Data Visualization with Python Literacy

  • 13m
  • 13 questions
The Data Visualization with Python Literacy benchmark will measure your ability to recall and relate the underlying data visualization concepts in Python. You will be evaluated on your ability to recognize the foundational concepts of data visualization, representation, charting, and plotting in Python using libraries such as Matplotlib, Plotly, and Seaborn. A learner who scores high on this benchmark demonstrates that they have basic data visualization skills using Python.

Topics covered

  • 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 and configure simple graphs with lines and markers using the Matplotlib data visualization library
  • create various basic line charts visualizing random data using Matplotlib and pyplot
  • Create various special histograms, such as a histogram visualizing multiple columns
  • describe what Seaborn is and how it relates to other data science libraries in Python
  • generate histograms and pie charts to analyze distributions and create scatter plots to plot the relationship between two variables in a dataset
  • list libraries that can be used in Python to implement data visualization
  • load and explore the dataset used for visualization
  • plot pie charts, box plots, and scatter plots using Pandas
  • 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