Data Science Doesn't Happen in a Notebook.
Learn how real data science teams work — using Git, GitHub, VS Code, Cookie Cutter Data Science and Data Version Control — through a complete simulated team project with four collaborators, five milestones and production-style code.
- Git & GitHub
- VS Code
- Cookie Cutter DS
- Data Version Control
- Certificate
Stop working alone. Start working like a team.
WATCH THE PROGRAM PREVIEW
Every Data Scientist Learns Python. Almost None Learn How Teams Actually Work.
If you have been working on data science projects alone in Jupyter Notebooks — you are missing half the picture. Do any of these sound familiar?
Stuck in a Notebook
"All my data science projects live either in Jupyter Notebooks on my laptop or on Google Colab. I've never worked any other way."
Heard of Git, Never Used It
"I've heard of Git and GitHub but don't understand why a data scientist would actually need them."
No Idea How Teams Collaborate
"I don't know how multiple people could work on the same data science project without breaking each other's code."
No Project Structure
"My projects have no structure — just a folder of notebooks and scripts. I don't know what a professional project looks like."
Pull Requests Are a Mystery
"I don't know what pull requests, branches, or code reviews are — or why they matter."
Notebook to Production Gap
"I can build models but I have no idea how to move from a notebook to something a team could actually maintain."
This course was built for data scientists who are ready to work the way professionals do.
Not Another Tutorial. A Real Team Project.
Most courses teach Git commands. This one teaches you how to use Git as part of a functioning data science team.
Simulated Team Environment
Follow a four-person team — Ricky, Amy, Sarah and Bob — as they work through a complete data science project. Every team interaction, conflict, review and merge is shown in real time, so you see exactly how collaboration works in practice.
Professional Project Structure
Learn Cookie Cutter Data Science — the industry-standard project template that organises code, data, notebooks and documentation in a way teams can navigate and maintain. Move permanently beyond the single-notebook project.
Production-Ready Workflows
From virtual environments and requirements.txt to Makefiles for workflow automation and DVC for data version control — learn the tools and habits that separate a solo notebook from a maintainable, reproducible data science project.
A Complete Collaborative Data Science Toolkit.
Every tool. Every workflow. Every team skill — taught through a real project simulation.
Git & Version Control
Track every change to your code, revert mistakes instantly, and understand the history of a project. The foundation of every professional development workflow.
GitHub
Host, share and collaborate on projects using the world's most widely used code platform. Create repositories, manage permissions and integrate with your team.
VS Code for Data Science
Move beyond Jupyter Notebooks. Set up VS Code as a professional data science environment with Git integration, file tracking and project navigation.
Cookie Cutter Data Science
Give every project a clean, consistent, industry-standard structure from day one. CCDS organises your code, data, notebooks and docs so your whole team can find what they need.
Branching & Pull Requests
Work on features in isolation without affecting the main codebase. Learn to raise, review, approve and merge pull requests — the core collaboration loop in any team.
Code Review
Learn to review a teammate's pull request, identify issues, request changes and approve merges. A skill most data scientists never develop — but every team depends on.
Data Version Control (DVC)
Data changes just like code does. Learn DVC to track datasets and model files alongside your code — making your experiments reproducible and auditable.
Makefile Automation
Automate your data pipeline so any team member can reproduce your workflow with a single command. Eliminate manual steps and environment inconsistencies.
Virtual Environments & Requirements.txt
Ensure your project runs identically on every team member's machine. Learn to create, manage and share environments correctly.
Project Boards & Milestones
Track work visually using GitHub Projects. Assign tasks, set milestones and move cards across columns — just like a real engineering team.
Conflict Resolution
Learn what happens when two people edit the same file — and how to resolve merge conflicts cleanly without losing work or breaking the project.
End-to-End Team Project
Apply every tool and workflow through a five-milestone project simulation with four teammates — from data ingestion through model building to production code and documentation.
What Most Data Science Courses Teach — vs What You Will Learn Here.
Typical Courses
- Projects live in a single Jupyter Notebook
- No version control — save-as and hope
- No project structure — files scattered everywhere
- Only works on one machine
- Cannot collaborate without overwriting each other
- No code review or approval process
- Cannot reproduce results reliably
This Program
- Professional project structure with Cookie Cutter DS
- Full Git version control on every change
- Organised, navigable project that any teammate can use
- Reproducible environment with virtual env and requirements.txt
- Branching, pull requests and collaborative merging
- Code review and approval workflow
- DVC for data and model reproducibility
16 Modules. One Complete Team Project. Zero Guesswork.
From your first Git commit to production code reviewed and merged by a data science team.
Built for Serious Learners.
Perfect For
- Data Scientists and ML engineers who work solo and want to collaborate professionally
- Learners who have completed a data science or machine learning course and want the next level
- Analysts moving into engineering or product teams where version control is expected
- Anyone preparing for a data science role where Git and GitHub are listed as requirements
- Developers building data science projects who want industry-standard structure and workflows
Not Ideal For
- Complete beginners with no prior data science or Python experience
- Those looking for a machine learning or statistical modelling course
- Learners who want only theory without hands-on project work
- Those looking for deep learning or MLOps/cloud deployment content
Everything You Need to Work Like a Pro.
Self-Paced
Learn on your schedule. No deadlines.
Lifetime Access
Buy once. Return whenever you need a refresher.
Regular Updates
Content evolves as tools and practices change.
Certificate
Earn a completion certificate on finishing.
Practical Learning
Learn techniques used by real practitioners.
Team Simulation
A four-person project across five milestones — not a toy example.
Your instructors
Vijay
Lead Instructor | Tech Co-founder
B.S. Data Science
Vijay completed his B.S. in Data Science and Applications from the Indian Institute of Technology Madras, graduating with a CGPA of 9.7+ and earning the Academic Distinction Award. He is now the Co-Founder of a technology company developing AI-powered solutions. As the creator and instructor of this course content, Vijay combines academic excellence with hands-on industry experience, helping learners master concepts through practical, real-world applications.
Animesh Tiwari
AI & Data Capability Advisor | Educator
MScFE | MBA | MBB | PGDStats | PGPBABI
Animesh has trained 30,000+ learners across Data Science, AI, and Machine Learning over 10+ years, working with leading EdTech platforms and maintaining an average learner rating of 4.85/5 from 50,000+ reviews. Before transitioning into Data Science education, he held leadership roles in the corporate sector, managing large teams and delivering outcomes for clients across technology, banking, and telecommunications. As the architect behind this course, Animesh defined the learning objectives, designed the curriculum structure, and reviewed every module to ensure practical relevance and industry alignment. His focus is on helping learners build skills that translate directly into real-world applications and career growth.
Real Voices. Real Experiences.
Most courses teach models. This course taught me how data scientists actually collaborate in real-world teams.
The project-based approach made Git, GitHub, pull requests and code reviews surprisingly easy to understand.
The simulated team project was brilliant. It showed exactly how data science projects are managed in organizations.
The sections on Cookie Cutter Data Science, DVC and Makefile automation were worth the course alone. Extremely practical and immediately applicable.
Clear explanations, practical examples, and zero unnecessary complexity. Vijay's teaching style is easy to follow. This course taught me team workflows, not just Git commands. Extremely practical and easy to follow.
What stood out about Vijay was his ability to connect every tool to a real-world problem. By the end of the course, Git, GitHub and team workflows felt less like software tools and more like essential data science skills.
Questions, Answered.
Build the Skill That Turns a Data Scientist Into a Team Player.
Git, GitHub, structured projects and real team workflows — the skills every data science job expects and almost no course teaches.