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Release v1.3.0 - January 05, 2025

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@Tanu-N-Prabhu Tanu-N-Prabhu released this 05 Jan 15:45
· 49 commits to master since this release
v1.3.0
66ec22e

Version 1.3.0 - January 5, 2026

Summary:
This release expands the repository’s documentation and thought-leadership content by adding a new reflective Python article to the LinkedIn folder and introducing a dedicated Release Notes badge in the README for improved navigation and discoverability of repository updates.
In addition, this release improves the internal structure of the Python learning materials by standardizing folder naming conventions and reorganizing core educational content for clarity and scalability.

Upgrade Steps

  • No manual steps required.

Breaking Changes

  • None

New Features

  • Added: New LinkedIn article to the repository

    • “I Wrote Python for 5 Years. My Biggest Bug Wasn’t in the Code.”
    • Explores long-term Python development experience, personal growth, and non-technical challenges
    • Focuses on mindset, decision-making, and career lessons rather than syntax or tooling
    • Written in a reflective, story-driven format aligned with professional LinkedIn audiences
    • Placed inside the LinkedIn folder for structured content organization

  • Added: Release Notes badge to the README

    • Introduced a dedicated badge labeled “Release Notes”
    • Links directly to the GitHub Releases page for quick access to version history
    • Styled consistently with existing README badges using <img>-based Shields.io format
    • Improves documentation discoverability and repository professionalism

Refactor

  • Refactored: Standardized Python learning folder structure

    • Renamed Src to 01_Python_Basics for improved beginner clarity
    • Renamed and corrected the Exercise folder to 02_Python_Exercises_And_Practice
    • Moved all exercise content under the main Python directory
    • Renamed the interview preparation folder to Python_Coding_Interview_Prep
    • Applied consistent Title_Case_With_Underscores naming across Python folders
    • Introduced numeric prefixes to reflect a clear learning progression

  • Refactored: Reorganized quizzes within the Python learning structure

    • Renamed the Quiz folder to 03_Quizzes
    • Moved quizzes inside the main Python/ directory
    • Aligned quizzes with the early-stage learning flow
    • Improved consistency with numbered, curriculum-style folder organization

  • Refactored: Consolidated Lists content into Python Basics

    • Moved the Lists notebook into 01_Python_Basics
    • Removed the standalone Lists folder to eliminate duplication
    • Improved conceptual grouping of core Python data structures
    • Simplified navigation for beginners

  • Refactored: Consolidated core data structure examples into Python Basics

    • Moved String-related notebooks into 01_Python_Basics
    • Moved Tuple-related notebooks into 01_Python_Basics
    • Improved conceptual grouping of fundamental Python topics
    • Reduced fragmentation of beginner-level content

  • Refactored: Reorganized HackerRank exercises under Python directory

    • Moved HackerRank exercise files into the main Python/ folder
    • Aligned external practice problems with the repository’s learning structure
    • Improved discoverability of practice material for learners

  • Refactored: Removed duplicate practice material

    • Deleted the Built-in Functions Practice Problems file
    • Retained the canonical version inside Python Exercises to avoid duplication

  • Refactored: Improved and reorganized variable scope documentation

    • Fixed typos in the Global and Local Variables notebook
    • Moved the notebook into 01_Python_Basics
    • Improved accuracy, readability, and placement of foundational Python concepts

  • Refactored: Introduced a dedicated Data Science structure outside core Python

    • Created a top-level Data_Science/ directory to separate domain-specific content from Python fundamentals
    • Established clear conceptual boundaries between language learning and applied data workflows
    • Improved long-term scalability and curriculum-style navigation

  • Refactored: Organized pandas-based learning materials

    • Moved pandas-focused notebooks out of the Python directory
    • Renamed and standardized pandas file naming conventions
    • Grouped pandas content under Data_Science/pandas/ for clarity and reuse

  • Refactored: Structured data preprocessing content

    • Organized preprocessing-related notebooks (including one-hot encoding) under Data_Science/preprocessing/
    • Improved alignment with real-world data science pipelines
    • Reduced confusion between Python syntax and data preparation techniques

  • Refactored: Added a dedicated Exploratory Data Analysis (EDA) section

    • Created an eda/ folder under Data_Science/
    • Moved EDA-focused content into a clearly defined analytical phase
    • Reflected industry-standard data science workflows

  • Refactored: Organized data acquisition and API-based notebooks

    • Moved external API usage (e.g., Google Trends API) out of Python fundamentals
    • Introduced a Data_Science/data_sources/ section
    • Clarified the distinction between API consumption and Python language concepts

  • Refactored: Added a Data Science introduction section

    • Created Data_Science/00_Introduction/ for conceptual onboarding content
    • Placed high-level, narrative learning material separately from implementation notebooks
    • Improved beginner-friendly entry points into the Data Science learning path

Bug Fixes

  • None

Performance Improvements

  • None

Other Changes

  • Strengthened the repository’s documentation navigation and usability.
  • Improved visibility of release history for contributors and readers.
  • Continued standardization of badge-based documentation patterns.
  • Reinforced a professional, maintainable release workflow for future updates.
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