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About this courseSkip About this course
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. This version of the course includes a new module on statistical learning.
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.
At a glance
What you'll learnSkip 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
Week 1: Python Basics
Review of basic Python 3 language concepts and syntax.
Week 2: Python Research Tools
Introduction to Python modules commonly used in scientific computation, such as NumPy.
Weeks 3 & 4: Case Studies
This collection of six case studies from different disciplines provides opportunities to practice Python research skills.
Week 5: 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.
About the instructors
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