We just launched Statistical Thinking in Python (Part 1) taught by Justin Bois. After all of the hard work of acquiring data and getting them into a form you can work with, you ultimately want to make clear, succinct conclusions from them. This crucial last step of a data analysis pipeline hinges on the principles of statistical inference. In this course, you will start building the foundation you need to think statistically, to speak the language of your data, to understand what they are telling you. The foundations of statistical thinking took decades upon decades to build, but they can be grasped much faster today with the help of computers. With the power of Python-based tools, you will rapidly get up to speed and begin thinking statistically by the end of this course.
Statistical Thinking in Python (Part 1) features 62 interactive exercises that combine high-quality video, in-browser coding, and gamification for an engaging learning experience that will sharpen your statistical thinking skills.
What you'll learn
In the first chapter of this course, you will first explore your data by plotting them and computing simple summary statistics. [Start First Chapter For Free] Once you're warmed up, you will compute useful summary statistics, which serve to concisely describe salient features of a data set with a few numbers. Next, you will learn how to think probabilistically about discrete quantities, those that can only take certain values, like integers. This is an important first step in building the probabilistic langauge necessary to think statistically. In the last chapter, you will focus on probabilistic thinking with continuous variables, such as those that can take on any fractional value. Many of the principles are the same, but there are some subtleties. At the end of this last chapter of the course, you will be speaking the probabilistic language you need to launch into the inference techniques covered in the sequel to this course.
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