Python II

(aka Python Tsunami Part II)

A 3-day workshop on how to perform data analysis in the Python coding language.

Content

The course will familiarize participants with the use of python as a basic data science tool, with a focus on the scikit-learn library. It combines exercises on data modelling tasks with brief theoretical introductions to the different types of models (supervised and unsupervised), as well as advanced visualization with the matplotlib and seaborn libraries.

The course will further cover advanced features of the python language such as objects, classes, and user-defined functions, which are elementary for expert use of python.

Lastly, participants will be introduced to best practices for testing and documenting python code.

Requirements

This course builds on Python Part 1 and familiarity with the basic operations of python is a requirement.

Learning Outcomes

A student who has met the objectives of the course will be able to: 

• Understand and use advanced concepts of the python language such as user-defined functions, classes and modules
• Perform modelling with the data science library scikit-learn
• Explain the use of supervised and unsupervised learning techniques and how to choose a model
• Create customized visualizations with the seaborn and matplotlib libraries
• Test and document python code