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.