Roadmap
📊 Data Science
Math → Python → ML → Deep Learning → Deployed Models.
What you'll learn
End-to-end Data Science: build statistical intuition, master Python data tooling, learn classical ML, then deep learning and deployment.
Who this is for
Engineers, analysts and students who want to enter the ML / Data Science field.
After completion you can
- Train and evaluate ML models
- Build and deploy a deep learning model
- Tell stories with data visualizations
- Be interview-ready for DS roles
Industry demand
2020
2021
2022
2023
2024
2025
Prerequisites
✅ Required
- • Python basics
- • High-school math
⚡ Helpful
- • Linear algebra & calculus intuition
Beginner · Month 1–2
Math & Python Foundations
0/5 topics
0%
Intermediate · Month 3–5
Machine Learning
0/6 topics
0%
Advanced · Month 6–8
Deep Learning & Deployment
0/7 topics
0%
Projects to build
Project 1
EDA Notebook
Full exploratory analysis on Kaggle dataset.
Pandas/Seaborn
View project guide →
Project 2
House Price Predictor
End-to-end regression pipeline.
scikit-learn
View project guide →
Project 3
Customer Churn Classifier
Realistic business ML project.
Random ForestXGBoost
View project guide →
Project 4
Image Classifier API
Trained model served behind an API.
PyTorch + FastAPI
View project guide →
Project 5
LLM-powered Q&A Bot
Fine-tune & deploy a small LLM.
HuggingFace + Streamlit
View project guide →
Certifications
Google Data Analytics Professional Certificate
IBM Data Science Professional
DeepLearning.AI Specializations
Career & salary
Data Analyst
₹5L – ₹12L /yr
Data Scientist (Mid)
₹12L – ₹30L /yr
ML Engineer
₹15L – ₹45L /yr
Senior Data Scientist
₹30L – ₹70L /yr