Python for Data Science: Advanced Data Visualization Using Seaborn
Python
| Intermediate
- 11 videos | 1h 3m 49s
- Includes Assessment
- Earns a Badge
Explore Seaborn, a Python library used in data science that provides an interface for drawing graphs that convey a lot of information, and are also visually appealing. To take this course, learners should be comfortable programming in Python, have some experience using Seaborn for basic plots and visualizations, and should be familiar with plotting distributions, as well as simple regression plots. You will work with continuous variables to modify plots, and to put it into a context that can be shared. Next, learn how to plot categorical variables by using box plots, violin plots, swarm plots, and FacetGrids (lattice or trellis plotting). You will learn to plot a grid of graphs for each category of your data. Learners will explore Seaborn standard aesthetic configurations, including the color palette, and style elements. Finally, this course teaches learners how to tweak displayed data to convey more information from the graphs.
WHAT YOU WILL LEARN
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Work with seaborn to glean patterns in a dataset by visualizing the relationships between several pairs of variables
Define the aesthetic parameters for a plot and make use of seaborn's built-in templates for creating shareable graphs
Recognize what a normal distribution is and what is defined as an outlier
Use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset
Compare the use cases for swarm plots, bar plots strip plots, and categorical plots -
Create a facetgrid to visualize distributions within a range of categories
Configure a facetgrid to convey more information and to draw one's focus to specific plots
Describe what a color palette is and select from the built-in color palettes available
Identify the kinds of color palettes to use depending on the type of data it will represent
Recall different ways to visualize data within categories and identify use cases for specific aesthetic parameters
IN THIS COURSE
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2m 17s
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8m 3sIn this video, you will learn how to work with Seaborn to glean patterns in a dataset by visualizing the relationships between several pairs of variables. FREE ACCESS
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3. Configuring Plot Aesthetics6m 28sLearn how to define the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating graphs that can be shared. FREE ACCESS
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4. Normal Distribution and Outliers2m 19sAfter completing this video, you will be able to recognize a normal distribution and what is defined as an outlier. FREE ACCESS
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5. Distributions Within Categories - Part 18mIn this video, you will learn how to use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset. FREE ACCESS
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6. Distributions Within Categories - Part 25m 29sLearn how to compare the use cases for swarm plots, bar plots, strip plots, and categorical plots. FREE ACCESS
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7. Analyzing Categories with Facet Grids - Part 15m 13sIn this video, you will learn how to create a FacetGrid to visualize distributions within a range of categories. FREE ACCESS
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8. Analyzing Categories with Facet Grids - Part 27m 24sDuring this video, you will learn how to configure a FacetGrid to convey more information and to draw attention to specific plots. FREE ACCESS
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9. Introducing Color Palettes5m 12sUpon completion of this video, you will be able to describe what a color palette is and select from the built-in color palettes available. FREE ACCESS
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10. Using Color Palettes7m 33sIn this video, learn how to identify the kinds of color palettes to use depending on the type of data being represented. FREE ACCESS
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11. Exercise: Advanced Data Visualization Using Seaborn5m 52sAfter completing this video, you will be able to recall different ways to visualize data within categories and identify use cases for specific aesthetic parameters. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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