Open‑Source Tools for AI‑Enabled Academic Research
A practical, hands-on training in Google Colab, Kaggle and GitHub — the three tools that quietly hold together modern, reproducible research. Build one real project end‑to‑end across three evenings.
Research is drowning in tools it was never taught to use
Most researchers still learn statistics, methodology and writing — but almost never the infrastructure that modern, AI-enabled research actually runs on.
The gap is structural, not personal. Every year, more research depends on code, data pipelines and AI models — yet most academic training still stops at the literature review and the statistics course. Students and faculty are expected to "figure out" notebooks, datasets and version control on their own, usually under deadline pressure, right when it matters most.
That gap has real costs: analyses that can't be rerun, results that can't be verified, and projects that quietly stall because a laptop couldn't handle the dataset or a collaborator couldn't open the file. Reproducibility and collaboration aren't optional extras anymore — they're what separates research that gets cited from research that gets forgotten.
This workshop exists to close that gap directly: three tools, three evenings, one real project — so the next dataset you touch turns into a result you can actually defend, share and build on.
Reproducibility is the new peer review
Journals and funders increasingly expect open code and data alongside a paper — not just a methods section.
Collaboration has outgrown email attachments
Co-authors across cities and countries need one shared, versioned source of truth — not "final_v3_FINAL.ipynb".
Compute shouldn't be the bottleneck
Free cloud notebooks mean a low-spec laptop is no longer an excuse to skip real data analysis or model training.
What each tool actually does for your research
Three tools, three distinct jobs — together they cover the full lifecycle of a modern research project.
Google Colab
role: the analysis engineA free, cloud-hosted notebook where your code, results and explanations live side by side — with GPU/TPU access, so heavy analysis doesn't need a powerful laptop.
- Run Python analysis without any local setup
- Produce readable, shareable research notebooks
- Turn raw data into charts, models and findings
Kaggle
role: the data sourceA vast, well-documented library of real-world datasets and community notebooks — a shortcut past the slowest part of most projects: finding usable data.
- Discover datasets relevant to your research question
- Pull data programmatically via the Kaggle API
- Learn from public notebooks and benchmark models
GitHub
role: the permanent recordThe version-controlled home for your project — where your work becomes citable, collaborative and impossible to accidentally lose or overwrite.
- Track every change with commits, not filenames
- Collaborate safely using branches and issues
- Publish a professional, citable research repository
Three evenings, one continuous project
Each day builds directly on the last — by Day 3, your Day 1 dataset is a published GitHub repository.
Google Colab
- Create research notebooks
- Upload and manage datasets
- Use Python libraries for analysis
- Perform exploratory data analysis
- Create visualisations
- Save and share notebooks
Kaggle
- Explore research-grade datasets
- Download datasets using the Kaggle API
- Understand dataset documentation
- Clean and analyse data
- Build a basic machine-learning model
- Use Kaggle notebooks
GitHub
- Create a research repository
- Upload notebooks and project files
- Write a professional README
- Manage versions using commits
- Collaborate using branches and issues
- Connect Google Colab with GitHub
Built for anyone doing — or supporting — research
No prior coding expertise required. If you touch data or write papers, this workshop is for you.
UG & PG Students
Working on projects, theses or dissertations that need real data and reproducible analysis.
Research Scholars
PhD and M.Phil scholars who need a dependable analysis-to-publication workflow.
Faculty & Educators
Guides and mentors who want to teach these tools to their own students with confidence.
Early-Career Researchers & Analysts
Anyone entering AI/data-driven research who wants an industry-standard toolkit from day one.
Led by a practitioner, not just a slide deck
Dr. Sudharson D
Kumaraguru College of Technology, Coimbatore, India. Brings hands-on industry data-engineering experience directly into the academic research workflow taught in this workshop.
Reserve your seat
Choose the option that applies to you. Every registrant receives a certificate and full session recordings.
Indian Participants
- Live access to all 3 sessions
- Hands-on project across Colab, Kaggle & GitHub
- Certificate of participation
- Recordings of every session
Foreign Participants
- Live access to all 3 sessions
- Hands-on project across Colab, Kaggle & GitHub
- Certificate of participation
- Recordings of every session