Python Developer Guide

Welcome

This guide gives a compact roadmap for learning Python, key topics every developer should know, curated learning resources (courses and YouTube channels), guidance on career paths that use Python, a primer on using Python for AI/ML, and a short note about Python's creator.

Important Python Topics

Core knowledge areas to master, ordered roughly from beginner to advanced.

• Python Basics: syntax, variables, data types, control flow, functions, error handling.
• Data Structures: lists, tuples, sets, dicts, comprehensions, iterators, generators.
• OOP: classes, inheritance, magic methods, composition, dataclasses.
• File I/O & Serialization: open, context managers, CSV, JSON, pickle.
• Testing: unittest, pytest, test-driven development basics.
• Packaging & Environments: venv, pip, requirements.txt, pyproject.toml, setuptools, poetry.
• Async & Concurrency: threading, multiprocessing, asyncio, concurrent.futures.
• Networking & Web: HTTP requests (requests), building APIs (Flask, FastAPI), web scraping (BeautifulSoup, Scrapy).
• Databases: SQL (sqlite, PostgreSQL), ORMs (SQLAlchemy, Django ORM), NoSQL basics.
• Data Science & Visualization: numpy, pandas, matplotlib, seaborn, data cleaning.
• Machine Learning & AI: scikit-learn, TensorFlow, PyTorch, model evaluation.
• DevOps & Automation: scripting, cron, Docker basics, CI/CD tools.
• Security & Best Practices: input validation, secure dependencies, logging, observability.

Curated Learning Resources

Mix of structured courses and YouTube channels to follow — free and paid options.

Career Paths with Python

Python is widely used across industries. Common career paths:

• Backend Web Developer: Build APIs and services using Flask, Django, or FastAPI. Learn databases, authentication, and deployment.
• Data Scientist / Analyst: Work with pandas, numpy, visualization, statistics, SQL, and reporting.
• Machine Learning Engineer / AI Researcher: Train and deploy ML models using scikit-learn, TensorFlow, PyTorch. Focus on model lifecycle and productionization.
• DevOps / SRE: Use Python for automation, tooling, CI/CD pipelines, and cloud automation (boto3 for AWS).
• Automation & Scripting: Quick wins in companies—automating workflows, data pipelines, and ETL tasks.
• Embedded & IoT: MicroPython and CircuitPython for constrained devices.

How to prepare

• Build a portfolio of small projects and deploy at least one end-to-end app.
• Open-source contributions and GitHub presence help recruiters.
• Learn system design basics, testing, and writing maintainable code.
• Practice interview-style coding and domain-specific tasks (SQL, ML case studies).

AI & Machine Learning with Python

Python is the dominant language for AI and ML because of its rich ecosystem. Typical workflow:

1. Data collection and cleaning (pandas, numpy)
2. Exploratory data analysis and visualization (matplotlib, seaborn, plotly)
3. Feature engineering
4. Modeling (scikit-learn for classical ML; TensorFlow / PyTorch for deep learning)
5. Evaluation and hyperparameter tuning
6. Deployment (Flask, FastAPI, TorchServe, TensorFlow Serving, Docker)

Key libraries and tools:

• numpy, pandas — numerical work and data frames
• scikit-learn — baseline models and pipelines
• TensorFlow, Keras, PyTorch — deep learning
• MLFlow, Weights & Biases — experiment tracking
• Jupyter / JupyterLab — interactive exploration

Quick tips:

• Start with small datasets and well-known benchmarks (Iris, MNIST).
• Focus on reproducibility: seed random generators, document environment and dependencies.
• Learn to profile and optimize data pipelines before scaling models.

About Python & Its Creator

Python was created by Guido van Rossum in the late 1980s and first released in 1991. Guido led the language as its "Benevolent Dictator For Life (BDFL)" until stepping down in 2018. Python's design emphasizes readability, simplicity, and a strong standard library.

Key milestones:

• 1991 — Python 0.9.0 released
• 2000 — Python 2.0 introduced (many features, now end-of-life)
• 2008 — Python 3.0 released (incompatible changes, now standard)
• Python today is maintained by the Python core development team and a large open-source community.

Starter Projects

Project ideas to build skills:

• CLI todo app with file persistence and tests.
• REST API for a simple resource and deploy to Heroku or a VPS.
• Data analysis notebook exploring a public dataset (Kaggle).
• Image classification model using transfer learning (PyTorch / TF).
• Automation scripts for repetitive tasks (file renaming, scraping).