A large portion of the field of statistics and statistical methods is dedicated to data where the distribution is known. Samples of data where we already know or can easily identify the distribution of are called parametric data. Often, parametric is used to refer to data that was drawn from a Gaussian distribution in common usage. Data in which the distribution is unknown or cannot be easily identified is called nonparametric. In the case where you are working with nonparametric data, specialized nonparametric statistical methods can be used that discard all information about the distribution.

Statistics is a field of mathematics that is universally agreed to be a prerequisite for a deeper understanding of machine learning. Although statistics is a large field with many esoteric theories and findings, the nuts and bolts tools and notations taken from the field are required for machine learning practitioners. With a solid foundation of what statistics is, it is possible to focus on just the good or relevant parts. In this crash course, you will discover how you can get started and confidently read and implement statistical methods used in machine learning with Python in seven days. This is a big and important post. You might want to bookmark it.