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Data Science For Dummies by Lillian Pierson

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Chapter 7

Mathematical Modeling in Data Science

In This Chapter

arrow Building decision models with multi-criteria decision making

arrow Approximating values with Taylor polynomials

arrow Dividing and conquering with bisection methods

arrow Predicting the future with Markov chains

A lot gets said about using statistics to solve problems in data science, but the data science community rarely mentions mathematical methods. Despite their less-than-superstar status, however, mathematical methods can be extremely helpful, especially if you’re interested in building concise decision models. You can also use them to make fast approximations and to predict future values based on what you know of your present data. In this chapter, I discuss how you can use multi-criteria decision making and numerical methods, as well as Markov chains, to do all of the above.

Introducing Multi-Criteria Decision Making (MCDM)

Life is complicated. We’re often forced to make decisions where several different criteria come into play, and it often seems unclear what criterion should have priority. Mathematicians, being mathematicians, ...

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