Data Analysis with Python Competency (Intermediate Level)

  • 23m
  • 23 questions
The Data Analysis with Python Competency benchmark will measure your ability to recall and relate Python concepts, including 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 good Python data analysis, visualization, and data wrangling skills and can work on data analysis projects with minimal supervision.

Topics covered

  • apply a join operation on two related but dissimilar DataFrames using the merge function
  • apply the loc and iloc functions to access specific rows and columns
  • calculate deltas and percentage returns in stock prices
  • compare deep copies of arrays with views and know when to use each of them
  • configure an univariate distribution's appearance, including color, size, and the components of the plot
  • define the aesthetic parameters for a plot and make use of Seaborn's built-in templates for creating shareable graphs
  • describe and apply different techniques involved in sorting the contents of a pandas DataFrame
  • describe and apply the different techniques involved in handling datasets where some information is missing
  • describe the concept of a hierarchical index or multi-index and how it can be useful
  • distinguish between scatter plots, hexbin plots, and KDE plots
  • filter data using the loc, iloc, at, and iat functions
  • identify and deal with duplicate records
  • identify and work with time-series data
  • implement a hierarchical index and access the DataFrame's contents based on that index
  • look up data using different techniques
  • perform a regression analysis on a pair of variables in your dataset by using the Seaborn lmplot
  • perform grouping and aggregations on data
  • perform reshape operations on arrays to visualize its contents in different ways
  • recognize what a normal distribution is and what is defined as an outlier
  • retrieve specific parts of an array using row and column indices
  • specify grouping and aggregations with multiple indexes
  • summarize records into bins or categories
  • work with Seaborn to glean patterns in a dataset by visualizing the relationships between several pairs of variables