Steps to Follow in the Python Developer Roadmap

If you're in tech, Python is a skill that can take you far. But where do you start, and how do you progress? Having mentored developers and switched careers into tech myself, I've put together a roadmap that's helped many navigate their Python journey. Here's a breakdown of key areas to focus on as you level up your Python skills: 1. Core Python    Start with the basics - syntax, variables, and data types. Then move on to control structures and functions. This foundation is crucial. 2. Advanced Python    Once you're comfortable with the basics, dive into decorators, generators, and asynchronous programming. These concepts will set you apart. 3. Data Structures    Get really good with lists, dictionaries, and sets. Then explore more advanced structures. You'll use these constantly. 4. Automation and Scripting    Learn to manipulate files, scrape websites, and automate repetitive tasks. This is where Python really shines in day-to-day work. 5. Testing and Debugging    Writing tests and debugging efficiently will save you countless hours. Start with unittest and get familiar with pdb. 6. Package Management    Understanding pip and virtual environments is crucial for managing projects. Don't skip this. 7. Frameworks and Libraries    Depending on your interests, explore web frameworks like Django, data science libraries like Pandas, or machine learning tools like TensorFlow. 8. Best Practices    Familiarize yourself with PEP standards and stay updated on Python enhancements. Clean, readable code is invaluable. Remember, the key isn't just learning syntax - it's applying what you learn to real projects. Start small, but start building. What area of Python are you currently focusing on?

💻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 → 𝗙𝗿𝗼𝗺 𝗭𝗲𝗿𝗼 𝘁𝗼 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 If you’re starting today (or restarting), here’s a crisp path + resources you can actually follow. Save this 🧠 𝟬) 𝗦𝗲𝘁𝘂𝗽 (𝗪𝗲𝗲𝗸 𝟬) • Install: Python 3.12+, VS Code (or PyCharm) • Essentials: pip, venv, Jupyter, Git + GitHub 𝟭) 𝗖𝗼𝗿𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 (𝗪𝗲𝗲𝗸𝘀 𝟭–𝟰) • Syntax, data types, loops, functions, modules • Files, errors/exceptions, list/dict/set comprehensions • Mini-projects: CLI to-do app, CSV cleaner, unit converter 𝟮) 𝗗𝗮𝘁𝗮 & 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗪𝗲𝗲𝗸𝘀 𝟱–𝟳) • NumPy, pandas, matplotlib → EDA basics • Projects: Sales dashboard, KPI tracker, A/B test simulator 𝟯) 𝗪𝗲𝗯 & 𝗔𝗣𝗜𝘀 (𝗪𝗲𝗲𝗸𝘀 𝟴–𝟵) • HTTP, REST, JSON, requests • FastAPI/Flask: build a tiny API (CRUD + pagination) • Project: “Public API explorer” + docs 𝟰) 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 (𝗪𝗲𝗲𝗸 𝟭𝟬) • SQL (joins, window functions), SQLAlchemy/psycopg2 • Project: ETL pipeline → API → DB → dashboard 𝟱) 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗦𝗰𝗿𝗶𝗽𝘁𝗶𝗻𝗴 (𝗪𝗲𝗲𝗸 𝟭𝟭) • Schedules, argparse/click, logging, pathlib • Project: Daily report bot (pulls data → emails summary) 𝟲) 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗣𝗮𝗰𝗸𝗮𝗴𝗶𝗻𝗴 (𝗪𝗲𝗲𝗸 𝟭𝟮) • pytest, fixtures, coverage, type hints (mypy) • Package your tool (pyproject.toml) + versioning 𝟳) 𝗖𝗵𝗼𝗼𝘀𝗲 𝗮 𝗧𝗿𝗮𝗰𝗸 (𝗪𝗲𝗲𝗸𝘀 𝟭𝟯+) 𝗗𝗮𝘁𝗮/𝗠𝗟: scikit-learn, feature engineering, model eval, ML pipelines 𝗕𝗮𝗰𝗸𝗲𝗻𝗱: FastAPI, auth, caching, Celery/Redis, Docker, CI/CD 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻/𝗗𝗲𝘃𝗢𝗽𝘀: Bash + Python, IaC basics, cloud functions 𝗛𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝘁 • 60–90 mins/day → one small feature at a time • Ship weekly: post your repo link + a 60-sec demo • Learn by teaching: write a short “what I learned” note If you want the 𝗰𝗵𝗲𝗰𝗸𝗹𝗶𝘀𝘁 𝘃𝗲𝗿𝘀𝗶𝗼𝗻 of this roadmap, comment “𝗥𝗢𝗔𝗗𝗠𝗔𝗣” and I’ll share the template. Fox Hunt AI #Python #LearnPython #DataScience #MachineLearning #MLOps #100DaysOfCode #DevOps #BackendDevelopment #FastAPI #Pandas #NumPy #SQL #APIs #ETL #OpenSource #CodingJourney #CareerSwitch #TechCareers #DataAnalytics #SoftwareEngineering #BigData #FoxHunt #DataPipelines #CloudEngineering #DataOps #Python #Spark #Kafka #TechRoadmap #CareerGrowth #DataEngineer #MLOps #AWS #BigQuery #foxhunt

Are you planning to learn Python or already into it? This roadmap will help you guide in your career. Please remember not all roadmaps are suitable for everyone! We all have all pur journeys! 🔹 Learn the Basics: Lay a strong foundation with fundamental concepts including: - Basic Syntax: Getting familiar with the language structure. - Variables & Data Types: Understanding different types of data storage. - Conditionals: Mastering decision-making in your code. - Lists, Tuples, Sets, Dicts: Manipulating data collections efficiently. - Functions & Built-in Functions: Writing modular and reusable code. - Type Casting & Exceptions: Handling data type conversions and errors gracefully. 🔹 Data Structures and Algorithms: Delve into the core of programming with concepts like: - Arrays & Linked Lists: Managing data elements systematically. - Heaps, Stacks, Queues: Exploring specialized data storage. - Hash Tables: Unleashing the power of key-value pairs. - Binary Search Trees: Navigating and organizing data efficiently. - Recursion: Solving complex problems through self-referential functions. - Sorting Algorithms: Mastering essential sorting techniques. 🔹 Advanced Topics: Elevate your Python prowess by diving into advanced concepts, including: - Regular Expressions (RegEx): Pattern matching for text manipulation. - Lambdas: Writing concise anonymous functions. - Classes & Inheritance: Creating object-oriented code structures. - Methods & Dunder Methods: Enhancing class functionality. - PyPI, Pip, Conda: Managing packages and dependencies. - List Comprehensions & Generator Expressions: Condensing code for efficiency. - Programming Paradigms: Exploring different approaches to coding. - Builtin & Custom Iterators: Navigating data streams seamlessly. 🔹 Learn a Framework: Extend your skills with popular Python frameworks: - Pyramid, Flask, Django: Building robust web applications. - Synchronous & Asynchronous Programming: Handling concurrency effectively. - Tornado, aiohttp, gevent, Sanic, Fast API: Exploring diverse asynchronous options. 🔹 Test Your Apps: Ensure the quality of your creations with effective testing tools: - doctest, nose, pytest, unittest/pyUnit: Validating your code's functionality. What do you like to add?

𝗣𝘆𝘁𝗵𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 (𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝘁𝗼 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱) 𝗪𝗲𝗲𝗸 𝟭 – 𝗣𝘆𝘁𝗵𝗼𝗻 𝗕𝗮𝘀𝗶𝗰𝘀 Start with Python fundamentals. Learn: ↳ Variables and Data Types ↳ Conditionals and Loops ↳ Functions Resource: https://lnkd.in/dT6J5cpg 𝗪𝗲𝗲𝗸 𝟮 – 𝗢𝗯𝗷𝗲𝗰𝘁-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 (𝗢𝗢𝗣) Dive into OOP. Understand: ↳ Classes and Objects ↳ Inheritance ↳ Encapsulation and Polymorphism Resource: https://lnkd.in/gJq_NSQD 𝗪𝗲𝗲𝗸 𝟯 – 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 & 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 Study core data structures. Practice: ↳ Lists, Sets, Dictionaries ↳ Stack, Queue, Linked List ↳ Sorting and Searching Algorithms Resource: https://lnkd.in/g8q9ccZ9 𝗪𝗲𝗲𝗸𝘀 𝟰 – 𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Learn web frameworks. Start with: ↳ Flask (For Beginners) ↳ Django (For Advanced) Resource: Flask: https://lnkd.in/gzEbRWGa Django: https://lnkd.in/gHsGSfwC 𝗪𝗲𝗲𝗸 𝟱 – 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜𝘀 Develop APIs using Flask/Django. Key Concepts: ↳ CRUD Operations ↳ Authentication ↳ JSON Data Handling Resource: https://lnkd.in/g_t7H65f 𝗪𝗲𝗲𝗸𝘀 𝟲 – 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Explore database integration with Python. ↳ SQL Databases (SQLite/PostgreSQL) ↳ NoSQL Databases (MongoDB) Resource: SQLite: https://lnkd.in/gmJ6GvqC MongoDB: https://lnkd.in/g73KwDHv 𝗪𝗲𝗲𝗸 𝟳 – 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 & 𝗗𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 Focus on testing your Python code. Learn: ↳ Unit Testing (unittest/pytest) ↳ Debugging Techniques (pdb module) Resource: Pytest: https://lnkd.in/gfFMQKaN unittest: https://lnkd.in/gGdZ6TqC 𝗪𝗲𝗲𝗸𝘀 𝟴 – 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 Learn advanced Python topics. Deep dive into: ↳ Decorators ↳ Generators ↳ Context Managers Resource: https://lnkd.in/g-isg3ux 𝗪𝗲𝗲𝗸𝘀 𝟵 – 𝗗𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Learn how to deploy Python applications. Explore: ↳ Deploying on Heroku ↳ Docker for containerization Resource: Heroku: https://lnkd.in/gSYkAzU2 Docker: https://lnkd.in/gGakbruK 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗦𝘁𝗮𝗴𝗲 (𝗪𝗲𝗲𝗸 𝟭𝟬 – 𝟭𝟭) Build and deploy 2-3 real-world projects based on: ↳ Web Applications (using Flask/Django) ↳ API Services ↳ Data Analysis Projects 𝗙𝗶𝗻𝗮𝗹 𝗪𝗲𝗲𝗸 – 𝗥𝗲𝗳𝗶𝗻𝗲 𝘆𝗼𝘂𝗿 𝗦𝗸𝗶𝗹𝗹𝘀 Focus on practicing coding challenges. Use: ↳ LeetCode ↳ HackerRank Resource: LeetCode: https://leetcode.com/ HackerRank: https://lnkd.in/gpwJcPvC --- 📕 400+ 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/gv9yvfdd 📘𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 : https://lnkd.in/gPrWQ8is 📙 𝗣𝘆𝘁𝗵𝗼𝗻 𝗟𝗶𝗯𝗿𝗮𝗿𝘆: https://lnkd.in/gHSDtsmA 📗 45+ 𝗠𝗮𝘁𝗵𝗲𝗺𝗮𝘁𝗶𝗰𝘀 𝗕𝗼𝗼𝗸𝘀 https://lnkd.in/ghBXQfPc

Roadmap for learning Python: Python is one of the most versatile programming languages today. From web development and automation to data science and machine learning, it almost feels like Python is everywhere. Whether you're automating repetitive tasks, building apps or ML models, mastering Python's fundamentals is essential. I received a copy of Modern Python Cookbook by Steven Lott. 𝗠𝘆 𝘁𝗵𝗼𝘂𝗴𝗵𝘁𝘀: It’s an excellent resource that offers clear, practical explanations, challenges and examples. Steven has decades of experience in Python and writing Python books, and that translates into a resource that is easy to absorb and level up your Python skills quickly. If you want to learn Python or level up your Python skills, I highly recommend that you consider this book. Grab your copy here: https://lnkd.in/geHWxCiV Now, let’s walk through the key areas you should focus on to become proficient with Python. This roadmap is a logical progression that builds upon itself. In saying that, there can be overlap between stages, and at times, things can be learned concurrently rather than sequentially if you feel that suits you better. 𝟭) 𝗗𝗮𝘁𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀 Data structures are the building blocks of software. Python’s built-in data structures like lists, dictionaries, sets, and tuples. Knowing when to use each one ensures optimal performance for specific tasks. 𝟮) 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 Learn to define functions with parameters, type hints, and recursion. This will make your code more reusable and maintainable. 𝟯) 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗳𝗹𝗼𝘄 Understand conditional statements (if, else, elif) and loops. These are the building blocks of logic in your code. 𝟰) 𝗘𝗿𝗿𝗼𝗿 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 Handle runtime errors gracefully using try, except, and finally blocks. This ensures your program can handle unexpected conditions without crashing. 𝟱) 𝗢𝗯𝗷𝗲𝗰𝘁-𝗼𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 (𝗢𝗢𝗣) Dive into OOP concepts such as classes, inheritance, and encapsulation to structure your code in a modular and maintainable way. 𝟲) 𝗧𝗲𝘀𝘁𝗶𝗻𝗴, 𝗹𝗼𝗴𝗴𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗱𝗲𝗯𝘂𝗴𝗴𝗶𝗻𝗴 Writing tests, logging events, and debugging are essential to maintaining high-quality code. 𝟳) 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝗶𝗲𝘀 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 Learn to manage dependencies and versions using tools like pip-tools. This is essential for maintaining consistent environments. 𝟴) 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺 Explore asyncio, multithreading, and multiprocessing to handle tasks efficiently and boost performance. 𝟵) 𝗗𝗲𝘀𝗶𝗴𝗻 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 Implement design patterns to create modular, scalable, and maintainable code that aligns with best practices. Following this roadmap will help you evolve from writing simple scripts to building robust, efficient applications. Whether it’s handling errors gracefully, managing dependencies, or mastering concurrency, these topics will elevate your Python skills to the next level.

Mastering Python: Your Roadmap to Success in 2025 Whether you're a beginner or aiming to specialize in advanced applications, Python continues to be one of the most versatile and in-demand programming languages. Here’s a structured Python Roadmap to guide your journey, from foundational concepts to real-world applications: 1. Start with the Basics Build your core with syntax, variables, data types, and control structures. This foundation is key to everything that follows. 2. Object-Oriented Programming (OOP) Understand how to design clean, scalable software using classes, inheritance, and powerful magic methods. 3. Data Structures & Algorithms (DSA) Critical for coding interviews and performance-driven applications. Learn arrays, trees, recursion, and sorting algorithms. 4. Package Managers Get comfortable with tools like pip, PyPi, and conda to manage your libraries and environments efficiently. 5. Advanced Python Concepts Master comprehensions, generators, decorators, and more to write efficient, Pythonic code. 6. Web Frameworks Explore Django, Flask, and FastAPI to build dynamic, secure web applications and APIs. 7. Automation Automate tedious tasks with file operations, web scraping, and GUI/network automation — a huge productivity boost. 8. Testing Learn unit and integration testing to build robust, error-free code. Test-Driven Development (TDD) can transform your workflow. 9. Data Science & Machine Learning Dive into powerful libraries like Pandas, Scikit-learn, and TensorFlow to analyze data and build AI models. #Python #LearningPath #CodingJourney #DataScience #WebDevelopment #PythonDeveloper #Automation #Programming #TechCareer #100DaysOfCode #DevCommunity

*** New to Python? *** ~ Python is a fantastic language to learn. Here’s a step-by-step roadmap to help you get started: 1. Understand the Basics: * Variables and Data Types * Basic Operators (Arithmetic, Comparison, Logical, etc.) * Control Flow (if-elif-else, for loops, while loops) * Functions (defining and calling) 2. Work with Data Structures: * Lists * Tuples * Dictionaries * Sets 3. Dive into Modules and Libraries: * Understanding import * Standard Libraries (like math, datetime, os) * Popular Third-Party Libraries (like requests, numpy, pandas) 4. File Handling: * Reading from and Writing to files * Working with CSV and JSON files 5. Error Handling: * Try, Except, Finally Blocks * Custom Exceptions 6. Object-Oriented Programming (OOP): * Classes and Objects * Inheritance * Polymorphism * Encapsulation 7. Understanding Modules and Packages: * Creating and Importing Modules * Using Packages 8. Web Scraping and APIs: * Basics of Web Scraping with BeautifulSoup * Interacting with APIs using requests 9. Building Projects: * Start with small projects like a to-do list or a calculator * Gradually move to more complex projects, such as a web app using Flask/Django or a data analysis project 10. Version Control with Git: * Basic Git Commands * Using GitHub for Collaboration 11. Testing: * Writing Unit Tests * Using Libraries like unittest or pytest 12. Continue Learning: * Join Python communities (like Reddit, Stack Overflow, GitHub) * Keep experimenting with new libraries and frameworks * Contribute to open-source projects ~ Python is a versatile and beginner-friendly language, perfect for diving into programming. With a structured approach to learning the basics, exploring data structures, mastering libraries, handling files, understanding object-oriented programming, and building real-world projects, you’ll be well on your way to becoming proficient. Remember, the key to mastering Python—or any skill—is persistence, practice, and a willingness to keep learning. The Python community is vast and supportive, so don’t hesitate to reach out, ask questions, and share your journey. --- B. Noted

If you want to build a career in tech in data, AI, analytics, ML, or automation, there’s one skill that opens more doors than anything else: 𝐏𝐲𝐭𝐡𝐨𝐧. It’s the backbone of almost everything we do in the data field, and the demand keeps growing, with Python roles routinely crossing 100K in the U.S. But here’s the truth: most learners jump between random tutorials and never build anything that sticks. So here’s the exact roadmap I’d follow if I were learning Python from scratch today. 𝐒𝐭𝐞𝐩 𝟏: 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐏𝐲𝐭𝐡𝐨𝐧 (4 𝐡𝐨𝐮𝐫𝐬) Hugo Bowne-Anderson starts you at zero. Variables, lists, functions - the foundations. But you're working with real datasets immediately. Not just theory. 𝐖𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐛𝐮𝐢𝐥𝐝: Basic data analysis scripts that actually do something useful from day one. 𝐋𝐢𝐧𝐤: https://lnkd.in/dXkdi5R5 𝐒𝐭𝐞𝐩 𝟐: 𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐏𝐲𝐭𝐡𝐨𝐧 (4 𝐡𝐨𝐮𝐫𝐬) The same instructor takes you deeper. Matplotlib for visualizations. Pandas basics. Logic and control flow. 𝐖𝐡𝐲 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: No context switching. Everything builds on what you just learned. The concepts actually stick. 𝐋𝐢𝐧𝐤: https://lnkd.in/d6NZzcXn 𝐒𝐭𝐞𝐩 𝟑: 𝐃𝐚𝐭𝐚 𝐌𝐚𝐧𝐢𝐩𝐮𝐥𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐩𝐚𝐧𝐝𝐚𝐬 (4 𝐡𝐨𝐮𝐫𝐬) Maggie Matsui shows you the reality of data work. Importing messy files. Cleaning. Filtering. Aggregating. 𝐓𝐡𝐞 𝐭𝐫𝐮𝐭𝐡: This is 80% of any data job. GroupBy, merge, pivot tables - you'll use these every single day. Link: https://lnkd.in/d5Ry-fxV 𝐒𝐭𝐞𝐩 𝟒: 𝐒𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐬𝐜𝐢𝐤𝐢𝐭-𝐥𝐞𝐚𝐫𝐧 (4 𝐡𝐨𝐮𝐫𝐬) George Boorman brings you into ML. Classification, regression, model evaluation. You're building predictive models that work. 𝐖𝐡𝐚𝐭 𝐬𝐮𝐫𝐩𝐫𝐢𝐬𝐞𝐝 𝐦𝐞: ML isn't magic. The library handles the complexity. You just need to know which tool fits which problem. Link: https://lnkd.in/d6DRxnbk 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚𝐂𝐚𝐦𝐩 𝐰𝐨𝐫𝐤𝐞𝐝: → Structured progression (no jumping between random tutorials) → Hands-on coding in every lesson → Real datasets, not toy examples → No setup friction - code directly in browser I went through this exact sequence. Now I build ML models. The path is clear. The tools are there. You just have to start with Step 1. 𝐏.𝐒. I share data analytics insights and career tips in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

I Thought I Knew Python… Until Production Humbled Me. APIs. Automation. Data pipelines. AI tooling. Python touches all of them. I took shortcuts. I jumped straight into frameworks. Then I noticed a pattern. The engineers who truly understood Python moved faster. They automated everything. They built internal tools. They integrated AI without friction. They build easily because they mastered the basics. So I stopped learning randomly. I went back to fundamentals. I combined theory with hands-on execution. 1️⃣ A clear learning roadmap → So I knew what to study and in what order. 2️⃣ DataCamp for structured learning → https://lnkd.in/ewHeiTrU It gave me guided progression, exercises, and immediate feedback. 3️⃣ Building small real projects → CLI tools → Simple APIs → Automation scripts → Small data transformations in notebooks Production exposes weak fundamentals really fast. That’s why I put together this roadmap. So you don’t have to learn this lesson the hard way.

If you’re in your university or aiming for a career in AI/ML - this is for you. (Also addressing some great feedback I received from my last post!) A few months ago, I had a short call with a university student trying to break into AI. We sketched out a realistic roadmap - not perfect, but focused. I’m sharing that updated version here, along with a few key lessons from the thoughtful feedback I got: Let’s be honest: University often gives you lots of theory (math, stats, proofs which are super important!). But what it doesn’t always give you is production-level practical experience. Here’s a roadmap to help balance both. 📘 If I had to start again today, I’d focus on just 2-3 things first: → Python + SQL + PyTorch → Cloud Basics (AWS or GCP) → Model Deployment (CI/CD + MLOps basics) But if you want the extended version - here’s a full roadmap, with some additions based on community feedback: 1️⃣ 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 → Python (non-negotiable) → Learn to write real code, not just notebooks → Get familiar with APIs (FastAPI / Flask) 2️⃣ 𝗖𝗹𝗼𝘂𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀 & 𝗠𝗟𝗢𝗽𝘀 → Start with AWS - ML certification or AI Practitioner are structured and beginner-friendly → Learn basics of CI/CD (GitHub Actions, etc.) → Try MLflow or SageMaker for simple experiments → Learn Docker and basic containerization → Terraform if you’re infra-curious 3️⃣ 𝗠𝗼𝗱𝗲𝗹 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 → PyTorch → XGBoost for tabular use-cases → Hugging Face basics for LLMs 4️⃣ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 (𝗬𝗘𝗦, 𝘁𝗵𝗲𝗼𝗿𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀!) → Understand core ML Algorithms and concepts: overfitting, regularization, loss functions → Get comfortable with mathematics, stats and linear algebra → Learn how to think about models, not just how to run them You don’t need all the math proofs, but intuition is key - especially if you’re aiming for long-term depth. 5️⃣ 𝗠𝗼𝗱𝗲𝗿𝗻 𝗔𝗜 𝗦𝘁𝗮𝗰𝗸 𝗔𝘄𝗮𝗿𝗲𝗻𝗲𝘀𝘀 → LLMs, RAG, Agents - awareness is enough at first → Focus more on how the tools solve problems than chasing every new framework 6️⃣ 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 → Pandas (always), Polars or DuckDB (bonus) → Spark for large-scale workflows 7️⃣ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 + 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 → Learn how to evaluate models in production (A/B testing, feedback loops) → Understand logging, monitoring, and debugging models post-deployment A final reminder: You don’t need to master every tool. What matters most is problem-solving mindset and adaptability - tools will change, fundamentals won’t. And yes - if vibe coding, asking “why” before “how,” and connecting technical work to business outcomes will take you further than any tool ever can. What would you add to this? 👇 Share your thoughts in the comments! - ♻️ Repost if you found it helpful 🤗 ➕ Follow me - Shantanu for Production AI - ML - MLOps content and Career tips!