What you'll learn

  • Python 3 programming basics (a review)
  • Python tools (e.g., NumPy and SciPy modules) for research applications
  • How to apply Python research tools in practical settings

Course description

This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects. In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings.

Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.

This run of the course includes revised assessments and a new module on machine learning.

Course outline

  • Python Basics

    Review of basic Python 3 language concepts and syntax.

  • Python Research Tools 

    Introduction to Python modules commonly used in scientific computation, such as NumPy.

  • Case Studies

    This collection of six case studies from different disciplines provides opportunities to practice Python research skills.

  • Statistical Learning

    Exploration of statistical learning using the scikit-learn library followed by a two-part case study that allows you to further practice your coding skills.


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