Link to Content:

DataCamp Statistical Thinking in Python Part 1

Created/Published/Taught by:

DataCamp

Justin Bois

Hugo Bowne-Anderson

Vincent Lan

Yashas Roy

Content Found Via:

DataCamp

Free? Partially: Some Free Content, Some Paid

Cost Range:

$0.00 - $29.00

Tags: exploratory data analysis / python / statistics

Difficulty Rating:

FromÂ DataCamp:

“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 the course.”

This course consists of four chapters:

- Graphical exploratory data analysis
- Quantitative exploratory data analysis
- Thinking probabilistically – Discrete variables
- Thinking probabilistically – Continuous variables

Part 2 of this course can be found here: Statistical Thinking in Python 2.

Recommended Prerequisites: Intro to Python for Data Science, Intermediate Python for Data Science

Go to Content: DataCamp – Statistical Thinking in Python (Part 1)

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