Phase 1: Python Fundamentals & Core Programming (4-6
weeks)
Week 1-2: Python Basics
● Syntax & Data Types: Variables, strings, numbers, booleans
● Data Structures: Lists, tuples, dictionaries, sets
● Control Flow: If statements, loops (for, while)
● Functions: Definition, parameters, return values, scope
● File I/O: Reading/writing files, working with CSV/JSON
Week 3-4: Intermediate Python
● Object-Oriented Programming: Classes, objects, inheritance, polymorphism
● Exception Handling: Try/except blocks, custom exceptions
● Modules & Packages: Import system, creating modules
● List Comprehensions: Basic and advanced comprehensions
● Decorators: Function decorators, class decorators
Week 5-6: Advanced Python Concepts
● Generators & Iterators: Yield, generator expressions, custom iterators
● Context Managers: With statements, creating context managers
● Metaclasses: Understanding metaclasses and their use cases
● Async Programming: Asyncio, coroutines, async/await
● Memory Management: Garbage collection, weak references
Phase 2: Software Engineering Principles (3-4 weeks)
Week 7-8: SOLID Principles & Design Patterns
● SOLID Principles:
○ Single Responsibility Principle (SRP)
○ Open/Closed Principle (OCP)
○ Liskov Substitution Principle (LSP)
○ Interface Segregation Principle (ISP)
○ Dependency Inversion Principle (DIP)
● Design Patterns:
○ Creational: Singleton, Factory, Builder
○ Structural: Adapter, Decorator, Facade
○ Behavioral: Observer, Strategy, Command
Week 9-10: Testing & Code Quality
● Unit Testing:
○ unittest framework
○ pytest (fixtures, parametrization, mocking)
○ Test-driven development (TDD)
○ Mock objects and patching with unittest.mock
● Code Quality:
○ Code coverage with coverage.py
○ Linting with pylint, flake8
○ Code formatting with black, isort
○ Type hints and mypy
○ Documentation with docstrings and Sphinx
Phase 3: Database & SQL Mastery (3-4 weeks)
Week 11-12: SQL Fundamentals
● Basic Queries: SELECT, WHERE, ORDER BY, GROUP BY
● Joins: INNER, LEFT, RIGHT, FULL OUTER joins
● Aggregate Functions: COUNT, SUM, AVG, MIN, MAX
● Subqueries: Correlated and non-correlated subqueries
● Data Modification: INSERT, UPDATE, DELETE
Week 13-14: Advanced SQL & Database Design
● Advanced SQL:
○ Window functions (ROW_NUMBER, RANK, LAG, LEAD)
○ Common Table Expressions (CTEs)
○ Stored procedures and functions
○ Indexes and query optimization
○ Transactions and ACID properties
● Database Design:
○ Normalization (1NF, 2NF, 3NF, BCNF)
○ Entity-Relationship diagrams
○ Database schemas and constraints
Python-SQL Integration
● Database Connectivity: psycopg2, sqlite3, SQLAlchemy
● ORM Concepts: SQLAlchemy Core and ORM
● Database Migrations: Alembic
● Connection Pooling: Managing database connections
Phase 4: Data Engineering with Python (4-5 weeks)
Week 15-16: Data Processing & ETL
● Data Processing Libraries:
○ Pandas: DataFrames, data manipulation, cleaning
○ NumPy: Numerical computing, arrays
○ Dask: Parallel computing for larger datasets
● ETL Pipelines:
○ Extract: APIs, databases, file systems
○ Transform: Data cleaning, validation, transformation
○ Load: Batch and streaming data loading
Week 17-18: Data Engineering Tools
● Apache Airflow:
○ DAG creation and scheduling
○ Task dependencies and sensors
○ Monitoring and logging
● Data Formats & Serialization:
○ Parquet, Avro, ORC
○ Protocol Buffers, MessagePack
○ Data compression techniques
Week 19: Streaming & Real-time Processing
● Apache Kafka: Producers, consumers, topics
● Python Streaming: kafka-python, confluent-kafka
● Real-time Analytics: Stream processing patterns
Phase 5: Containerization & Docker (2-3 weeks)
Week 20-21: Docker Fundamentals
● Docker Basics:
○ Images, containers, Dockerfile
○ Docker commands (build, run, exec, logs)
○ Port mapping and volume mounting
○ Docker networks
● Advanced Docker:
○ Multi-stage builds
○ Docker Compose for multi-container applications
○ Environment variables and secrets
○ Docker registries and image management
Week 22: Docker for Python Applications
● Containerizing Python Apps:
○ Optimizing Python Docker images
○ Poetry/pip in containers
○ Health checks and monitoring
○ Development vs production containers
Phase 6: Cloud Computing Fundamentals (4-5 weeks)
Week 23-24: Cloud Concepts & AWS Basics
● Cloud Computing Models: IaaS, PaaS, SaaS
● AWS Core Services:
○ EC2: Virtual machines, security groups
○ S3: Object storage, buckets, policies
○ RDS: Managed databases
○ Lambda: Serverless computing
○ IAM: Identity and access management
Week 25-26: Cloud-Native Development
● AWS for Python Developers:
○ Boto3 SDK for AWS automation
○ AWS CLI and CloudFormation
○ Lambda functions with Python
○ API Gateway and serverless architectures
Week 27: Multi-Cloud & Cloud-Native Patterns
● Azure/GCP Basics: Core services comparison
● Cloud-Native Patterns:
○ Microservices architecture
○ API-first design
○ Event-driven architecture
○ Circuit breaker pattern
Phase 7: Kubernetes & Container Orchestration (4-5 weeks)
Week 28-29: Kubernetes Fundamentals
● Core Concepts:
○ Pods, Services, Deployments
○ ConfigMaps and Secrets
○ Namespaces and resource quotas
○ Ingress controllers
● Kubernetes Architecture:
○ Master and worker nodes
○ etcd, kube-apiserver, kubelet
○ Container runtime (Docker/containerd)
Week 30-31: Advanced Kubernetes
● Advanced Concepts:
○ StatefulSets and DaemonSets
○ Persistent Volumes and Claims
○ RBAC (Role-Based Access Control)
○ Network policies and service mesh
● Kubernetes Ecosystem:
○ Helm charts for package management
○ Monitoring with Prometheus and Grafana
○ Logging with ELK stack
Week 32: DevOps & CI/CD
● CI/CD Pipelines:
○ GitHub Actions, GitLab CI, Jenkins
○ Building and deploying containerized apps
○ Infrastructure as Code (Terraform, Pulumi)
○ GitOps with ArgoCD or Flux
Phase 8: Advanced Topics & Specialization (4-6 weeks)
Week 33-34: Performance & Monitoring
● Application Performance:
○ Python profiling (cProfile, line_profiler)
○ Memory profiling with memory_profiler
○ Performance optimization techniques
○ Caching strategies (Redis, Memcached)
● Observability:
○ Logging best practices
○ Metrics and monitoring
○ Distributed tracing
○ APM tools (New Relic, Datadog)
Week 35-36: Security & Best Practices
● Security:
○ OWASP Top 10
○ Secure coding practices
○ Container security
○ Secrets management (HashiCorp Vault)
○ Authentication and authorization
Week 37-38: Data Engineering Specialization
● Big Data Tools:
○ Apache Spark with PySpark
○ Data lakes and data warehouses
○ Snowflake, BigQuery, Redshift
○ Data pipeline orchestration
● Machine Learning Operations (MLOps):
○ Model deployment and versioning
○ ML pipelines with Kubeflow
○ Feature stores and model monitoring

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