Data Analysis with Python Literacy

  • 18m
  • 18 questions
The Data Analysis with Python Literacy benchmark will measure your ability to recall and relate Python concepts, including using the NumPy library and its arrays for manipulating and analyzing data, and a basic idea of Python libraries such as pandas, Matplotlib, seaborn for data analysis. A learner who scores high on this benchmark demonstrates that they have a basic understanding of Python libraries, visualization libraries such as Matplotlib and seaborn, and basic skills for performing data analysis using NumPy and pandas.

Topics covered

  • create a pandas Series object
  • create DataFrames from dictionaries and tuples
  • create specialized NumPy arrays
  • create the tabular DataFrame object in pandas
  • create various basic line charts visualizing random data using Matplotlib and pyplot
  • define and plot the distribution of a single variable using a histogram and kernel density estimate curve
  • define a Pandas DataFrame and describe how data can be stored and accessed in these data structures
  • describe what Seaborn is and how it relates to other data science libraries in Python
  • explore the different mathematical operations available when working with arrays
  • import data from a CSV file using pandas and visualize it with a basic line chart
  • install NumPy and learn how to create basic NumPy arrays
  • load and explore the dataset used for visualization
  • load data into a DataFrame from a CSV file
  • look up data using DataFrame methods
  • perform basic operations on Series objects
  • plot pie charts, box plots, and scatter plots using Pandas
  • work with functions which apply to each element of an array
  • work with Pandas Series by accessing elements using the default and a custom index