Data Visualization with Python Competency (Intermediate Level)

Topics covered

• analyze the relationship between two variables by plotting a bivariate distribution
• clean missing data in mismatched DataFrames
• create bar and lollipop charts that visualize multiple related variables in one chart
• customize various aspects of a line chart, such as the color of the line
• define and plot the distribution of a single variable using a histogram and kernel density estimate curve
• distinguish between scatter plots, hexbin plots, and KDE plots
• identify and work with time-series data
• import data from a CSV file using pandas and visualize it with a basic line chart
• list the prominent data visualization libraries that can be used with Matplotlib
• perform a regression analysis on a pair of variables in your dataset by using the Seaborn lmplot
• recognize criteria that should be considered when selecting an appropriate data visualization library
• recognize what a normal distribution is and what is defined as an outlier
• use Matplotlib to create exploded pie charts and treemaps
• use Matplotlib to visualize how individual proportions add up to a whole using pie charts
• use the Seaborn pair plot to generate a grid to plot the relationship between multiple pairs of variables in your dataset
• work with Seaborn to glean patterns in a dataset by visualizing the relationships between several pairs of variables