical prediction over a categorical target. In the churn example, a basic yes/no prediction of whether a customer is likely to continue to sub‐ scribe to the service may not be sufficient; we want to model the probability that the customer will continue.
Usually the classes are mutually exclusive. ” In this example the two classes could be called will respond and will not respond. 20 | Chapter 2: Business Problems and Data Science Solutions For a classification task, a data mining procedure produces a model that, given a new individual, determines which class that individual belongs to. A closely related task is scoring or class probability estimation. A scoring model applied to an indi‐ vidual produces, instead of a class prediction, a score representing the probability (or some other quantification of likelihood) that that individual belongs to each class.