# Statistical Thinking in Python (Part 1)

Build the foundation you need to think statistically and to speak the language of your data.
3 Hours18 Videos61 Exercises121,066 Learners
4550 XP

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## 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.

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.
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.
In the following tracks
Data Science for Everyone Machine Learning for EveryoneData Scientist Statistics Fundamentals
Collaborators
Hugo Bowne-AndersonVincent LanYashas Roy

#### 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!

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden