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

Python    |    Intermediate
  • 13 videos | 1h 33m 30s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 20 users Rating 4.5 of 20 users (20)
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.

WHAT YOU WILL LEARN

  • 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

IN THIS COURSE

  • 2m 45s
  • 9m 18s
    In this video, find out how to create custom figure-level and axis-level strip plots. FREE ACCESS
  • Locked
    3.  Creating Swarm Plots
    8m 51s
    In this video, learn how to compare strip plots and swarm plots. FREE ACCESS
  • Locked
    4.  Visualizing Basic Time Series Data
    7m 21s
    Learn how to visualize time series data using figure-level and axis-level line charts. FREE ACCESS
  • Locked
    5.  Performing Time Series Operations
    7m 21s
    Find out how to operate on time series data. FREE ACCESS
  • Locked
    6.  Creating Line Charts
    6m 2s
    In this video, you will create custom line charts to visualize time series data. FREE ACCESS
  • Locked
    7.  Visualizing Relationships Using Regression Plots
    8m 51s
    In this video, learn how to use the regplot() at the axis-level and lmplot() at the figure-level for regression plots. FREE ACCESS
  • Locked
    8.  Customizing Regression Plots Using Categories
    9m 29s
    Find out how to use the hue, col, and row input arguments to categorize regression plots. FREE ACCESS
  • Locked
    9.  Applying Logistic Regression
    7m 31s
    During this video, you will learn how to apply logistic regression to categorical data. FREE ACCESS
  • Locked
    10.  Creating Pair Plots
    8m 2s
    During this video, you will learn how to create pair plots to visualize relationships among multiple variables. FREE ACCESS
  • Locked
    11.  Customizing Pair Plots
    8m 56s
    In this video, you will learn how to customize pair plots with KDE curves, regression plots, and contour maps. FREE ACCESS
  • Locked
    12.  Visualizing Correlation Matrices with Heatmaps
    6m 17s
    In this video, you will create custom heatmaps to visualize correlation matrices. FREE ACCESS
  • Locked
    13.  Course Summary
    2m 48s

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

Rating 4.3 of 12 users Rating 4.3 of 12 users (12)
Rating 4.6 of 59 users Rating 4.6 of 59 users (59)
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Rating 4.6 of 328 users Rating 4.6 of 328 users (328)
Rating 4.2 of 13 users Rating 4.2 of 13 users (13)
Rating 4.5 of 275 users Rating 4.5 of 275 users (275)