Final Exam: Data Visualization for Web Apps Using Python

Final Exam: Data Visualization for Web Apps Using Python

• 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

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.

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

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

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.

Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

Python Statistical Plots: Visualizing & Analyzing Data Using Seaborn

• 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

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.