Computational Physics: Scientific Programming with Python from Udemy

This course is for everyone who wants to learn and get better in Python and physics.

Except for some school mathematics, no prior knowledge is required. We will start from the basics and climb the ladder up to advanced projects.

Python is an enormously powerful tool and widely used in theoretical and computational physics.It is not difficult to use but the whole topic can be overwhelming to learn if you are on your own.

In computational physics we use numerical techniques from mathematics, such as:

Interpolation & Model fitting
Derivatives & Integrals
Differential equations
Eigenvalue problems
Monte Carlo methods

to solve problems from all areas of physics.

You are kindly invited to join this carefully prepared course that will teach you all you need to know about Python for scientific programming. It includes a crash course, quizzes, exercises, solutions and, of course, hands-on programming sessions in which we will solve real-life examples, such as

Calculating the magnetic field of a charged wire (integrals & derivatives)
Chaos & the butterfly effect (differential equations)
Heat propagation in a sample (differential equations)
Simulating (and navigating) a spaceship interacting with sun, earth and moon (differential equations)
The strange behavior of coupled oscillators (Eigenvalue problems, Fourier analysis & fitting procedure)
Ferromagnets & Antiferromagnets (Monte Carlo methods)
Special properties of graphene (Advanced science lecture about the Nobel prize winning material)
... & many more

Why me?

My name is Börge Göbel and I am a postdoc working as a scientist in theoretical physics. I have refined my advisor skills as a tutor of Bachelor, Master and PhD students in theoretical physics and have other successful courses here on Udemy.

Especially when I started my PhD, I was impressed how easily you can solve demanding tasks with Python. I have used the program for the results in many of my publications and have recommended Python to all of my students.

“Excellent course, it is just what I was looking for: everything you need to know about Python for solving physics problems from the basics. Very well structured, full of examples and applications to real problems, template files to help you follow the classes and entertaining while instructive explanations.“ - Adrián Terrones Aragón

I hope you are excited and I kindly welcome you to our course.

What's inside

Learning objectives

Getting started: a beginner-friendly crash course about numpy, functions, loops, conditionals, lists, arrays & plots
Numerical methods: derivatives & integrals, differential equations & eigenvalue problems, interpolation & monte carlo methods

Practice at physics problems: moment of inertia, magnetic field of a wire, radioactive decay, harmonic oscillators, free fall, rolling balls
Application to advanced problems: chaotic systems, heat equation, 3-body problem, spaceship mission, coupled pendulums, magnetism, graphene & quantum physics

Getting started: a beginner-friendly crash course about numpy, functions, loops, conditionals, lists, arrays & plots
Numerical methods: derivatives & integrals, differential equations & eigenvalue problems, interpolation & monte carlo methods
Practice at physics problems: moment of inertia, magnetic field of a wire, radioactive decay, harmonic oscillators, free fall, rolling balls
Application to advanced problems: chaotic systems, heat equation, 3-body problem, spaceship mission, coupled pendulums, magnetism, graphene & quantum physics

Syllabus

Python installation via Anaconda & Alternatives

Hello & Welcome!

Structure & Overview of this course

Installing Python via Anaconda for free

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses Python to solve real-world physics problems, such as calculating magnetic fields and simulating spaceship navigation, which are valuable skills for scientific research and development

Starts with a crash course in Python, covering fundamental concepts like NumPy, functions, loops, and data structures, making it accessible to learners with little to no prior programming experience

Covers numerical methods such as derivatives, integrals, differential equations, eigenvalue problems, and Monte Carlo methods, which are essential tools in computational physics

Includes hands-on programming sessions and projects that apply Python to solve problems in chaotic systems, heat equations, and the three-body problem, reinforcing theoretical concepts with practical application

Explores advanced topics like magnetism, graphene, and quantum physics, providing exposure to cutting-edge research areas and potentially inspiring further study

Utilizes Anaconda for Python installation, which is a free and open-source distribution, lowering the barrier to entry for students who may not have access to commercial software

Reviews summary

Computational physics and python programming

According to learners, this course provides a solid foundation in both Python programming and its application to computational physics problems. Many students found the course structure well-organized, progressing from basics to more advanced topics. The practical projects and hands-on coding sessions using Jupyter notebooks were frequently highlighted as particularly helpful and engaging. While the pace is generally considered appropriate, some less experienced learners found certain physics concepts or the required mathematical background challenging, suggesting it might be more suitable for those with some prior exposure to physics or calculus. Overall, the course is seen as a valuable resource for anyone looking to apply Python to scientific tasks.

Useful template files and additional resources provided.

"...template files to help you follow the classes..."

"The provided template files made it easy to follow along and practice."

"I found the additional resources and links valuable for further study."

"Having the code templates saved a lot of typing and potential errors."

"The exercise files were well-prepared."

Instructor provides clear and instructive explanations.

"...entertaining while instructive explanations."

"The instructor explains concepts clearly and is easy to follow."

"I found the explanations of both the physics and the code to be very helpful."

"His teaching style makes complex ideas understandable."

"The explanations for the numerical methods were particularly clear."

Logical progression of topics from simple to complex.

"Very well structured, full of examples and applications to real problems..."

"The course progresses logically from Python basics to complex physics problems."

"I appreciated the clear organization of the syllabus and modules."

"The step-by-step approach made potentially difficult topics more manageable."

"The structure helps build understanding progressively."

Provides a solid base in Python for scientific use.

"Excellent course, it is just what I was looking for: everything you need to know about Python for solving physics problems from the basics."

"This course provided me with a strong foundation in using Python for scientific computing."

"I found the initial crash course on NumPy and basic programming very helpful."

"It covers the essential numerical methods needed for physics simulations."

"The sections on functions, loops, and data handling built a necessary base."

Excellent hands-on coding and relevant projects.

"Very well structured, full of examples and applications to real problems..."

"The hands-on programming sessions in which we will solve real-life examples are the best part."

"The projects like the magnetic field calculation and simulating orbits were very engaging and practical."

"I gained valuable experience applying numerical methods to actual physics scenarios."

"It was great to see how the concepts translated into working code for physical problems."

May require more than basic math/physics background.

"Except for some school mathematics, no prior knowledge is required. We will start from the basics..."

"While it says no prior knowledge is required, some familiarity with calculus and physics is definitely helpful."

"I struggled with some of the physics concepts underlying the programming examples."

"Beginners might find the leap from basic Python to the physics applications a bit steep."

"Understanding the mathematical methods (like differential equations) is key."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Computational Physics: Scientific Programming with Python with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, which are essential for solving eigenvalue problems and understanding matrix operations in computational physics.

Browse courses on Linear Algebra

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Review matrix operations such as addition, subtraction, and multiplication.
Practice solving systems of linear equations.
Understand eigenvalues and eigenvectors and their significance.

Read 'Computational Physics' by Mark Newman

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Supplement the course material with a dedicated textbook on computational physics, providing a broader and deeper understanding of the subject.

View Computational Physics on Amazon

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Read chapters that align with the course syllabus.
Work through the examples and exercises in the book.
Compare the book's approach to the topics with the course's approach.

Read 'Numerical Recipes in C'

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Gain a deeper understanding of numerical methods by studying a classic text that covers the algorithms in detail.

View Numerical Recipes in C++ on Amazon

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Select chapters relevant to the course syllabus, such as integration and differential equations.
Read the selected chapters and understand the algorithms presented.
Compare the algorithms in the book with the Python implementations used in the course.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Follow SciPy Tutorials on Numerical Integration

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Enhance your skills in numerical integration by following the official SciPy tutorials, which provide practical examples and best practices.

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Visit the SciPy website and find the tutorials on numerical integration.
Work through the tutorials, paying attention to the different integration methods available.
Experiment with different parameters and functions to see how they affect the results.

Create a Blog Post on a Computational Physics Topic

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Reinforce your understanding of a specific topic by explaining it in a blog post format, targeting an audience with some physics background.

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Choose a specific topic from the course, such as Monte Carlo methods.
Research the topic and gather relevant information.
Write a blog post explaining the topic in a clear and concise manner.
Include examples and visualizations to illustrate the concepts.
Proofread and edit the blog post before publishing.

Implement Numerical Methods from Scratch

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Solidify your understanding of numerical methods by implementing them from scratch in Python, without relying on pre-built libraries.

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Choose a numerical method, such as the trapezoidal rule for integration.
Write Python code to implement the chosen method.
Test your implementation with known functions and compare the results to analytical solutions.
Refactor your code to improve readability and efficiency.

Simulate a Physical System

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Apply your knowledge of computational physics to simulate a real-world physical system, such as a pendulum or a projectile motion.

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Choose a physical system to simulate.
Develop a mathematical model of the system.
Write Python code to simulate the system using numerical methods.
Visualize the results of the simulation using plots.
Analyze the simulation results and draw conclusions.

Career center

Learners who complete Computational Physics: Scientific Programming with Python will develop knowledge and skills that may be useful to these careers:

Computational Physicist

A computational physicist uses computational methods to solve complex physics problems. If you want to become a computational physicist, this course is directly relevant to your career aspirations. The course focuses on using Python for scientific programming and covers essential numerical techniques used in computational physics. The hands-on programming sessions, which include examples like calculating the magnetic field of a charged wire and simulating a spaceship's interaction with celestial bodies, directly mirror the type of work this computational physicist performs. Furthermore, the coverage of advanced topics like graphene's properties could be directly applicable to research in materials science.

See salaries and explore the career path for Computational Physicist

Data Scientist

A data scientist analyzes complex data to extract meaningful insights and develop data-driven solutions. This course helps build a foundation in the crucial programming skills needed for data analysis and modeling. Data scientists often use tools to perform numerical calculations, and this course specifically addresses numerical techniques from mathematics, such as interpolation, model fitting, derivatives, integrals, differential equations, eigenvalue problems, and Monte Carlo methods. The course helps prospective data scientists by applying these techniques to solving real-life physics examples, which helps to reinforce this data scientist's understanding of the material.

See salaries and explore the career path for Data Scientist

Simulation Engineer

Simulation engineers create and use computer simulations to model and analyze complex systems. This course can be extremely valuable for simulation engineers by providing them with the necessary Python programming skills and a solid understanding of numerical methods. The course covers techniques such as differential equations and Monte Carlo methods, which are frequently used in simulations. The hands-on programming sessions, including simulating a spaceship's motion, provide practical experience which is directly applicable to simulation projects. Simulation engineers will be able to apply these skills and knowledge to improve the accuracy and efficiency of their simulations.

See salaries and explore the career path for Simulation Engineer

Research Engineer

Research engineers apply scientific and engineering principles to design and conduct research. This course helps research engineers by providing them with the necessary Python programming skills and a solid understanding of numerical methods. It covers essential techniques such as derivatives, integrals, differential equations, and Monte Carlo methods, which are widely used in engineering research. The hands-on programming sessions, including examples like simulating heat propagation in a sample, provide practical experience. Research engineers will be able to apply these skills and knowledge to improve the efficiency and accuracy of their research projects.

See salaries and explore the career path for Research Engineer

Machine Learning Engineer

A machine learning engineer develops and deploys machine learning models. This course helps machine learning engineers by strengthening their proficiency in Python and numerical methods. The course encompasses essential mathematical techniques used in machine learning, such as interpolation, model fitting, and Monte Carlo methods. These techniques are essential for developing and optimizing machine learning algorithms. Furthermore, the hands-on programming sessions help you apply these techniques to solving real-world problems, which reinforces familiarity with the material. This course is particularly helpful for engineers who need to implement numerical solutions in their machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Research Scientist

Research scientists design and conduct experiments to investigate scientific phenomena. This course may significantly benefit you by establishing a firm base in Python and numerical methods. The syllabus covers a wide range of mathematical techniques vital for data analysis and modeling, such as interpolation, derivatives, integrals, and differential equations. Moreover, the course emphasizes hands-on programming sessions, which allow you to solve real-world problems and reinforce the concepts learned. By applying these skills, a research scientist can improve efficiency and accuracy in their scientific work.

See salaries and explore the career path for Research Scientist

Robotics Engineer

Robotics engineers design, develop, and test robots and robotic systems. This course helps robotics engineers by giving them a firm base in Python and numerical methods. The course helps those pursuing robotics engineering by strengthening their knowledge of techniques such as differential equations and eigenvalue problems, which are used in robotics simulations. The hands-on programming sessions, including simulating a spaceship's interaction with celestial bodies, offer practical experience which supports projects in robotics.

See salaries and explore the career path for Robotics Engineer

Quantitative Analyst

A quantitative analyst, often working in the finance industry, develops and implements mathematical models for financial analysis and risk management. This course can be extremely helpful for someone aiming to be a quantitative analyst, by equipping them with strong Python programming skills and a solid understanding of numerical methods. The course covers essential mathematical techniques used in quantitative finance, such as interpolation, derivatives, integrals, differential equations, and Monte Carlo methods. The practical programming exercises, such as calculating the magnetic field of a charged wire, help you in developing your problem-solving skills.

See salaries and explore the career path for Quantitative Analyst

Data Analyst

A data analyst collects, processes, and analyzes data to provide insights and support decision-making. This course helps data analysts by teaching them Python and many useful numerical methods. Data analysts benefit from solid data manipulation skills in Python, which they can obtain from this course. The course covers basic programming concepts, data structures, and fundamental numerical analysis, which are essential for data analysis. Furthermore, the hands-on programming sessions enable the data analyst to apply these concepts to solve real-world problems, like analyzing a periodic signal via Fourier transforms.

See salaries and explore the career path for Data Analyst

Financial Modeler

Financial modelers create mathematical models to forecast financial performance and analyze investment opportunities. This course may be useful for financial modelers, by giving them a deeper understanding of computational methods. The course covers key techniques such as interpolation, fitting, derivatives, and Monte Carlo methods, which are used in financial modeling. The hands-on programming sessions, including model fitting, provide practical experience. Financial modelers can then apply these skills to improve accuracy and efficiency in their financial models.

See salaries and explore the career path for Financial Modeler

Software Developer

Software developers design, develop, and test software applications. This course can greatly benefit software developers by improving their proficiency in Python and numerical methods. The course covers fundamental programming concepts, data structures, and algorithms, which are essential for software development. Moreover, the hands-on programming sessions let you apply these concepts to solving real-world problems, such as simulating a spaceship's interaction with celestial bodies. These experiences equip the software developer with practical skills that can be applied to a wide range of software development projects.

See salaries and explore the career path for Software Developer

Biostatistician

Biostatisticians apply statistical methods to biological and health-related data. This course may be helpful for biostatisticians, by sharpening their Python programming skills and knowledge of numerical methods. The course covers essential techniques like interpolation, model fitting, and Monte Carlo methods, which are useful in many statistical analyses. The hands-on programming sessions, including fitting a polynomial model function, offer practical experience. Biostatisticians can apply these skills to analyze biological data and improve the accuracy of their statistical models.

See salaries and explore the career path for Biostatistician

Physics Teacher

A physics teacher educates students in the principles of physics. This course may be useful for physics teachers, by providing them with a deeper understanding of computational physics techniques that they can integrate into their teaching. Although prior experience is not required, the course covers fundamental concepts in numerical methods and Python programming, which can be used by physics teachers to enhance their lectures. The course also includes hands-on programming sessions with real-world examples, such as calculating the magnetic field of a charged wire, which can be adapted into engaging classroom activities.

See salaries and explore the career path for Physics Teacher

Acoustic Engineer

Acoustic engineers analyze and design systems to control noise and vibration. This course may be useful for acoustic engineers by improving their proficiency in Python and numerical methods. The course covers important techniques such as Fourier transforms and differential equations, which are used in acoustic analysis and modeling. The hands-on programming sessions, including analyzing a periodic signal via Fourier transforms, offer practical experience. Acoustic engineers can apply these skills to analyze and design effective noise control solutions.

See salaries and explore the career path for Acoustic Engineer

Image Processing Specialist

An image processing specialist develops algorithms and systems for processing and analyzing digital images. This course may be useful for image processing specialists, by improving their proficiency in Python and numerical methods. The course covers essential techniques, such as Fourier analysis and interpolation, which are used in image processing. The hands-on programming sessions, including contour plots, give practical experience. Image processing specialists can apply these skills to create and improve their image processing algorithms.

See salaries and explore the career path for Image Processing Specialist

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Computational Physics: Scientific Programming with Python.

Computational Physics

Save

Comprehensive introduction to computational physics, covering a wide range of topics relevant to the course. It provides clear explanations of numerical methods and their applications to physics problems. The book includes numerous examples and exercises that can help you solidify your understanding of the material. It is particularly useful for understanding the practical aspects of applying computational techniques to solve physics problems.

Computational Physics

Paperback

Numerical Recipes in C++

Save

Provides a comprehensive overview of numerical methods, including those used in computational physics. It covers topics such as interpolation, integration, and differential equations. While the examples are in C, the algorithms are easily adaptable to Python. It serves as a valuable reference for understanding the underlying principles behind the numerical techniques used in the course.

Numerical Recipes in C++

Paperback

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Numerical Recipes in C++

Kindle Edition

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