Data Visualization Literacy (Beginner Level)

Topics covered

• add up/down bars and high/low lines to a line chart, copy and paste a line chart to a new sheet, and format the line chart
• Compare categorical data by category against continuous values using bar charts
• create a histogram to visualize the frequency counts of data in bins using bars
• create various basic line charts visualizing random data using Matplotlib and pyplot
• Create various special histograms, such as a histogram visualizing multiple columns
• customize various aspects of a line chart, such as the color of the line
• identify use cases that require tables and bar charts
• illustrate how autocorrelation and cross-correlation can be used to identify recurring patterns in data through Matplotlib
• import data from a CSV file using pandas and visualize it with a basic line chart
• import data from Excel into QlikView
• list use cases and best practices for line charts and pie charts
• open up Microsoft Excel and read in data from an Excel workbook and a CSV file
• outline different presentation types and considerations before choosing a chart type for your data
• recognize the importance of data visualization in the age of big data
• recognize use cases that require histograms and box plots
• use Matplotlib to create a heatmap that visualizes correlations and has labels for each correlation
• use Matplotlib to create box-and-whisker plots to display various statistics, such as the median, upper and lower quartiles and outliers
• use Matplotlib to use correlation heatmaps to visually represent covariate relationships
• use Matplotlib to visualize the relationship between two continuous variables using scatter plots
• visualize data using built-in Excel charts, explore various column chart types, and create basic and clustered column charts