Skip to content
View datasciencekibaatein's full-sized avatar
12 followers · 0 following

Block or report datasciencekibaatein

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don’t include any personal information such as legal names or email addresses. Markdown is supported. This note will only be visible to you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
datasciencekibaatein/README.md

Data Science Learning Path

A structured repository documenting my journey through Data Science — from Python fundamentals to Machine Learning. All materials are organized to support both my learning and anyone following a similar path.

Data Science Learning Path

About This Repository

This repository serves as the working reference for my Data Science curriculum, currently being taught on my YouTube channel Datascience ki Baatein — where I explain concepts in Hindi for absolute beginners.

Everything here is production-quality material, not throwaway notes. If you find a project, notebook, or explanation useful — take it, learn from it, build on it.

Repository Structure

/python — Complete Python foundation series Variables, data structures, control flow, functions, file handling, exception handling, modules & packages. Each topic includes a Jupyter notebook and video reference.

/projects — Portfolio-quality projects End-to-end implementations covering multiple concepts. Currently includes the Student Marks Management System (Python capstone) with modular architecture.

/numpy-pandas (coming soon) Data manipulation and analysis with NumPy and Pandas — the working toolkit of every Data Scientist.

/machine-learning (coming soon) Supervised and unsupervised learning algorithms, with mathematical intuition and scikit-learn implementations.

/resources — Curated references Cheat sheets, roadmaps, and reading lists I've personally found useful. Everything here is vetted, not scraped from Google.

Learning Philosophy

I believe in three things when it comes to teaching Data Science:

1. Foundation before frameworks. You cannot skip Python and jump to LangChain. The industry hires Data Scientists who understand systems — not people who prompt-engineer their way through problems.

2. Language matters. Hindi-speaking learners deserve quality content in the language they think in. Translation isn't a "downgrade" — it's a bridge to underserved audiences.

3. Depth over speed. A properly built 6-month curriculum beats a rushed 30-day bootcamp every time. Real skills compound.

Tech Stack

Tech Stack

Python · NumPy · Pandas · Matplotlib · Seaborn · Scikit-Learn · SQL · Statistics · Deep Learning · NLP · GenAI (LangChain, RAG)

For Learners

If you're following along with the YouTube series, videos and notebooks are aligned. If you want to contribute — either through corrections, additional examples, or suggestions — open an issue or send a pull request.

If this repository saves you even a few hours of learning time, consider subscribing to the YouTube channel. That's the fuel that keeps this work going.

Contact

For questions, collaborations, or corrections:

Currently teaching Python fundamentals. NumPy and Pandas begin next.

Pinned Loading

  1. Titanic-Survival-Prediction Titanic-Survival-Prediction Public

    Production-ready Titanic Survival Prediction application built with FastAPI and Scikit-Learn, featuring an interactive frontend, REST API, and complete ML deployment workflow.

    Jupyter Notebook

  2. UCI-BankNote-Prediction UCI-BankNote-Prediction Public

    Machine Learning classification project built with FastAPI and Scikit-Learn for authenticating banknotes using the UCI Banknote Authentication dataset. Includes REST API and interactive web interface.

    Jupyter Notebook