Data Science & AI Roadmap
Complete 12-18 month journey from foundations to production ML & AI systems
Prerequisites
- Basic programming knowledge
- High school mathematics
- Familiarity with command line
- Problem-solving mindset
Learning Outcomes
- Build end-to-end ML pipelines
- Master Python & SQL for data
- Create EDA visualizations
- Deploy simple ML models
- Version control with Git
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- ✓ 21 Premium Courses
- ✓ Weekly Live Mentorship Sessions
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Learning Path
Portfolio Projects
End-to-End Tabular ML Pipeline
BeginnerData ingestion → model → deployment + monitoring
NLP Classifier with Explainability
IntermediateFine-tune transformer + LIME/SHAP explanations
RAG QA Application
AdvancedPolicy docs QA with vector store + streaming UI
Time Series Forecasting
IntermediateProbabilistic forecasts with backtesting & deployment
Computer Vision Pipeline
AdvancedFine-tune for detection/segmentation + web demo
MLOps Infrastructure
AdvancedMLflow + DVC + automated retrain pipeline
AI Agent System
ExpertLLM agent with DB query & natural language summary
Business Intelligence Dashboard
IntermediateETL → warehouse → dashboard with KPIs
Key Learning Resources
Foundations
- • roadmap.sh (AI/Data Scientist)
- • DataCamp courses
- • Kaggle Learn
- • Fast.ai courses
Advanced
- • Hugging Face documentation
- • PyTorch tutorials
- • MLflow/Neptune blogs
- • Papers with Code
Practice
- • Kaggle competitions
- • GitHub portfolio
- • Medium/blog writing
- • Open source contribution
Recommended Weekly Schedule
Daily (1-2 hrs)
Coding practice, Kaggle notebooks, tool exploration
3× Week (2 hrs)
Structured courses, video tutorials, reading papers
Weekend (4-6 hrs)
Project work, portfolio building, blog writing
Complete one medium project every 4-6 weeks for optimal progress