Python for Research and Scientific Computing

Name: Python for Research and Scientific Computing
Rating: 4.5 (57 reviews)

A Project-Based Course for Researchers and Engineers to Learn Scientific Problem-Solving with Python

Created byDr. Ediz Herkert

Last updated 7/2025

English

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

Course content

6 sections • 61 lectures • 9h 3m total length

Welcome!1:44

Monte Carlo Method - Part 13:07
Monte Carlo Method - Part 26:13
Monte Carlo Method - Part 36:45
Monte Carlo Method - Part 49:50
Monte Carlo Integration - Part 11:51
Monte Carlo Integration - Part 28:14
Monte Carlo Integration - Part 35:30
Monte Carlo Integration - Part 412:48
Harmonic Oscillator - Part 111:32
Harmonic Oscillator - Part 27:25
Harmonic Oscillator - Part 38:55
Satellite Orbit - Part 110:19
Satellite Orbit - Part 29:17
Satellite Orbit - Part 35:42
Satellite Orbit - Part 412:14
Brownian Motion - Part 16:08
Brownian Motion - Part 27:50
Brownian Motion - Part 35:55
Brownian Motion - Part 412:05

Particle Tracking - Part 16:57
Particle Tracking - Part 24:51
Particle Tracking - Part 311:11
Particle Tracking - Part 48:48
Parameter Estimation - Part 18:32
Parameter Estimation - Part 215:59
Parameter Estimation - Part 36:15
Parameter Estimation - Part 411:16
Parameter Estimation - Part 59:38
Parameter Estimation - Part 62:30
Statistics - Part 19:47
Statistics - Part 26:26
Statistics - Part 311:30
Statistics - Part 49:09
Statistics - Part 56:28
Statistics - Part 610:31
Demonstrates merging dataframes on beer style, computing an adjusted rating with mean and standard deviation, and extracting top and bottom beers using pandas.

The Basics of SymPy - Part 117:54
Modeling Physical Systems with SymPy - Part 15:52
Modeling Physical Systems with SymPy - Part 225:33
Modeling Physical Systems with SymPy - Part 321:10
Optimal Experimental Design - Part 13:05
Optimal Experimental Design - Part 214:34
Learn how Beer Lambert law models light attenuation in ethanol, estimate concentration from optical measurements, and explore how measurement positions affect parameter accuracy through curve fitting and noise.
Optimal Experimental Design - Part 39:08
Optimal Experimental Design - Part 49:54
Optimal Experimental Design - Part 517:12
Optimal Experimental Design - Part 613:12

Colors and Colormaps - Part 17:57
Colors and Colormaps - Part 29:05
Colors and Colormaps - Part 38:01
Colors and Colormaps - Part 45:15
Creating Custom Styles - Part 14:22
Creating Custom Styles - Part 25:36
Creating Custom Styles - Part 37:12
Creating Custom Styles - Part 49:26
Figure Export and Post-Processing - Part 17:26
Figure Export and Post-Processing - Part 210:09
Figure Export and Post-Processing - Part 37:51

Requirements

Basic knowledge of Python syntax and types
Understanding of university-level mathematics
Prior experience in scientific research or a science, technology, engineering, and math (STEM) field

Description

"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.

Who this course is for:

Scientists, researchers, and professionals in STEM fields who want to improve their Python skills specifically for scientific applications
Students or graduates in scientific disciplines seeking to strengthen their programming abilities and streamline their research workflows
Professionals in industries such as data analysis, engineering, and technology who want to apply Python to solve scientific problems
Anyone with a strong interest in scientific research and a desire to use Python as a powerful tool in their field

Python for Research and Scientific Computing

What you'll learn

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Course content

Welcome1 lecture • 2min

Introduction4 lectures • 30min

Simulations19 lectures • 2hr 32min

Data Analysis16 lectures • 2hr 20min

Designing Models with Symbolic Programming10 lectures • 2hr 18min

High-Quality Figures11 lectures • 1hr 22min

Requirements

Description

Who this course is for: