Python for Data Science: Advanced Data Visualization Using Seaborn
Python 3
| 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 variablesdefine the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating shareable graphsrecognize what a normal distribution is and what is defined as an outlieruse boxplots and violin plots to visualize the distributions of data within specific categories of your datasetcompare the use cases for swarm plots, bar plots strip plots, and categorical plots
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create a FacetGrid to visualize distributions within a range of categoriesconfigure a FacetGrid to convey more information and to draw one's focus to specific plotsdescribe what a color palette is and select from the built-in color palettes availableidentify the kinds of color palettes to use depending on the type of data it will representrecall different ways to visualize data within categories and identify use cases for specific aesthetic parameters
IN THIS COURSE
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1.Course Overview2m 17sUP NEXT
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2.Searching for Patterns in a Dataset8m 3s
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3.Configuring Plot Aesthetics6m 28s
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4.Normal Distribution and Outliers2m 19s
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5.Distributions Within Categories - Part 18m
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6.Distributions Within Categories - Part 25m 29s
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7.Analyzing Categories with Facet Grids - Part 15m 13s
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8.Analyzing Categories with Facet Grids - Part 27m 24s
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9.Introducing Color Palettes5m 12s
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10.Using Color Palettes7m 33s
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11.Exercise: Advanced Data Visualization Using Seaborn5m 52s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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