School of Scientific Computing & Informatics · Scientific programming
Scientific Computing with Python
Python is the common language of modern science, from physics and chemistry to biology and earth sciences. This course teaches the numerical Python stack that every computational course in the catalogue builds on.
Course overview
Scientific Computing with Python teaches programming through numerical problems: arrays and vectorised computation with NumPy, fitting models to data, solving equations, integrating and optimising with SciPy, and communicating results with matplotlib.
Good practice is built in from the first week: notebooks for exploration, scripts and functions for anything reused, version control, and simple tests that catch numerical mistakes before they reach a figure.
Learning outcomes
By the end of the course you will be able to:
- Write clear Python with functions and modules
- Use NumPy arrays and vectorised operations efficiently
- Fit models, optimise, integrate and solve equations with SciPy
- Produce publication-quality figures with matplotlib
- Organise, test and version-control a small computational project
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates in any science, engineering, pharmacy, medicine, agriculture, geography or computing programme (B.Sc., B.Tech., B.E., BCA, M.Sc., M.Tech., MCA, PhD)
- Researchers, faculty and working professionals in research, healthcare, biotech, pharma or IT who want to apply computing to science
- Final-year students may apply before their results are declared
Prerequisites
- School-level mathematics
- No programming experience required
Best suited to
- Science and engineering students who need to compute
- Researchers moving from spreadsheets or MATLAB to Python
- Learners preparing for the informatics, physics and chemistry courses
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Types, control flow and functions
- Modules and packages
- Jupyter and scripts
Topics
- Arrays, shapes and broadcasting
- Vectorisation versus loops
- Random numbers and simulation
Topics
- Curve fitting and optimisation
- Integration and differential equations
- Statistical tests
Topics
- matplotlib fundamentals
- pandas for tabular data
- Reading and writing common formats
Topics
- Project layout and environments
- Testing numerical code
- Git and sharing
Topics
- Model
- Analysis
- Report
Practical sessions and projects
Capstone project
A reproducible computational study
Build, test and document a small numerical model or data analysis, from raw input to figure.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- Python foundations
Write functions to convert units and summarise a small experimental dataset.
- NumPy
Simulate random walks and diffusion and compare loop and vectorised performance.
- SciPy for science
Fit an enzyme-kinetics model, integrate a population model and test a hypothesis on measured data.
- Figures and data
Turn a raw instrument export into a clean table and a journal-ready figure.
- Reproducible projects
Package your analysis with tests and publish it as a Git repository.
- Project: a computational study
Model and analyse a scientific problem of your choice and present code, results and figures.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. Recommended computer: 8 GB RAM, 30 GB free disk, Linux, macOS or Windows with WSL2.
Datasets
- Open instrument and environmental datasets: Fitting and analysis exercises.Public data portals
- Simulated systems: Numerical methods with known answers.Generated in class
Core reading
- Harris CR et al. (2020). Array programming with NumPy. Nature 585:357–362.
- Virtanen P et al. (2020). SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 17:261–272.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Scientific Computing with Python, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Scientific Computing with Python
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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