✅ Phase 1: Python Fundamentals
Python is a versatile and beginner-friendly programming language. Mastering Python fundamentals is essential for data analysis, machine learning, and AI development.
- Environment Setup: Install Python, set up an IDE like VS Code or PyCharm, and configure virtual environments using venv or conda.
- Basic Syntax & Data Types: Learn variables, data types (int, float, string, boolean), and perform operations using operators.
- Control Structures: Understand conditional statements (if-else) and loops (for, while) for iterative tasks.
- Functions & Modules: Create reusable functions using def and organise code with modules and packages using import.
- Object-Oriented Programming (OOP): Implement classes, objects, inheritance, and polymorphism to build scalable applications.
- Error Handling: Use try-except blocks to handle exceptions and ensure program stability.
- Working with Libraries: Install and manage libraries using pip, and explore essential packages like NumPy, Pandas, and Matplotlib.
🚀 Ready to start? Download Python Here and kickstart your coding journey!
✅ Phase 2: Advanced Python Concepts
Take your Python skills to the next level by learning advanced concepts essential for building robust applications. Master file management, handle exceptions gracefully, and write cleaner, more efficient code using decorators and generators.
-
📂 File Handling:
Learn to read, write, and append data using Python’s built-in open() function. Manage files using
different modes like r, w, and a.
Example:
with open('file.txt', 'r') as file: data = file.read() -
⚠️ Exception Handling:
Prevent program crashes using try-except blocks. Handle errors gracefully and use finally for
clean-up tasks.
Example:
try: x = int("abc") except ValueError: print("Invalid input") -
🔎 Regular Expressions (Regex):
Perform complex pattern matching using Python’s re module. Useful for validating emails, phone numbers,
and other text data.
Example:
re.search(r'\d+', 'Age: 25') -
🎩 Decorators:
Modify the behaviour of functions using @decorators. They enhance code readability and reusability.
Example:
@log_decorator def greet(): print("Hello!") -
⚙️ Generators:
Efficiently iterate over large datasets using yield instead of storing values in memory. Generators are
memory-efficient and used in data processing.
Example:
def gen(): yield 1; yield 2
🚀 Ready to explore more? Check out the official Python Documentation.
✅ Phase 3: Web Development with Python
Leverage the power of Python for web development using frameworks like Flask and Django. Create dynamic web applications, build and consume REST APIs, and deploy your projects to the web using platforms like Heroku or AWS.
-
🌐 Frameworks: Flask vs Django
- Flask: Lightweight and flexible, ideal for small projects and APIs.
- Django: Powerful and feature-rich, perfect for large-scale applications with built-in authentication
and admin panels.
Example (Flask):
from flask import Flask, jsonify app = Flask(__name__) @app.route('/hello') def hello(): return jsonify({"message": "Hello, World!"}) app.run() -
📡 Build and Consume REST APIs
Design and develop RESTful APIs using Python. Learn to handle HTTP requests, JSON data, and API
authentication.
Example (API Endpoint):
GET /api/users→ Returns a list of users. -
🚀 Deployment
Deploy your web application using platforms like Heroku, AWS, or Render. Understand
containerization using Docker and manage cloud services effectively.
Example (Heroku Deployment Command):
heroku create my-python-app
🚀 Ready to build your first Python web app? Start with Flask Documentation or explore Django Official Site.
✅ Phase 4: Data Science & Machine Learning
Start your journey into data science by mastering essential Python libraries. Manipulate and analyse data using NumPy and Pandas, visualise insights using Matplotlib and Seaborn, and explore powerful machine learning algorithms using Scikit-Learn.
-
📊 Data Manipulation with NumPy & Pandas
NumPy provides support for multi-dimensional arrays and mathematical operations. Pandas is used for data
manipulation and analysis using DataFrames and Series.
Example (Pandas):
import pandas as pd data = {'Name': ['John', 'Alice'], 'Age': [25, 30]} df = pd.DataFrame(data) print(df) -
📈 Data Visualization with Matplotlib & Seaborn
Visualise data to uncover trends and patterns. Use Matplotlib for basic plotting and Seaborn for detailed
statistical visualisation.
Example (Seaborn):
import seaborn as sns sns.histplot(data=df, x='Age', kde=True) -
🤖 Machine Learning with Scikit-Learn
Build machine learning models using supervised and unsupervised algorithms. Perform tasks like classification,
regression, and clustering.
Example (Linear Regression):
from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X_train, y_train)
🚀 Ready to dive in? Explore the official docs: - NumPy - Pandas - Matplotlib - Seaborn - Scikit-Learn
✅ Phase 5: Automation and Scripting
Python's simplicity and versatility make it an excellent choice for automating repetitive tasks, extracting data, and building efficient scripts. Master automation using libraries like Selenium and BeautifulSoup for web scraping, along with data manipulation tools.
-
⚙️ Task Automation with Python
Automate mundane tasks like renaming files, sending emails, or generating reports using Python scripts and
scheduling tools like Task Scheduler or CRON.
Example:
import os for file in os.listdir('./files'): print(f'Renaming {file}') -
🌐 Web Scraping using Selenium or BeautifulSoup
Extract data from websites for analysis using BeautifulSoup for static sites and Selenium for
dynamic content rendered using JavaScript.
Example (BeautifulSoup):
from bs4 import BeautifulSoup import requests response = requests.get('https://example.com') soup = BeautifulSoup(response.text, 'html.parser') print(soup.title.text) -
📊 Data Extraction & Manipulation
Extract data from CSVs, JSON, and Excel files using Pandas. Perform data cleaning, analysis, and
manipulation for further insights.
Example (Pandas):
import pandas as pd df = pd.read_csv('data.csv') print(df.head())
🚀 Ready to automate your tasks? Get started with: - Python OS Module - BeautifulSoup Documentation - Selenium Guide
✅ Phase 6: DevOps and Deployment
DevOps bridges the gap between development and operations, ensuring faster and more reliable software delivery. Learn to containerize applications using Docker, orchestrate deployments with Kubernetes or AWS, and automate CI/CD pipelines using GitHub Actions.
-
🐳 Containerization with Docker
Package applications with their dependencies using Docker containers. Docker ensures consistent deployment
across different environments.
Example (Dockerfile):
FROM python:3.12 WORKDIR /app COPY . /app RUN pip install -r requirements.txt CMD ["python", "app.py"]Command to Build and Run Docker Container:
docker build -t my-app . && docker run -p 5000:5000 my-app -
🚀 Deploying with Kubernetes or AWS
Use Kubernetes for container orchestration, ensuring efficient scaling and management of applications. Deploy
on cloud platforms like AWS using Elastic Kubernetes Service (EKS).
Example (Kubernetes Deployment YAML):
apiVersion: apps/v1 kind: Deployment metadata: name: my-app spec: replicas: 3 selector: matchLabels: app: my-app template: metadata: labels: app: my-app spec: containers: - name: my-app image: my-app:latest ports: - containerPort: 5000 -
⚙️ Automating with GitHub Actions (CI/CD)
Implement Continuous Integration and Continuous Deployment using GitHub Actions. Automate testing, building,
and deploying your applications.
Example (GitHub Action YAML):
name: CI/CD Pipeline on: [push] jobs: build: runs-on: ubuntu-latest steps: - name: Checkout Code🎯 Start your Python development journey with confidence and keep building projects to strengthen your skills!