Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
9 Hour(s) 3 Minute(s)
Language
English
Taught by
Dr. Ediz Herkert
Rating
Course Overview
A Project-Based Course for Researchers and Engineers to Learn Scientific Problem-Solving with Python
"Python for Research and Scientific Computing" is a project-based course designed to improve your Python skills efficiently and make your research more insightful.
In this course, you learn to master powerful scientific Python tools like JupyterLab, NumPy, Matplotlib, SciPy, Pandas, and SymPy. Develop the ability to:
Implement advanced numerical techniques such as Monte Carlo simulations.
Numerically solve multidimensional and coupled differential equations.
Track and predict Brownian motion for insightful video analysis.
Estimate model parameters through optimization and curve fitting.
Conduct statistical analysis on extensive databases with millions of entries.
Design physical models with symbolic programming.
This practice-oriented course applies proven methods and best practices that will enable you to solve scientific challenges with confidence. Whether you're a professional in science, technology, engineering, or math (STEM) or an experienced researcher, you'll benefit from engaging coding projects that strengthen your problem-solving skills. Independent exercises help you to deepen your understanding and proficiency in applying Python to solve real-world scientific problems. Solutions are provided to support your progress every step of the way.
If you're a curious researcher or STEM professional with some knowledge of Python and advanced math, this course will help you apply those skills to real scientific problems. Sign up now and discover how Python can make your research more effective.
Course Content
- 6 section(s)
- 61 lecture(s)
- Section 1 Welcome
- Section 2 Introduction
- Section 3 Simulations
- Section 4 Data Analysis
- Section 5 Designing Models with Symbolic Programming
- Section 6 High-Quality Figures
What You’ll Learn
- Develop an analytical mindset and problem-solving skills to tackle research challenges using Python
- Gain proficiency in popular scientific Python packages, including NumPy, Matplotlib, SciPy, Pandas, and SymPy
- Implement advanced numerical techniques like Monte Carlo simulations
- Numerically solve multidimensional and coupled differential equations
- Track and predict Brownian motion through video analysis
- Estimate model parameters through optimization and curve fitting
- Conduct statistical analysis on extensive databases with millions of entries
- Design physical models with symbolic programming
- Acquire practical tips and tricks to create high-quality graphics using Python and Inkscape
Skills covered in this course
Reviews
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KKong MIng chow
Many useful tips to use matplotlib and scipy libraries.
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KKeval Dabhi
The explanations and instructions are follow-able and straight-forward so far. The creator didn't waste time on explaining the basic commands, which was good cause the course has per-requisite of some prior python knowledge! Good Work :) ('Thank You')
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CCraig Erdman
Good material, nicely presented. This short, fun course will add some arrows to your research and data science quiver.
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DDan Cornea
It is very well presented and goes through practical examples which is always a great way to learn. Highly recommended to anyone who needs practice with scientific computing!
