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Statistical Thinking in Python (Part 1)

Build the foundation you need to think statistically and to speak the language of your data.

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3 Hours18 Videos61 Exercises151,250 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


    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.

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    Introduction to Exploratory Data Analysis
    50 xp
    Tukey's comments on EDA
    50 xp
    Advantages of graphical EDA
    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

In the following tracks

Data Scientist Statistics Fundamentals


Hugo Bowne-AndersonYashas Roy
Justin Bois Headshot

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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