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SkillSoft Explore Course

IT Skills     Software Design and Development     Python     Statistical Plots with Seaborn

Seaborn's smartly designed interface lets you illuminate data through aesthetically pleasing statistical graphics that are incredibly easy to build. In this course, you'll discover Seaborn's capabilities.


You'll begin using strip plots and swarm plots and recognizing how they work together using low-intensity noise. You'll then work with time series data through various techniques, like resampling data at different time frequencies and plotting with confidence intervals and other types of error bars. Next, you'll visualize both logistic and linear regression curves.


Moving on, you'll use the pairplot function to visualize the relationships between columns in your data, taken two at a time, in a grid format. You'll change the chart type being visualized and create pair plots with multiple chart types in each plot. Lastly, you'll create and format a heatmap of a correlation matrix to identify relationships between dataset columns.



Objectives

Python Statistical Plots: Time Series Data & Regression Analysis in Seaborn

  • discover the key concepts covered in this course
  • create custom figure-level and axis-level strip plots
  • contrast strip plots and swarm plots
  • visualize time series data using figure-level and axis-level line charts
  • perform operations on time series data
  • create custom line charts visualizing time series data
  • use the axis-level regplot() and figure-level lmplot() for regression plots
  • use the hue, col, and row input arguments to categorize regression plots
  • apply logistic regressions to categorical data
  • create pair plots to visualize multivariate relationships
  • customize pair plots with KDE curves, regression plots, and contour maps
  • create custom heatmaps to visualize correlation matrices
  • summarize the key concepts covered in this course