Introduction:

A Python Developer Training Plan structured into progressive levels: Beginner, Intermediate, and Advanced, covering core Python, data science, and API development.

Training Plan

Phase 1: Beginner – Python Programming Fundamentals (4 Weeks)

Block 1: Introduction to Python

• Installing Python and IDEs (VS Code, Jupyter, PyCharm)
• Python syntax, keywords, variables, and data types
• Input/output, type casting, and string formatting
• Basic operators and expressions

Block 2: Control Structures & Functions

• Conditional statements (if, elif, else)
• Loops (for, while), break, continue, range( )
• Functions, arguments, return values
• Lambda functions and basic recursion

Block 3: Data Structures

• Lists, Tuples, Sets, Dictionaries
• List comprehension
• Nested structures and data manipulation

Block 4: Modules and File Handling

• Importing and using built-in/external modules
• Writing and importing custom modules
• File handling (read/write in text/CSV/JSON)
• Exception handling and try-except-finally

Phase 2: Intermediate – OOP, Libraries, and Testing (4 Weeks)

Block 5: Object-Oriented Programming (OOP)

• Classes, objects, attributes, and methods
• __init__, __str__, __repr__
• Inheritance, polymorphism, encapsulation
• @classmethod, @staticmethod, @property

Block 6: Testing & Debugging

• Unit testing with unittest and pytest
• Debugging techniques (using IDEs, breakpoints)
• Logging module
• Code formatting with black and linting with flake8

Block 7: Working with Libraries

• NumPy (arrays, vectorization)
• Pandas (DataFrames, filtering, grouping, merging)
• Matplotlib & Seaborn (data visualization basics)

Block 8: Virtual Environments and Dependency Management

• venv and virtualenv
• pip, pip-tools, and requirements.txt
• Introduction to poetry or pipenv

Phase 3: Data Science & Machine Learning (5 Weeks)

Block 9: Exploratory Data Analysis

• Loading data (CSV, Excel, SQL)
• Handling missing data, duplicates
• Descriptive statistics
• Data visualization for EDA

Block 10: Introduction to Machine Learning

• Scikit-learn basics
• Supervised learning (Linear Regression, Decision Trees)
• Unsupervised learning (K-Means Clustering)
• Train/Test split, Cross-validation

Block 11: Advanced ML Concepts

• Pipelines and model evaluation
• Hyperparameter tuning with GridSearchCV
• Feature selection and importance

Block 12: Data Projects

• Complete real-world projects like:

  • Customer churn prediction
  • Movie recommendation system
  • Sales forecasting

Block 13: Data Engineering Basics

• Working with large datasets
• Intro to Dask and PySpark
• SQL with Python (using sqlite3, SQLAlchemy)
• Data pipelines with Airflow or Prefect (basic)

Phase 4: API Development and Backend Skills (4 Weeks)

Block 14: REST APIs with Flask/FastAPI

• Flask/FastAPI basics
• Request/response lifecycle
• CRUD operations with RESTful design
• JSON serialization

Block 15: Database Integration

• Connecting with SQLite/MySQL/PostgreSQL
• ORM with SQLAlchemy or Tortoise ORM
• Alembic for migrations

Block 16: Authentication & Deployment

• JWT and OAuth2
• Role-based access
• Deploying APIs with Gunicorn + Nginx + Docker
• Hosting on Heroku, Render, or Azure

Block 17: Building & Consuming APIs

• Consuming 3rd-party APIs using requests
• Handling rate limits, pagination, and OAuth tokens
• Building APIs with Swagger/OpenAPI documentation

Phase 5: Capstone Project & Portfolio Development (2 Weeks)

Block 18-19: Final Projects (Team/Individual)

• Choose one or more:

  • Data science model with dashboard
  • Full CRUD REST API with user authentication
  • Python script to automate business tasks (scraping, reporting)

Deliverables

• Clean, documented codebase
• Unit tests and CI (e.g., GitHub Actions)
• README with setup and usage instructions
• Deployed application link or video demo

Optional Add-ons

• Web scraping with BeautifulSoup and Selenium
• Python GUI apps with Tkinter or PyQT

Notes

• Each Block can be between 1 day to 1 week of time depending on developer's current level
• Current Level can be evaluated using Questionnaire Article