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Developer Learning

This repository tracks my programming study journey toward becoming a software developer.

The goal is not only to complete exercises, but to build strong fundamentals through:

  • reading official and book-based material
  • writing code by hand
  • solving exercises
  • reviewing and refactoring code
  • writing notes
  • practicing Git and GitHub workflow
  • building small projects over time

Current Focus

Python Fundamentals

Current study path:

Python Crash Course
Exercism Python Track
Custom drills
Code review and refactoring
Small projects

The current Python phase focuses on:

  • Python syntax
  • variables
  • strings
  • numbers
  • functions
  • lists
  • loops
  • ranges
  • slicing
  • tuples
  • basic testing with assert
  • clean code habits

Repository Structure

developer-learning/
├── python/
│   ├── python-crash-course/
│   │   ├── chapter-01-getting-started/
│   │   ├── chapter-02-variables-simple-data-types/
│   │   ├── chapter-03-introducing-lists-exercises/
│   │   └── chapter-04-working-with-lists/
│   │
│   ├── exercism/
│   │   └── python/
│   │
│   ├── drills/
│   │   ├── week-01/
│   │   └── week-02/
│   │
│   └── reviews/
│
├── README.md
└── .gitignore

Study Workflow

For each topic, I follow this cycle:

read → type examples → solve exercises → test → review → refactor → commit

1. Read

Read the relevant chapter, documentation, or exercise instructions.

2. Type examples

Type examples manually instead of only copying them.

3. Solve exercises

Write a separate Python file for each exercise when possible.

4. Test

Run the files from the terminal.

Example:

python3 python/python-crash-course/chapter-04-working-with-lists/pizzas.py

For simple practice tests:

python3 python/drills/week-01/test_week_01_drills.py

For Exercism tests:

python3 -m pytest -o markers=task exercise_test.py

5. Review

Check:

  • Does the code run?
  • Are variable names clear?
  • Is indentation correct?
  • Are there unnecessary print() calls?
  • Should a function use return instead?
  • Is there repeated logic?
  • Can I explain every line?

6. Refactor

Improve code after it works.

7. Commit

Commit meaningful progress.

git add .
git commit -m "Complete Chapter 4 loop exercises"
git push

Python Study Sources

The main learning sources are:

  • Python Crash Course
  • Official Python Documentation
  • Exercism Python Track
  • Custom drills and review questions

Official Python documentation:

https://docs.python.org/3/

Exercism Python Track:

https://exercism.org/tracks/python

Naming Conventions

Python files should use lowercase letters and underscores.

Good:

simple_message.py
name_cases.py
exercise_3_1_names.py
counting_to_twenty.py

Avoid:

SimpleMessage.py
name-cases.py
Exercise 3.py

Python Style Rules

Basic style habits:

  • use 4 spaces for indentation
  • use clear variable names
  • keep files organized
  • avoid excessive blank lines
  • use comments when they explain why something exists
  • use snake_case for variables and functions
  • use uppercase names for constants

Examples:

favorite_language = "Python"
expected_bake_time = 40
EXPECTED_BAKE_TIME = 40

Use constants when a value should stay fixed:

EXPECTED_BAKE_TIME = 40
MINUTES_IN_HOUR = 60

Git Workflow

Check project status:

git status

Stage changes:

git add .

Commit changes:

git commit -m "Describe the work completed"

Push to GitHub:

git push

Recommended commit message examples:

git commit -m "Complete Chapter 3 list exercises"
git commit -m "Add Chapter 4 exercises README"
git commit -m "Refactor Week 1 drills"
git commit -m "Complete Currency Exchange exercise"

Generated Files

Do not commit generated Python cache files.

Ignored examples:

__pycache__/
*.pyc
.DS_Store
.venv/
.env

If cache files appear, remove them:

find . -name "*.pyc" -delete
find . -name "__pycache__" -type d -exec rm -r {} +

Reviews

Weekly or chapter reviews are stored in:

python/reviews/

A review should include:

  • what I studied
  • exercises completed
  • what I understand well
  • what still feels weak
  • bugs I fixed
  • code I refactored
  • questions for review
  • next focus

Long-Term Roadmap

The long-term goal is to build toward full-stack development.

Planned areas:

Python fundamentals
Django
PostgreSQL
JavaScript review
React
Testing
Git workflow
Small projects
Portfolio projects
Job preparation

Definition of Good Progress

Good progress means:

  • code runs
  • exercises are completed
  • mistakes are reviewed
  • code is cleaned up
  • concepts can be explained
  • GitHub is up to date
  • weak areas are identified
  • practice continues consistently

The goal is not speed.

The goal is clean, tested, explainable code.