Final Exam: Data Visualization for Web Apps Using Python

Python 3.6+    |    Intermediate
  • 1 Video | 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

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

IN THIS COURSE

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    Data Visualization for Web Apps Using Python
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