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Statistical Thinking in Python (Part 1)

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  • 18 Videos
  • 61 Exercises
  • 3 hours 
  • 4,827 Participants
  • 4550 XP

Instructor(s):

Justin Bois
Justin Bois

Justin Bois is a lecturer 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!

Collaborator(s):

Hugo Bowne-Anderson Hugo Bowne-Anderson

Vincent Lan Vincent Lan

Yashas Roy Yashas Roy

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, 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 this course.

Prerequisites:

1Graphical exploratory data analysis Free

Look before you leap! A very important proverb, indeed. Prior to diving in headlong 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. So it is a fitting subject for the first chapter of Statistical Thinking in Python.