Napoleon-Christos Oikonomou has completed

# Statistical Thinking in Python (Part 1)

3 hours
4,550 XP

## 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.

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1. 1

### Graphical Exploratory Data Analysis

Free

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.

Play Chapter Now
Introduction to Exploratory Data Analysis
50 xp
What is the goal of statistical inference?
50 xp
50 xp
Plotting a histogram
50 xp
Plotting a histogram of iris data
100 xp
Axis labels!
100 xp
Adjusting the number of bins in a histogram
100 xp
Plot all of your data: Bee swarm plots
50 xp
Bee swarm plot
100 xp
Interpreting a bee swarm plot
50 xp
Plot all of your data: ECDFs
50 xp
Computing the ECDF
100 xp
Plotting the ECDF
100 xp
Comparison of ECDFs
100 xp
Onward toward the whole story!
50 xp
2. 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. 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. 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.

Datasets

2008 election results (all states)2008 election results (swing states)Belmont StakesSpeed of light

Collaborators

Justin Bois

Lecturer at the California Institute of Technology

Justin Bois is a Teaching Professor in the Division of Biology and Biological Engineering at the California Institute of Technology. He teaches nine different classes there, nearly all of which heavily feature Python. He is dedicated to empowering students in the biological sciences with quantitative tools, particularly data analysis skills. Beyond biologists, he is thrilled to develop courses for DataCamp, whose students are an excited bunch of burgeoning data scientists!
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