While Python remains a highly popular language for data wrangling, visualization, general machine learning, and web integration, its statistical modeling abilities are far less advertised. A large percentage of data scientists still use other statistical-computing languages such as R, MATLAB, SPSS, or SAS for their modeling and analysis.

While each of these alternatives offers its own unique blend of features and power for statistical analyses, it’s useful for any data scientist to know more about various Python frameworks and methods that can be used for routine operations of descriptive and inferential statistics. It helps to build a unified data processing pipeline.

This short book describes and demonstrates some of the essential statistical modeling frameworks and methods for Python, which can help us do statistical modeling and probabilistic computation. We also focus on Pythonic examples of doing stochastic simulation/probabilistic computing. This is one area that is traditionally neglected in many Python-based data science/statistics textbooks or online resources.

Example chapters on stochastic simulation,

• Computing the value of 'pi' by simulation of throwing dirt
• Monte Carlo integration of complicated mathematical functions
• Brownian motion
• Confidence interval simulation by repeated sampling
• Game of chance simulation
• How to create a pseudo-random generator and produce arbitrary random variables from first principles
• Stock market portfolio simulation