Introduction
In the previous chapters, we reviewed technical aspects of high-performance interactive computing in Python. We now begin the second part of this book by illustrating a variety of scientific questions that can be tackled with Python.
In this chapter, we introduce statistical methods for data analysis. In addition to covering statistical packages such as pandas, statsmodels, and PyMC3, we will explain the basics of the underlying mathematical principles. Therefore, this chapter will be most profitable if you have basic experience with probability theory and calculus.
The next chapter, Chapter 8, Machine Learning, is closely related; the underlying mathematics is very similar, but the goals are slightly different. In this chapter, we show how to gain insight into real-world data and how to make informed decisions in the presence of uncertainty. In the next chapter, the goal is to learn from data—that is, to generalize and to predict outcomes from partial observations.
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