Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

Python    |    Intermediate
  • 17 videos | 1h 46m 42s
  • Includes Assessment
  • Earns a Badge
Rating 4.2 of 13 users Rating 4.2 of 13 users (13)
The wealth of Python data visualization libraries makes it hard to decide the best choice for each use case. However, if you're looking for statistical plots that are easy to build and visually appealing, Seaborn is the obvious choice. You'll begin this course by using Seaborn to construct simple univariate histograms and use kernel density estimation, or KDE, to visualize the probability distribution of your data. You'll then work with bivariate histograms and KDE curves. Next, you'll use box plots to concisely represent the median and the inter-quartile range (IQR) and define outliers in data. You'll work with boxen plots, which are conceptually similar to box plots but employ percentile markers rather than whiskers. Finally, you'll use Violin plots to represent the entire probability density function, obtained via a KDE estimation, for your data.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Install the necessary python modules to work with seaborn
    Create histograms for univariate data
    Use the distplot() function for customizing histograms
    Create figure-level and axis-level kde curves
    Implement bar charts, kde curves, and rug plots
    Represent bivariate visualizations with color coding and grouped charts
    Create univariate kde curves and cumulative distributions
    Visualize bivariate histograms and kde curves
  • Customize joint plots using histograms, kde curves, hexbin, and regression charts
    Implement figure-level and axis-level scatter plots
    Customize scatter plots with multiple variables and visualize categorical data
    Use the catplot and boxplot functions to create box and whisker plots
    Contrast box plots and boxen plots
    Use the figure-level catplot() and axis-level violinplot()
    Customize violin plots using hue and bandwidth
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 41s
  • 5m 6s
    In this video, you will learn how to install the necessary Python modules to work with Seaborn. FREE ACCESS
  • Locked
    3.  Visualizing Univariate Data
    7m 40s
    Learn how to create histograms for data with one variable. FREE ACCESS
  • Locked
    4.  Representing Data Using Histograms
    5m 23s
    Learn how to use the distplot() function to customize histograms. FREE ACCESS
  • Locked
    5.  Creating KDE Curves
    6m 33s
    In this video, find out how to create KDE curves at the figure level and the axis level. FREE ACCESS
  • Locked
    6.  Creating Univariate Plots
    7m 13s
    Learn how to create bar charts, KDE curves, and rug plots. FREE ACCESS
  • Locked
    7.  Representing Data Using Bivariate Visualizations
    7m 10s
    In this video, you will learn how to represent bivariate visualizations with color coding and grouped charts. FREE ACCESS
  • Locked
    8.  Creating KDE Curves and Cumulative Distributions
    5m 16s
    In this video, you will learn how to create univariate KDE curves and cumulative distributions. FREE ACCESS
  • Locked
    9.  Visualizing Data Using Bivariate Histograms
    7m 42s
    In this video, you will learn how to visualize bivariate histograms and KDE curves. FREE ACCESS
  • Locked
    10.  Understanding and Implementing Joint Plots
    6m 16s
    In this video, you will learn how to customize joint plots using histograms, KDE curves, hexbin, and regression charts. FREE ACCESS
  • Locked
    11.  Understanding and Implementing Scatter Plots
    6m 47s
    In this video, you will learn how to create figure-level and axis-level scatter plots. FREE ACCESS
  • Locked
    12.  Customizing Scatter Plots with Multiple Variables
    5m 48s
    In this video, find out how to customize scatter plots with multiple variables and visualize categorical data. FREE ACCESS
  • Locked
    13.  Creating Box Plots
    7m 43s
    Learn how to use the catplot and boxplot functions to create box plots and whisker plots. FREE ACCESS
  • Locked
    14.  Understanding and Implementing Boxen Plots in Seaborn
    5m 14s
    In this video, you will compare and contrast box plots and boxen plots. FREE ACCESS
  • Locked
    15.  Representing Data Using Violin Plots
    7m 47s
    Learn how to use the figure-level catplot() and the axis-level violinplot(). FREE ACCESS
  • Locked
    16.  Customizing Custom Violin Plots
    9m 45s
    In this video, you will learn how to customize violin plots using hue and bandwidth. FREE ACCESS
  • Locked
    17.  Course Summary
    2m 37s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Course Data Science Statistics: Using Python to Compute & Visualize Statistics
Rating 4.6 of 62 users Rating 4.6 of 62 users (62)
Book Python 3 Data Visualization Using ChatGPT /GPT-4
Book Python 3 and Data Visualization

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Course Dash Python Framework: Creating Widgets in Dash Apps
Rating 4.3 of 7 users Rating 4.3 of 7 users (7)
Course Python Development: Getting Started with Programming in Python
Rating 4.6 of 2165 users Rating 4.6 of 2165 users (2165)
Course Getting Started with Python: Introduction
Rating 4.6 of 4247 users Rating 4.6 of 4247 users (4247)