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Course Description:
Welcome to the self-paced “Scientific Computing with Python” training course! This comprehensive program is designed for science and engineering students, professors, research scholars, and working professionals who want to enhance their scientific computing skills using Python. This is the first part of the scientific computing course which is focused more on the basics so that anyone new to programming can easily understand. A more advanced version will be the next step.
In this course, we will cover all the major topics in Python from a scientific computing perspective. We will start with an introduction to Python, exploring its applications in scientific computing and learning how to use Python as an advanced calculator in an interactive mode. You will gain hands-on experience working with strings, creating and saving Python programs, and understanding the basics of variables.
Data structures play a crucial role in scientific computing, and in this course, we will cover lists, tuples, and dictionaries. You will learn how to effectively use these data structures for storing and manipulating data. We will also explore control flow statements, including for loops, if statements, while loops, as well as break and continue statements.
The course will delve into functions, both built-in and user-defined. You will understand the concept of pass-by-value vs. pass-by-reference and learn how to work with positional and keyword arguments. We will also cover more advanced topics such as classes and objects, giving you an introduction to object-oriented programming in Python. You will learn about class attributes, methods, inheritance, and how to use objects as attributes.
To facilitate efficient coding and reusability, we will dive into writing and importing modules. This will enable you to create your own Python modules and leverage existing ones to enhance your scientific computing capabilities.
In the realm of scientific computing, the course will introduce you to essential scientific packages. We will cover Numpy, a fundamental package for scientific computing, which provides powerful tools for working with arrays and numerical operations. Scipy, another important package, will be explored for scientific and mathematical computations. Additionally, we will dive into Matplotlib, a versatile library for data visualization, empowering you to create visually appealing plots and charts to communicate your findings effectively.
By the end of this course, you will have gained a solid foundation in scientific computing with Python. You will be equipped with the skills to perform complex calculations, handle and analyse data, visualize results, and even delve into more advanced topics such as scientific packages and object-oriented programming.
This self-paced course allows you to learn at your own convenience, giving you the flexibility to study whenever and wherever you want. With practical exercises, coding examples, and real-world projects, you will be able to apply your knowledge to solve scientific problems.
Embark on this exciting journey of mastering Python for scientific computing and unlock new possibilities in your academic or professional pursuits. Enrol now and gain the skills to excel in the field of scientific computing with Python.
Key information:
- Course Instructor: Mr. Nishant Soni. He has a master’s degree in engineering [M.S. (Engg.)] focused on high-performance computing (HPC) from the Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Bangalore. He has worked as an Applications Engineer (CFD and Heat Transfer) at COMSOL Multiphysics, Bangalore in India. Prior to joining COMSOL, he worked in the field of software development, specializing in HPC simulation solutions. He has also worked on several research projects involving high-speed unsteady aerodynamics and reduced-order modeling.
- Course content: 3 Modules- 28 Lessons (~20 hours) along with quizzes and challenges/assignments
- Doubt clearance: Email support.
- Discussion forum: A discussion forum to discuss any topics with fellow students and the instructor
- Total access period: 12 Months from the day of enrolling
- Computer requirement: Minimum 4 GB RAM and i3 processor
- Access to the course: Once you make the payment, your login ID and password will be sent automatically via email.
More Information
- You will have gained a solid foundation in scientific computing with Python
- Implement academic projects in Python for your M.S. / Ph.D. thesis and also for any industrial projects.
- Write your own solver
- To be competent in the highly demanding field of scientific computing across various disciplines in academia and industry.
- Science and Engineering students pursuing B.Sc., B.E./B.Tech, M.Sc., MS/M.Tech, Ph.D. for their academic projects and to enhance their skills.
- CFD Fluent users who is planning to learn PyFluent
- Any computing enthusiasts.
- Professors/Lecturers/Teaching Assistants who want to teach or guide their students in scientific computing projects.
- Researchers, Scientists, or Engineers who want to shift from FORTRAN or MATLAB to Python
- Professionals already working in the industry but want to improve their Python fundamentals
- Do I get a certificate?
Yes. When you finish all the lessons and corresponding assignments/quizzes, you will be given the certificate. - Do I need a powerful workstation/computer to learn this course?
No, a normal laptop with 4 or 8GB RAM and a decent processor (i3) is good enough for this course. - What if I don’t understand some portion or need to clarify some doubts?
We will support you through emails and discussion forums to clear all doubts and questions - Should I know the programming or any other CFD software to learn this course?
Programming knowledge is not a prerequisite - Is there any prerequisite?
The course is aimed at programmers of all levels of expertise who wish to write scientific computing applications in Python. Basic familiarity with computer hardware and software, where files can be kept and edited is expected. Basic knowledge of mathematics, such as operations between vectors and matrices, and the Newton-Raphson method for finding the roots of non-linear equations would be an advantage.
Course Content
Course Includes
- 30 Lessons
- 26 Quizzes
Ratings and Reviews
YAGIZ ATOK ERSOY The Scientific Computing with Python course was very well structured and practical. It helped me strengthen my understanding of numerical methods, data visualization, and automation in engineering simulations. The lessons were clear and progressive, making complex concepts easy to grasp. Special thanks to Nishant, our instructor, for his clear explanations, continuous support, and enthusiasm thro
LIJO VARGHESE This course is excellent for anyone starting their programming journey in Python. It provides a solid foundation, beginning with procedural programming and gradually introducing object-oriented programming (OOP). Overall, I highly recommend this course to anyone who wants to learn Python effectively.
Vishal Jyoti This was an excellent lecture!I genuinely appreciate the clarity and passion you brought to teaching python. Your ability to breakdown complex topics into simple and digestible concepts was fantastic.
SHAIK NAGULMEERA Designed to be easy to learn and enjoyable for someone with little to no prior experience.
Joydeb Mukherjee This course is very informative.
Ansh Vishal Course is good for beginners. The Final Project was challenging it included solving the physics equations, implementing PDE solver and computing the data in python, Veritasium's Helicopter Rope Debate.
Harpreet Dhiman This course helped me in starting to code , made complex topics like OOPS simple and problems at end of each lesson reinforce the concepts we learned
Amitoj Singh This course is really good and detailed.
Fabius A well structured course for beginners. It really introduced me well to Python with also advanced assignments which are an introduction to the next lesson.
The concepts are very well explained, really enjoyed the small coding assignment after every video.