Learn about the role of visualization; scatter plots; and Jupyter notebook interactive widgets.
- [Instructor] We have worked through the hard job…of cleaning data and learning the basics of pandas,…or at least refreshing them.…Now, we can have some fun.…Statistics is the science of learning from data…and of reducing complex structures and trends…in the world to succinct numerical descriptions…and do powerful visualizations.…And nobody was more apt at identifying…and explaining global trends in data…than the late statistician…and public health expert Hans Rosling.…
His book Factfulness, and his website gapminder.org,…are must reads and must see for anybody…who wants to understand our complex world as it is.…And certainly for anybody learning statistics.…Throughout this chapter we will use data curated…by Rosling's organization.…Here I want to give you a preview…of the powerful visualizations that we can achieve…very simply with Python.…And we use one of gapminder datasets.…
As usual, we import some modules.…And we load the dataset with read_csv.…Let's have a look.…Here I'm selecting every 20th row, up to row 200.…
Author
Michele Vallisneri
Released
7/17/2018- Installing and setting up Python
- Importing and cleaning data
- Visualizing data
- Describing distributions and categorical variables
- Using basic statistical inference and modeling techniques
- Bayesian inference
Skill Level Intermediate
Duration
Views
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Introduction
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Welcome1m 9s
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Using the exercise files1m 2s
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1. Installation and Setup
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2. Importing and Cleaning Data
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The structure of data1m 52s
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Create tidy data tables5m 20s
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Introducing pandas7m 28s
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Data cleaning12m 6s
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3. Visualizing and Describing Data
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The power of visualization7m 12s
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Describe distributions5m 3s
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Plot distributions7m 34s
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More quantitative variables7m 58s
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Plot categorical variables4m 30s
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Personal email analytics10m 10s
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4. Introduction to Statistical Inference
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Statistical inference1m 27s
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Confidence intervals9m 30s
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Bootstrapping7m 10s
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Hypothesis testing7m 34s
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5. Introduction to Statistical Modeling
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Statistical modeling1m 35s
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Fitting models to data7m 36s
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Goodness of fit6m 13s
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Cross validation6m 22s
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Logistic regression5m 30s
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Bayesian inference9m 14s
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Conclusion
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Next steps1m 55s
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Video: The power of visualization