Introduction to statistical data analysis in Python – frequentist and Bayesian methods by Cyrille Rossant.
Activists: I know, it really sounds more exciting than a hit from a crack pipe. Right? 😉
Seriously, consider this in light of: Activists Wield Search Data to Challenge and Change Police Policy. To cut to the chase, statistics proved that DWB stops (driving while black) resulted in searches of black men more than twice as often as white men but produced no more weapons/drugs. City of Durham changed its traffic stop policy. (I don’t know if DWB is now legal in Durham or not.)
But the point is that raw data and statistics can have an impact on a brighter than average city council. Doesn’t work every time but another tool to have at your disposal.
From the webpage:
In Chapter 7, Statistical Data Analysis, we introduce statistical methods for data analysis. In addition to covering statistical packages such as pandas, statsmodels, and PyMC, we 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. While in the present 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.
I first saw the Durham story in a tweet by Tim O’Reilly. The Python book was mentioned in a tweet by Scientific Python.