Using Python for Research

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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.

What you will 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

Skills you learn

• Go (Programming Language)
• Python (Programming Language)
• Research

Syllabus

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.

Auto Summary

The "Using Python for Research" course is designed to bridge the gap between introductory and advanced Python programming, making it an ideal choice for those looking to apply their Python skills in research settings. This course, offered by edX, begins with a review of Python 3 basics before delving into tools commonly used in scientific research, including a new module on statistical learning. Learners will engage in a combination of guided introductions and more independent, in-depth explorations. The course features various case studies selected for their scientific diversity and their demonstration of different Python functionalities. With a duration of 30 hours, this course is suitable for anyone at the awareness level who is eager to enhance their Python skills for professional research purposes. Subscription options are available through edX's professional plans, providing access to this comprehensive and engaging learning experience.