Course details

Python for Data Science: Advanced Data Visualization Using Seaborn

Python for Data Science: Advanced Data Visualization Using Seaborn


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description

This 11-video course explores Seaborn, a Python library used in data science to provide a high-level interface for drawing graphs that conveys both a lot of information, and are visually appealing. Seaborn also provides support for other data analysis and statistical libraries, such as SciPy and StatsModels. 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.



Expected Duration (hours)
1.1

Lesson Objectives

Python for Data Science: Advanced Data Visualization Using Seaborn

  • Course Overview
  • 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
  • Course Number:
    it_dspydsdj_06_enus

    Expertise Level
    Intermediate