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DataCamp – Statistical Thinking in Python (Part 1) No ratings yet.




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DataCamp Statistical Thinking in Python Part 1

Created/Published/Taught by:
DataCamp
Justin Bois
Hugo Bowne-Anderson
Vincent Lan
Yashas Roy

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DataCamp

Free? Partially: Some Free Content, Some Paid

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$0.00 - $29.00

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From 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:

  1. Graphical exploratory data analysis
  2. Quantitative exploratory data analysis
  3. Thinking probabilistically – Discrete variables
  4. 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

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