Build your ultimate AI agent
Course Description
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, speak the language of your data, and understand what your data is telling you. The foundations of statistical thinking took decades to build, but 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.
Prerequisites
Curriculum
Course outline
1
Graphical Exploratory Data Analysis
Before diving into sophisticated statistical inference techniques, you should first explore your data by plotting them and computing simple summary statistics. This process, called exploratory data analysis, is a crucial first step in statistical analysis of data.
- Introduction to Exploratory Data Analysis50 XP
- What is the goal of statistical inference?50 XP
- Advantages of graphical EDA50 XP
- Plotting a histogram50 XP
- Plotting a histogram of iris data100 XP
- Axis labels!100 XP
- Adjusting the number of bins in a histogram100 XP
- Plot all of your data: Bee swarm plots50 XP
- Bee swarm plot100 XP
- Interpreting a bee swarm plot50 XP
- Plot all of your data: ECDFs50 XP
- Computing the ECDF100 XP
- Plotting the ECDF100 XP
- Comparison of ECDFs100 XP
- Onward toward the whole story!50 XP
2
Quantitative Exploratory Data Analysis
In this chapter, you will compute useful summary statistics, which serve to concisely describe salient features of a dataset with a few numbers.
3
Thinking Probabilistically-- Discrete Variables
Statistical inference rests upon probability. Because we can very rarely say anything meaningful with absolute certainty from data, we use probabilistic language to make quantitative statements about data. In this chapter, you will learn how to think probabilistically about discrete quantities: those that can only take certain values, like integers.
4
Thinking Probabilistically-- Continuous Variables
It’s time to move onto 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 final chapter, you will be speaking the probabilistic language you need to launch into the inference techniques covered in the sequel to this course.
Statistical Thinking in Python (Part 1)
Course
Complete
