Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video Based |
Course Overview
The Scientific Computing Using Python certification course delves into essential computational methods vital for Science and Engineering postgraduates. Focused on Python programming, it covers interpolation, integration, differentiation, ODE and PDE solvers, basic linear algebra, and Monte Carlo techniques. With a 12-week duration, it empowers students for diverse applications in various industries.
Tailored for advanced undergraduates and postgraduates in Physics, Mathematics, Mechanical, Aerospace, Computer Science, and Chemical Engineering, the Scientific Computing Using Python certification by Swayam mandates a foundational grasp of calculus, linear algebra, and differential equations. It equips learners with indispensable tools for computational challenges in scientific and engineering domains.
Also Read: Online Python Certification Courses
The Highlights
- Elective Online Course
- 12 Weeks Duration
- Offered by IIT Kanpur
Programme Offerings
- Certificate of completion
- Practical Learning
- Hands-on Experience
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 1000 | yes | IIT Kanpur |
The Scientific Computing Using Python certification fees is free. However, if you want a certificate, you must register and take the proctored exam at designated centres, which is optional and comes with a fee of Rs 1000.
Scientific Computing Using Python Certification Fee Structure
Particulars | Total Fees |
Scientific Computing Using Python (exam) | Rs 1000/- |
Eligibility Criteria
Academic Qualifications
The Scientific Computing Using Python certification course is intended for postgraduate students of Science and Engineering.
Certification Qualifying Details
To receive the Scientific Computing Using Python certification by Swayam, you need a minimum average assignment score of 10/25 and an exam score of 30/75.
What you will learn
After completing the Scientific Computing Using Python certification syllabus, you will acquire a comprehensive understanding of computational methods, beginning with an introduction to the Python programming language, recognized as a fundamental tool in scientific and engineering applications.
Upon completion of the Scientific Computing Using Python training, you will delve into crucial topics such as interpolation, integration, differentiation, and the solution of ordinary and partial differential equations (ODE and PDE). You will also cover essential aspects of basic linear algebra and Monte Carlo techniques, providing you with a well-rounded skill set.
Who it is for
The Scientific Computing Using Python online course is suitable for those seeking to enhance their computational skills for application in various industries. This course is particularly beneficial for:
Admission Details
Follow these steps to join the Scientific Computing Using Python classes:
Step 1: Browse the URL below:
https://onlinecourses.nptel.ac.in/noc23_ph46/preview
Step 2: Click on the “Sign-in/ Register” button.
Step 3: Fill out the necessary details and submit the form
The Syllabus
- Module 1: About Computers
- Module 2: Python variables
- Module 3: Python arrays
- Module 1: Python Control Structure
- Module 2: Python functions
- Module 3: Programming style
- Module 1: Plotting
- Module 2: Data input/output
- Module 3: Error analysis and non-dimensionalization
- Module 1: Lagrange Interpolation
- Module 2: Splines
- Module 1: Numerical Integration: Newton Cotes
- Module 2: Gaussian quadrature
- Module 3: Multidimensional and misc integration
- Module 1: Differentiation
- Module 2: ODE solvers: Euler method
- Module 3: ODEs: Implicit schemes
- Module 1: ODEs: Higher-order method
- Module 2: ODEs: System of equations, Stiff equations
- Module 3: Fourier Transforms
- Module 1: Spectral method (PDE solvers): Diffusion equation
- Module 2: Spectral method: Wave and Burger equation solver
- Module 3: Spectral: Navier-Stokes equation solver
- Module 4: Spectral: Schrodinger equation solver
- Module 1: Finite Difference (FD) (PDE solvers): Diffusion equation
- Module 2: FD method: Wave and Burger equation solver
- Module 3: FD Method: Navier-Stokes equation solver
- Module 4: FD Method: Schrodinger equation solver
- Module 1: Solving Nonlinear Equations (Root Finders)
- Module 2: Boundary value problems (Shooting method)
- Module 3: Eigenvalue solver for diff equations
- Module 1: Laplace equation solvers
- Module 2: Laplace equation solvers
- Module 3: Poisson equation solvers
- Module 1: Linear algebra: Solution of linear equations
- Module 2: Linear algebra: Eigenvalues and eigenvectors
- Module 3: Intro to Monte Carlo method
- Module 4: Summary
Evaluation process
The Scientific Computing Using Python certification offers an optional exam. Your final score is determined by averaging the best 8 out of 12 assignments (25%) and your proctored exam score (75%).
Instructors
IIT Kanpur Frequently Asked Questions (FAQ's)
A basic understanding of calculus, linear algebra, and ordinary and partial differential equations is required. While not mandatory, a basic knowledge of computation is recommended.
Yes, the course is designed for postgraduate students, but advanced undergraduate students in fields like Physics, Mathematics, Mechanical, Aerospace, Computer Science, and Chemical Engineering, are also encouraged to enroll.
The course primarily focuses on the Python programming language, recognized as the de facto language for scientific and engineering applications.
This course equips you with practical computational skills highly valued in industries such as Computational Fluid Dynamics, Computational Electrodynamics, Data Sciences, and Finance.
Companies involved in Computational Fluid Dynamics, Computational Electrodynamics, Data Sciences, Finance, and related sectors recognize and support this course, indicating its relevance to real-world applications and industry demands.