Using Python for Research, Course from Harvard
| Learning Experience | 7.4 |
|---|
Take your introductory knowledge of Python 3 programming to the next level and learn how to use commonly implemented tools in ‘Python for research’ course.
Introduction
Take your introductory knowledge of Python 3 programming to the next level and learn how to use Python for research.
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.
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
Prerequisites
- Some previous Python programming experience (in any version)
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 programming skills in research.
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.
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Description
Introduction
Take your introductory knowledge of Python 3 programming to the next level and learn how to use Python for research.
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.
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
Prerequisites
- Some previous Python programming experience (in any version)
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 programming skills in research.
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.
Note: Your review matters
If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.
FAQ
Specification:
- EDX
- Harvard University
- Online Course
- Self-paced
- Intermediate
- 1-3 Months
- Free Course (Affordable Certificate)
- English
- Computer programming Research methodology
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