SKILL BENCHMARK

Data Analysis with Python Proficiency

  • 22m
  • 22 questions
The Data Analysis with Python Competency benchmark will measure your ability to recall and relate Python concepts, including using NumPy and pandas for manipulating, analyzing, and transforming the data, as well as Matplotlib and seaborn for visualizing data. A learner who scores high on this benchmark demonstrates that they have very good Python data analysis, visualization, and data wrangling skills and can work independently on data analysis projects.

Topics covered

  • apply a multi-index to a DataFrame and reshape it using the stack and melt operations
  • apply regular expressions and other advanced techniques to select and drop columns
  • clean missing data in mismatched DataFrames
  • compare the use cases for swarm plots, bar plots strip plots, and categorical plots
  • configure a FacetGrid to convey more information and to draw one's focus to specific plots
  • create a FacetGrid to visualize distributions within a range of categories
  • describe how operations can be performed between arrays of mismatched shapes using broadcasting
  • identify kinds of masking operations
  • implement joins using the join() method
  • load data into a Pandas DataFrame from a table in a relational database
  • manipulate and analyze time series data
  • parse and manipulate datetime values
  • perform advanced manipulations on string data
  • perform conditional filtering using the query function
  • perform filtering operations on categorical data
  • perform operations between arrays of mismatched shapes by applying broadcasting rules
  • recognize default and custom indexes and reindex DataFrames
  • transform data with user-defined functions
  • troubleshoot data with duplicates
  • use boxplots and violin plots to visualize the distributions of data within specific categories of your dataset
  • use pivot tables to explore data
  • use pivot tables to summarize data