Final Exam: Data Visualization for Web Apps Using Python

Python 3.6+    |    Intermediate
  • 1 Video | 30m 32s
  • Includes Assessment
  • Earns a Badge
Final Exam: Data Visualization for Web Apps Using Python will test your knowledge and application of the topics presented throughout the Data Visualization for Web Apps Using Python track of the Skillsoft Aspire Pythonista to Python Master Journey.

WHAT YOU WILL LEARN

  • accept user input using Dash components
    apply logistic regressions to categorical data
    compare and contrast date inputs using date pickers
    compare and contrast date inputs using date pickers and strings
    compare and contrast date inputs using strings and date pickers
    configure a multi-tab Dash application
    contrast strip plots and swarm plots
    contrast swarm plots and strip plots
    create a basic bar chart with Altair
    create a callback to add interactivity to charts
    create a Dash app
    create a gauge updated using a spin button
    create a map of the United States and plot state-specific information using markers and choropleth maps
    create an HTML button to embed in apps
    create an ordinary bar chart using the Plotly Express library
    create a user input form with validation
    create Boolean toggle switches
    create custom figure-level and axis-level strip plots
    create figure-level and axis-level KDE curves
    create histograms for univariate data
    create univariate KDE curves and cumulative distributions
    create various customized area charts
    create various customized area charts such as area charts with multiple categories, streamgraphs, and trellis area charts
    customize callbacks for more complex interactivity
    customize dropdowns using multi-select
    customize histograms using the distplot() function
    customize various aspects of a chart such as the axis ticks, legend, and title using various functions
    customize various aspects of a chart such as the axis ticks, legend, and title using various functions such as configure_title() and configure_legend()
    define gauge properties
    enhance bar charts by adding rules representing the mean or median of a distribution, conditional formatting, and creating stacked bar charts
  • execute operations on time series data
    generate a variety of box plots such as plain box plots, box plots with categorical color bars, and box plots with continuous color bars
    generate heat maps to visualize data in the form of a grid
    identify attributes of a strip plot
    illustrate some of the interactive features in line charts
    implement bar charts, KDE curves, and rug plots
    implement figure-level and axis-level scatter plots
    install Dash using the pip package installer
    install the necessary Python modules to work with Seaborn
    perform operations based on user input
    perform operations on time series data
    produce basic bar charts such as bar charts with labels and bar charts with the bars sorted in an ascending or descending order
    produce Gantt charts
    produce Gantt charts to visualize activities, tasks, or events against time
    produce world maps using data in the topo JSON format
    produce world maps using data in the topo JSON format and plot points on the map by specifying the latitude and longitude coordinates
    remove limits on dataset size set by Altair
    remove limits on dataset size set by Altair by default
    represent bivariate visualizations with color coding and grouped charts
    select values using basic dropdowns
    use basic dropdowns to select values
    use the distplot() function for customizing histograms
    use the pip package installer to install Dash
    verify the correct Python version is installed on your system, run Jupyter notebook, and install Altair
    visualize bivariate histograms and KDE curves
    visualize data and identify the relationships between variables using scatter plots
    visualize data and identify the relationships between variables using scatter plots and create a scatter plot where the color of the data points represent a variable
    visualize data using grouped bar charts and stacked bar charts
    visualize data using line charts and customize various aspects of the chart such as the interpolation and by adding rules to the chart
    visualize time series data using figure-level and axis-level line charts

IN THIS COURSE

  • Playable
    1. 
    Data Visualization for Web Apps Using Python
    33s
    UP NEXT

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

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

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Likes 2 Likes 2  
Likes 0 Likes 0  
Likes 1 Likes 1