The Checklist (Good practices for good programmers)

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The Checklist (Good practices for good programmers)

Contents

• Software Engineering
• Software Development good practices
• A.I/+D.S Projects

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Software Engineering

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Software Development good practices

• Project Settings:
  • .gitignore
  • Editor, IDE settings:
    • VSCode:
      • .editorconfig (or similar setting)
  • Virtual Environment:
    • Python:
      • Poetry, Venv, Virtualenv
  • Project Metadata:
    • Python:
      • pyproject.toml
      • requirements.txt
  • Project Management:
    • Python:
      • taskipy
  • Hooks:
    • Python:
      • pre-commit
  • CI/CD:
    • CI (Continuous Integration):
      • GitHub Action
    • CD (Continuous Delivery):
  • Project LICENSE
• Code Better:
  • Lint:
    • Python:
      • Black
      • Blue
      • Flake8
      • isort
      • reorder_python_imports
  • Documentation:
    • Python:
      • MkDocs:
        • mkdocs-material
        • mkdocstrings
        • mkdocstrings-python
        • mkdocs-macros-plugin
        • jinja2
      • ReadTheDocs
  • Exceptions:
    • Exceptions in Python
    • Exception Handling in C++
  • Object-Oriented Programming:
    • OOP in Python
    • OOP in C++
  • SOLID
  • Design Patterns:
  • Software Testing:
    • Pytest
  • Debugger:
    • Debugger in Python
• Web Development:
  • RESTful API
• Parallel Programming & Concurrent Programming:
• Algorithms:
  • Design and Analysis of Algorithms
  • Data Structures

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Software Engineering

• Data Science/A.I Project management:
  • Data Science projects types (+steps)
  • CRoss Industry Standard Process for Data Mining (CRISP-DM)
  • Data Pipeline

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A.I/+D.S Projects

• Statistics & Probability:
• Machine Learning:
• Deep Learning:
• Reinforcement Learning:
  • Good Practices (Tips & Tricks):
    • Data Preprocessing:
    • Feature Engineering:
    • Hyperparameter Tuning
    • Cross-Validation
    • Tips & Tricks:
      • Training, Validation and Testing sets
      • Review: Overfitting, Underfitting & O trade-off viés-variância
      • Learning Curves para Machine Learning
      • Data Storytelling

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Rodrigo Leite da Silva - drigols

• Software Engineering:
  • Data Science/A.I Project management:
    • Data Science projects types (+steps)
    • CRoss Industry Standard Process for Data Mining (CRISP-DM)
    • Data Pipeline
• Software Development & Engineering:
  • Algorithms:
    • Design and Analysis of Algorithms
    • Data Structures
  • Clean Code:
    • Docstrings:
      • Docstrings in Python
    • Exceptions:
      • Exceptions in Python
    • Object-Oriented Programming:
      • Object-Oriented Programming
    • Design Patterns:
    • Software Testing:
    • Debugger:
      • Debugger in Python
      • Debugger in C++
  • Parallel Programming & Concurrent Programming:
  • Web Development:
    • RESTful API:
      • REST (Constraints)
      • HTTP Methods
      • Status Codes
• Artificial Intelligence + Data Science Projects:
  • Data Preprocessing & Feature Engineering:
  • Statistics & Probability:
    • Descriptive Statistics
    • Probability
    • Random Variables
  • Machine Learning Algorithms::
    • Linear Regression
    • Ridge & Lasso Regression (L1 + L2)
    • Elastic Net
    • **Cross-Validation: K-Fold & Stratified K-Fold
    • Hyperparameter Tuning:
      • RandomizedSearchCV & GridsearchCV
    • Classification:
      • Logistic Regression
    • Ensemble Methods
  • Tips & Tricks:
    • Training, Validation and Testing sets
    • Review: Overfitting, Underfitting & O trade-off viés-variância
    • Learning Curves para Machine Learning
    • Data Storytelling
• Tools:
  • Project Settings:
    • General:
      • .gitignore
      • Hooks (pre-commit)
    • Python:
      • Virtual Environment (Venv, Virtualenv, Poetry)
      • requirements.txt
      • pyproject.toml
      • setup.cfg
      • Code Style:
        • reorder_python_imports
        • Black
        • Flake8
    • Editor, IDE settings:
      • VSCode:
        • .editorconfig

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Rodrigo Leite da Silva - drigols