Data Visualization in Python with seaborn and Altair Competency (Intermediate Level)

Topics covered

• access and visualize the top k data points based on a variable from a dataset
• analyze financial data using candlestick charts
• apply logistic regressions to categorical data
• create a map of the United States and plot state-specific information using markers and choropleth maps
• create a scatter chart where specific points can be selected based on a brush in another linked chart or form element
• create a scatter plot that is linked to a histogram so that the histogram visualizes all the points in the scatter plot that are selected by a brush
• create custom figure-level and axis-level strip plots
• create custom heatmaps to visualize correlation matrices
• create custom line charts visualizing time series data
• create various customized area charts such as area charts with multiple categories, streamgraphs, and trellis area charts
• create various custom scatter plots such as scatter plots where the size of the points represent a variable, scatter plots binned into categories, scatter plots with hollow points, and scatter plots with text labels
• customize pair plots with KDE curves, regression plots, and contour maps
• 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 an Isotype Visualization that represents data by default using dots and can be customized to use emojis to create a pictograph
• define violin plots such as basic violins, trellis violin plots, and violin plots with color scales
• enhance bar charts by adding rules representing the mean or median of a distribution, conditional formatting, and creating stacked bar charts
• 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
• perform various customizations on a line chart such as adding a range slider, visualizing multiple variables in a single chart, and adding a new axis for the second column
• produce Gantt charts to visualize activities, tasks, or events against time
• recognize the use of area charts as an extension of line charts to sum up all the values in a data set and create an area chart with a gradient in the color of the chart
• represent data with one dot per value using dot plots and visualize a range of data in a sequence using ranged dot plots
• use the hue, col, and row input arguments to categorize regression plots
• visualize individual data points in your dataset using strip plots
• visualize time series data using figure-level and axis-level line charts