Overview/Description
This course introduces you to the use of Altair visualizations which can convey very detailed information for specialized datasets. You will cover some of the graphs that can be used to convey the information in very specific kinds of datasets, while also giving you some hands-on experience with advanced chart configurations. You'll begin by plotting information on a map, both to mark locations of places as well as to convey numerical information about regions. You'll then build a heatmap to analyze the numbers associated with a combination of two categorical variables. Next, you'll implement candlestick charts to visualize stock price movements, dot plots to analyze the range of movement for some values, and Gantt charts to view a project plan. Finally, you'll explore the use of window functions to analyze the top K elements in each category of your dataset.
Python with Altair: Working with Specialized Graphs
discover the key concepts covered in this course
produce world maps using data in the topo JSON format and plot points on the map by specifying the latitude and longitude coordinates
create a map of the United States and plot state-specific information using markers and choropleth maps
generate heat maps to visualize data in the form of a grid
represent data with one dot per value using dot plots and visualize a range of data in a sequence using ranged dot plots
customize various aspects of a chart such as the axis ticks, legend, and title using various functions such as configure_title() and configure_legend()
create a scatter chart where specific points can be selected based on a brush in another linked chart or form element
analyze financial data using candlestick charts
define an Isotype Visualization that represents data by default using dots and can be customized to use emojis to create a pictograph
produce Gantt charts to visualize activities, tasks, or events against time
visualize individual data points in your dataset using strip plots
access and visualize the top k data points based on a variable from a dataset