Python scientific stack

In this course, we will learn about the NumPy library and its multidimensional array, the Pandas library and the structures it offers (Series and DataFrame), and how to efficiently plot data using the Matplotlib and Seaborn libraries, which will be especially useful for both exploratory analysis and communication of results.

Course content


  • Introduction to NumPy
  • The multidimensional array
  • Indexing and selection
  • Array sorting and concatenation
  • Array operations
  • NumPy functions
  • The numpy.random sublibrary


  • Introduction to Pandas
  • Series and DataFrames
  • Data selection
  • Operations with Pandas Structures
  • Reindexing and Multi-indexing
  • Sorting and classification
  • Functions and mapping
  • Data cleaning with Pandas
  • Grouping


  • Data visualization with matplotlib
  • Nomenclature
  • Programming interfaces
  • Figures and axes
  • Static and dynamic graphics
  • 3D graphics
  • Image management


  • Figure and axis functions
  • Distributions
  • Relationships between quantitative variables
  • Relationships between quantitative and qualitative variables
  • Statistical models
  • Heat maps
  • Visualization configuration

Duration: 12-20 hours