My Journey: Creating a Data Science with Python + GitHub

Author(s): Harshit Kandoi

Originally published on Towards AI.

“As an Engineering student, I panicked when recruiters asked for a portfolio until I built one that landed me interviews!”

Photo by Anete Lūsiņa on Unsplash

That moment of panic became real. In my final year of BTech, with a growing interest in data science and AI/ML, I realized I was unprepared to showcase my knowledge and skills I had built over time. My college had little to no placement support, and I knew I had to take control of things into my own hands. That’s the moment I decided to build a data science portfolio from scratch using nothing but Python Projects, GitHub as an Interface, and the internet.

In 2025, having a data science portfolio will not only be an advantage but a basic necessity. Thousands of students and early-career professionals are trying to enter the AI/ML field every day, and having a portfolio to showcase your journey can be the key to landing interviews, internships, or even admission to top MTech programs.

This blog is your step-by-step roadmap to creating a compelling data science portfolio that demonstrates your skillset, highlights your projects, and sets you apart from everyone. In this guide, you’ll discover ways to select the best projects for you,… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI