Data Visualization with Python Competency (Intermediate Level)

  • 16m
  • 16 questions
The Data Visualization with Python competency benchmark will measure your ability to recall and relate underlying data visualization concepts in Python. You will be evaluated on your ability to recognize the concepts of data visualization and advanced data visualization, as well as data representation, charting, and plotting in Python using pandas, Matplotlib, and Plotly libraries. A learner who scores high on this benchmark demonstrates that they have data visualization skills using Python.

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