Data Science for Business Quotes

“If you can’t explain it simply, you don’t understand it well enough. — Albert Einstein”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“data mining is an exploratory undertaking closer to research and development than it is to engineering.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“Unfortunately, creating an objective function that matches the true goal of the data mining is usually impossible, so data scientists often choose based on faith[22] and experience.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“The need for managers with data-analytic skills The consulting firm McKinsey and Company estimates that “there will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.” (Manyika, 2011). Why 10 times as many managers and analysts than those with deep analytical skills? Surely data scientists aren’t so difficult to manage that they need 10 managers! The reason is that a business can get leverage from a data science team for making better decisions in multiple areas of the business. However, as McKinsey is pointing out, the managers in those areas need to understand the fundamentals of data science to effectively get that leverage.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“overfit: it acquires details of the training set that are not characteristic of the population in general, as represented by the holdout set. In”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“information is a quantity that reduces uncertainty about something. So, if an old pirate gives me information about where his treasure is hidden that does not mean that I know for certain where it is, it only means that my uncertainty about where the treasure is hidden is reduced.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“strawberry Pop-Tarts increase in sales, like seven times their normal sales rate, ahead of a hurricane,’ Ms. Dillman said in a recent interview. ‘And the pre-hurricane top-selling item was beer.’”[”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“Data mining is used for general customer relationship management to analyze customer behavior in order to manage attrition and maximize expected customer value.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“In the past, firms could employ teams of statisticians, modelers, and analysts to explore datasets manually, but the volume and variety of data have far outstripped the capacity of manual analysis.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“We didn’t know in the past that strawberry Pop-Tarts increase in sales, like seven times their normal sales rate, ahead of a hurricane,’ Ms. Dillman said in a recent interview. ‘And the pre-hurricane top-selling item was beer.’”[”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“framing a business problem in terms of expected value can allow us to systematically decompose it into data mining tasks.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“High-level knowledge of the fundamentals helps creative business analysts see novel formulations.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

“In analytics, it’s more important for individuals to be able to formulate problems well, to prototype solutions quickly, to make reasonable assumptions in the face of ill-structured problems, to design experiments that represent good investments, and to analyze results.”
― Foster Provost, Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking