What Skills Make a Great Python Developer in 2026

Fundamentals — Daily Study Plan:
7:00 AM — Wake up, review the plan (15 min)
8:00 AM — Read Python docs and tutorials (45 min)
9:00 AM — Practice exercises (2 hours)
   - Week 1: Syntax, types, control flow
   - Week 2: Data structures
   - Week 3: OOP & modules
   - Week 4: Standard library
12:00 PM — Lunch break
1:00 PM — Build small projects (1.5 hours)
3:00 PM — Review and note gaps
4:00 PM — Practice problems (Codewars, LeetCode easy)
6:00 PM — Review progress, plan tomorrow
8:00 PM — Read code from open source
10:00 PM — Wind down

Week 1 checklist:
□ Variables, types, operators
□ Conditionals and loops
□ Functions and arguments
□ List / dict / set / tuple
□ Comprehensions

Week 2 checklist:
□ File I/O
□ Exceptions (try/except/finally)
□ Modules and packages
□ Virtual environments
□ pip basics

Week 3 checklist:
□ Classes and objects
□ Inheritance and polymorphism
□ Dunder methods
□ Dataclasses
□ Type hints basics

Week 4 checklist:
□ os, sys, pathlib
□ datetime
□ json, csv
□ re
□ logging

Frameworks & Tooling — Daily Study Plan:
7:00 AM — Wake up, review the plan (15 min)
8:00 AM — Read framework docs (45 min)
9:00 AM — Build features in a project (2 hours)
   - Week 5: First API endpoints
   - Week 6: Write tests, fix bugs
   - Week 7: Set up CI pipeline
   - Week 8: Dockerize the app
12:00 PM — Lunch break
1:00 PM — Review pull requests, read source code (1.5 hours)
3:00 PM — Refactor and improve
4:00 PM — Explore edge cases
6:00 PM — Review progress, plan tomorrow
8:00 PM — Read blog posts / changelogs
10:00 PM — Wind down

Week 5 checklist:
□ Routes, views, models (or FastAPI equivalent)
□ Request validation
□ Authentication basics
□ Database integration
□ API documentation

Week 6 checklist:
□ pytest fixtures
□ Unit tests for business logic
□ Integration tests for endpoints
□ Coverage reports
□ Debugging with pdb / logging

Week 7 checklist:
□ Branching strategy
□ Pull request workflow
□ GitHub Actions workflow
□ Pre-commit hooks
□ Automated tests in CI

Week 8 checklist:
□ pyproject.toml or setup.py
□ Poetry or pip-tools
□ Dockerfile
□ .env and secrets
□ Reproducible local setup

Data, AI & Production — Daily Study Plan:
7:00 AM — Wake up, review the plan (15 min)
8:00 AM — Read docs and papers (45 min)
9:00 AM — Build a data / ML feature (2 hours)
   - Week 9: ETL pipeline with pandas
   - Week 10: Train an ML model
   - Week 11: Add async and caching
   - Week 12: Add metrics and tracing
12:00 PM — Lunch break
1:00 PM — Benchmark and profile code (1.5 hours)
3:00 PM — Read open-source implementations
4:00 PM — Write documentation
6:00 PM — Review progress, plan tomorrow
8:00 PM — Follow AI / Python updates
10:00 PM — Wind down

Week 9 checklist:
□ pandas DataFrames & joins
□ NumPy vectorization
□ SQL joins and window functions
□ Data cleaning and validation
□ ETL script

Week 10 checklist:
□ scikit-learn pipeline
□ Model evaluation
□ Feature engineering
□ Calling an LLM API
□ Save and load model

Week 11 checklist:
□ asyncio basics
□ Concurrency vs parallelism
□ Redis / in-memory cache
□ Background jobs
□ Load testing with locust

Week 12 checklist:
□ Structured logging
□ Metrics (Prometheus / statsd)
□ Tracing (OpenTelemetry)
□ Health checks
□ Deployment with Docker