Python Roadmap (2025 Edition)



1. Basics of Python Programming


Install Python, use VS Code or PyCharm


Learn syntax, variables, data types


Master conditionals (if/else), loops (for/while)


Input/output, functions, indentation, scope



2. Data Structures and Algorithms


Lists, tuples, sets, dictionaries


Stacks, queues, heaps, linked lists


Sorting, searching, recursion, Big-O notation


Use collections and itertools modules



3. Object-Oriented Programming (OOP)


Classes, objects, constructors


Inheritance, encapsulation, polymorphism


__init__, __str__, dunder methods


Abstract classes (abc), interfaces, composition



4. File Handling and Error Management


Read/write text, JSON, CSV, Excel files


Use with open(), try/except/finally


Logging and custom exceptions



5. Modules and Packages


Import system, creating custom modules


pip, virtual environments (venv, poetry)


__init__.py, sys.path, standard library



6. Pythonic Programming and Best Practices


List comprehensions, generators, lambda functions


Decorators, context managers (with)


Type hints (typing), f-strings, *args, **kwargs


Follow PEP8 and use linters (flake8, black)



7. Working with APIs and Web


requests, httpx, json, pydantic


REST APIs, authentication, query params


Web scraping using BeautifulSoup, Selenium



8. Databases and Storage


SQLite, PostgreSQL, MySQL (sqlite3, sqlalchemy)


NoSQL: MongoDB (pymongo)


ORM basics and migrations



9. Advanced Python Concepts


Closures, decorators, generators, coroutines


Multithreading vs multiprocessing


Async programming with asyncio, aiohttp


Memory management and garbage collection



10. Libraries by Domain


Data Science: numpy, pandas, matplotlib, seaborn, scikit-learn


AI/ML: tensorflow, pytorch, transformers


Web Dev: flask, django, fastapi


Automation: selenium, pyautogui, schedule


DevOps: docker, fabric, invoke



11. Testing and Debugging


Unit testing with unittest, pytest


Mocking, fixtures, code coverage


Debugging with pdb, logging



12. Project Building and Git


Follow project structure: src/, tests/, README.md


Version control with Git/GitHub


Build projects: CLI tools, REST APIs, dashboards, web scrapers



13. Deployment and CI/CD


Package and deploy Python apps


Use Docker, GitHub Actions, Heroku, Railway


Write Dockerfile, requirements.txt, .env files



14. Bonus Topics


Build desktop apps with tkinter or PyQt


Use Python with IoT or Raspberry Pi


Create Python libraries and publish to PyPI


Comments

Post a Comment

Popular posts from this blog

I Tried Waking Up at 4AM for 30 Days — Here’s What Actually Happened

Why I Did This Let me be clear: I’m not a morning person. Before this experiment, my “morning” usually started around 8:30 AM — maybe 9 if I hit snooze (which I often did). But after reading so many stories about CEOs, athletes, and creators who wake up at 4AM, I got curious: What would happen if I tried it for a full month? No hacks. No special gear. Just pure willpower, a loud alarm, and a goal to see if I could change my life by changing my wake-up time.   The Rules Wake up at 4:00 AM every day — including weekends No hitting snooze Use the time between 4–7AM for intentional activities: journaling, working out, reading, or deep work In bed by 9:30 PM (or at least try) I tracked how I felt each week and what actually got done during those quiet hours. Week-by-Week Breakdown Week 1: Pain, Regret, and Coffee The first three days were brutal. My body was confused, and my brain felt like mush. I dragged myself out of bed and stumbled to the kitchen for coffee. I tried to read or jour...
Read more

10 Everyday Apps Using AI You Didn’t Know About

Image
AI isn’t just in robots or self-driving cars. It’s already powering the apps you use every day — silently improving your experience behind the scenes. From the playlists you love to the autocorrect that saves your typos — AI is everywhere. Let’s uncover 10 everyday apps that secretly use AI to work their magic  1. Instagram – Filters & Explore Feed AI at Work: Recognizes faces, enhances images, curates content. Examples: AR filters detect facial features. “Explore” tab shows posts you’re likely to enjoy. Caption & comment filtering uses NLP to hide offensive content. ---  2. Spotify – Music Recommendations AI at Work: Analyzes your listening habits to suggest music. Examples: "Discover Weekly" uses machine learning to find songs similar to your taste. AI even predicts your mood from song patterns! --- 3. Gmail – Smart Reply & Smart Compose AI at Work: Natural Language Processing (NLP) helps you write faster. Examples: Auto-suggests email replies like "Sounds ...
Read more

AI Engineers can be quite successful in this role without ever training anything

This is how they can succeed :  In today’s AI-driven world, many assume that success in artificial intelligence engineering requires developing complex machine learning models from the ground up. However, that’s not always the case. In fact, AI engineers can be highly successful without ever training a model themselves, thanks to the rise of pre-trained models, accessible APIs, and modern AI system design patterns. Here’s how. 1. Leveraging Pre-Trained Models Modern AI development rarely starts from a blank slate. Today’s AI engineers often use pre-trained models such as GPT (language), CLIP (vision-language), or Whisper (speech-to-text) as building blocks. Why It Works: Saves time, compute, and complexity Avoids the need for large training datasets Focus shifts from model training to application development Example: Using OpenAI’s GPT-4 to build a customer support chatbot or document summarizer without modifying the model itself. --- 2. Prompt Engineering Prompt engineering has em...
Read more