Python for Data Science: The Definitive Curriculum
Python for Data Science: The Definitive Curriculum
🎯 Foundations and Conceptual Framework
Introduction to Data Science
- Fundamentals and Philosophy
- History and evolution of data science
- The scientific method in data analysis
- Critical thinking and analytical approach
- Differences between DS, ML, AI, and statistics
- Roles and Specializations
- Data Scientist vs Data Analyst vs Data Engineer
- Generalist vs specialist data scientist
- Emerging roles in the industry
- Technical skills vs soft skills
- Ethics and Responsibility
- Bias in data and models
- Privacy and data protection (GDPR, CCPA)
- Transparency and explainability
- Social impact of models
- Sustainability in ML/AI
Professional Environment Setup
- Environment Management
- Anaconda and Miniforge
- Poetry for dependency management
- Docker for reproducible environments
- GPU setup (CUDA, cuDNN)
- Advanced Version Control
- Git flow for data projects
- DVC for data versioning
- CI/CD for DS projects
- IDEs and Tools
- JupyterLab with advanced extensions
- VSCode configuration for DS
- PyCharm Professional features
- Interactive notebooks (Colab, Databricks)
📊 Programming and Data Fundamentals
Advanced Python for DS
- Code Optimization
- Vectorization and efficient operations
- Memory and performance
- Profiling and debugging
- Parallel processing
- Design Patterns
- Factory pattern for models
- Strategy pattern for pipelines
- Observer for monitoring
- Decorator for transformations
- Testing in DS
- Unit testing of models
- Data testing
- Property-based testing
- Integration testing
Professional Data Manipulation
- Advanced Pandas
- Memory optimization
- Chunking for large datasets
- Custom vectorized operations
- Advanced MultiIndex
- Extension arrays
- Distributed Processing
- Dask for parallel computation
- Vaex for large datasets
- Rapids for GPU acceleration
- Formats and Storage
- Parquet and optimization
- HDF5 for scientific data
- Feather and Arrow
- Data Streaming
📈 Visualization and Communication
Advanced Visualization
- Visualization Systems
- Grammar of Graphics
- Altair and Vega
- Bokeh for interactivity
- Basic D3.js
- Specialized Visualizations
- Complex network visualization
- Spatiotemporal data
- High-dimensional data
- ML model visualization
- Professional Dashboards
- Dash for web applications
- Advanced Streamlit
- Panel for notebooks
- Voilà for deployments
Communicating Results
- Data Storytelling
- Effective narrative
- Presentation design
- Automated reports
- Technical documentation
- Business Intelligence
- Advanced Tableau
- Power BI DAX
- Looker
- Reporting methods
📐 Mathematical and Statistical Foundations
Mathematics for DS
- Applied Linear Algebra
- Matrix decomposition
- Eigenvalues and eigenvectors
- Matrix optimization
- Applications in ML
- Multivariable Calculus
- Gradients and partial derivatives
- Multivariable optimization
- Lagrange multipliers
- Applications in DL
- Optimization
- Convex and non-convex
- Numerical methods
- Stochastic optimization
- Genetic algorithms
Advanced Statistics
- Statistical Inference
- Bootstrapping and resampling
- Bayesian inference
- Mixed models
- Causal analysis
- Experimental Design
- Advanced A/B testing
- Factorial designs
- Multivariate tests
- Statistical power
- Time Series
- ARIMA/SARIMA models
- State-space models
- Advanced Prophet
- Temporal deep learning
🤖 Advanced Machine Learning
Deep Fundamentals
- Learning Theory
- PAC learning
- VC dimension
- Regularization and complexity
- Information theory
- Advanced Feature Engineering
- Automatic selection
- Feature importance
- Feature interaction
- Feature learning
Advanced Models
- Sophisticated Ensembles
- Advanced stacking
- Voting schemes
- Cascading
- AutoML
- Unsupervised Learning
- Spectral clustering
- Manifold learning
- Topic modeling
- Advanced embeddings
- Special Cases
- Semi-supervised learning
- Few-shot learning
- Active learning
- Online learning
🧠 Deep Learning and Advanced AI
Modern Architectures
- Advanced Transformers
- Modern architectures
- Advanced fine-tuning
- Training optimization
- Interpretability
- Generative Models
- Advanced GANs
- Diffusion models
- Hybrid architectures
- Flow-based models
- Reinforcement Learning
- DQN and variants
- Policy gradients
- Model-based RL
- Multi-agent systems
Specialized Applications
- Advanced Computer Vision
- Semantic segmentation
- Object tracking
- Few-shot vision
- Neural rendering
- Advanced NLP
- Question answering
- Summarization
- Translation
- LLMs and prompt engineering
🛠 MLOps and Production
Infrastructure
- Distributed Systems
- Kubernetes for ML
- Ray for training
- Spark structured streaming
- Airflow for pipelines
- Advanced Cloud
- Advanced AWS SageMaker
- Azure ML enterprise
- GCP Vertex AI
- Multi-cloud strategies
ML Operations
- Monitoring and Maintenance
- Drift detection
- Model health metrics
- Performance monitoring
- Auto-retraining
- Security and Governance
- Model security
- Data governance
- Compliance automation
- Audit trails
📡 Specialization and Use Cases
Industry Verticals
- Finance
- Risk modeling
- Fraud detection
- Trading algorithms
- Credit scoring
- Healthcare
- Medical imaging
- Clinical predictions
- Genomics
- Drug discovery
- Retail
- Demand forecasting
- Recommendation systems
- Price optimization
- Customer segmentation
- Manufacturing
- Predictive maintenance
- Quality control
- Supply chain optimization
- Process optimization
End-to-End Projects
- Real Implementations
- System architecture
- Scalability
- Monitoring
- Maintenance
- Case Studies
- Successes and failures
- Lessons learned
- Best practices
- ROI and metrics
🎓 Professional Development
Career and Growth
- Portfolio Building
- Featured projects
- Open source contributions
- Kaggle competitions
- Research papers
- Networking
- Technical communities
- Conferences
- Mentoring
- Personal branding
Trends and Future
- Emerging Technologies
- Quantum ML
- Edge AI
- AutoML/AutoDL
- Neural architecture search
- Research
- Reading papers
- Reproducing results
- Experimentation
- Publication