Python for Research and Scientific Computing

Python for Research and Scientific Computing, available at $49.99, has an average rating of 4.7, with 51 lectures, based on 26 reviews, and has 226 subscribers.

You will learn about 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, and Pandas Implement advanced numerical techniques like Monte Carlo simulations Numerically solve multidimensional and coupled differential equations Track and predict Brownian motion for insightful analysis Estimate model parameters through optimization and curve fitting Conduct statistical analysis on extensive databases with millions of entries Acquire practical tips and tricks to create high-quality graphics using Python and Inkscape This course is ideal for individuals who are Scientists, researchers, and professionals in STEM fields who want to improve their Python skills specifically for scientific applications or Students or graduates in scientific disciplines seeking to strengthen their programming abilities and streamline their research workflows or Professionals in industries such as data analysis, engineering, and technology who want to apply Python to solve scientific problems or Anyone with a strong interest in scientific research and a desire to use Python as a powerful tool in their field It is particularly useful for Scientists, researchers, and professionals in STEM fields who want to improve their Python skills specifically for scientific applications or Students or graduates in scientific disciplines seeking to strengthen their programming abilities and streamline their research workflows or Professionals in industries such as data analysis, engineering, and technology who want to apply Python to solve scientific problems or Anyone with a strong interest in scientific research and a desire to use Python as a powerful tool in their field.

Enroll now: Python for Research and Scientific Computing

Summary

Title: Python for Research and Scientific Computing

Price: $49.99

Average Rating: 4.7

Number of Lectures: 51

Number of Published Lectures: 51

Number of Curriculum Items: 51

Number of Published Curriculum Objects: 51

Original Price: €99.99

Quality Status: approved

Status: Live

What You Will 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, and Pandas
Implement advanced numerical techniques like Monte Carlo simulations
Numerically solve multidimensional and coupled differential equations
Track and predict Brownian motion for insightful analysis
Estimate model parameters through optimization and curve fitting
Conduct statistical analysis on extensive databases with millions of entries
Acquire practical tips and tricks to create high-quality graphics using Python and Inkscape

Who Should Attend

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

Target Audiences

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

Improve your research with “Python for Research and Scientific Computing” – a time-efficient course designed to enhance your Python skills and streamline your research process.

Discover the power of Python as you learn to master essential tools and popular scientific packages like JupyterLab, NumPy, Matplotlib, SciPy, and Pandas. 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 analysis.
Estimate model parameters through optimization and curve fitting.
Conduct statistical analysis on extensive databases with millions of entries.

This practice-oriented course applies proven methods and best practices that will enable you to master scientific challenges with confidence. Whether you’re an experienced researcher or STEM professional, you’ll benefit from hands-on coding projects and engaging tasks that strengthen your problem-solving skills. Explore independent exercises 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 a STEM professional with a foundation in advanced math and Python, this course enables you to get the most out of Python for your research projects. Sign up now to learn more about the power of Python for impactful scientific research.

Course Curriculum

Chapter 1: Welcome

Lecture 1: Welcome!

Chapter 2: Introduction

Lecture 1: Installing Anaconda

Lecture 2: Coding With Spyder

Lecture 3: Coding With Notepad++

Lecture 4: Coding With JupyterLab

Chapter 3: Building Simulations

Lecture 1: Monte Carlo Method – Pt. 1

Lecture 2: Monte Carlo Method – Pt. 2

Lecture 3: Monte Carlo Method – Pt. 3

Lecture 4: Monte Carlo Method – Pt. 4

Lecture 5: Monte Carlo Integration – Pt. 1

Lecture 6: Monte Carlo Integration – Pt. 2

Lecture 7: Monte Carlo Integration – Pt. 3

Lecture 8: Monte Carlo Integration – Pt. 4

Lecture 9: Harmonic Oscillator – Pt. 1

Lecture 10: Harmonic Oscillator – Pt. 2

Lecture 11: Harmonic Oscillator – Pt. 3

Lecture 12: Satellite Orbit – Pt. 1

Lecture 13: Satellite Orbit – Pt. 2

Lecture 14: Satellite Orbit – Pt. 3

Lecture 15: Satellite Orbit – Pt. 4

Lecture 16: Brownian Motion – Pt. 1

Lecture 17: Brownian Motion – Pt. 2

Lecture 18: Brownian Motion – Pt. 3

Lecture 19: Brownian Motion – Pt. 4

Chapter 4: Analyzing Data

Lecture 1: Particle Tracking – Pt. 1

Lecture 2: Particle Tracking – Pt. 2

Lecture 3: Particle Tracking – Pt. 3

Lecture 4: Particle Tracking – Pt. 4

Lecture 5: Parameter Estimation – Pt. 1

Lecture 6: Parameter Estimation – Pt. 2

Lecture 7: Parameter Estimation – Pt. 3

Lecture 8: Parameter Estimation – Pt. 4

Lecture 9: Parameter Estimation – Pt. 5

Lecture 10: Parameter Estimation – Pt. 6

Lecture 11: Statistics – Pt. 1

Lecture 12: Statistics – Pt. 2

Lecture 13: Statistics – Pt. 3

Lecture 14: Statistics – Pt. 4

Lecture 15: Statistics – Pt. 5

Lecture 16: Statistics – Pt. 6

Chapter 5: High-Quality Figures

Lecture 1: Colors & Colormaps – Pt. 1

Lecture 2: Colors & Colormaps – Pt. 2

Lecture 3: Colors & Colormaps – Pt. 3

Lecture 4: Colors & Colormaps – Pt. 4

Lecture 5: Creating Custom Styles – Pt. 1

Lecture 6: Creating Custom Styles – Pt. 2

Lecture 7: Creating Custom Styles – Pt. 3

Lecture 8: Creating Custom Styles – Pt. 4

Lecture 9: Figure Export & Post-Processing – Pt. 1

Lecture 10: Figure Export & Post-Processing – Pt. 2

Lecture 11: Figure Export & Post-Processing – Pt. 3

Instructors

Ediz Herkert
Stay curious!

Rating Distribution

1 stars: 0 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 6 votes
5 stars: 20 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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