IN THIS CHAPTER
Introducing the core basics of statistical probability
Reducing dataset dimensionality
Building decision models with multi-criteria decision making
Diving into regression methods
Overviewing outlier detection
Talking about time series analysis
Math and statistics are not the scary monsters that many people make them out to be. In data science, the need for these quantitative methods is simply a fact of life — and nothing to get alarmed over. Although you must have a handle on the math and statistics that are necessary to solve a problem, you don’t need to go study and get a degree in those fields.
Contrary to what many pure statisticians would have you believe, the data science field is not ...