Release v1.3.0 - January 05, 2025

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.

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Upgrade Steps

• No manual steps required.

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Breaking Changes

• None

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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

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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

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Bug Fixes

• None

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Performance Improvements

• None

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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.

Release v1.2.0 - December 07, 2025

Version 1.2.0 - December 07, 2025

Summary:
This release significantly improves the visual quality, readability, and overall professionalism of the repository documentation. Key enhancements include a fully redesigned badge section with cleaner alignment and consistent styling, updates to the “Recommended Tools” and “Why Choose This Repository” sections, and additional clarity and formatting improvements applied throughout the README.

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Upgrade Steps

• No manual steps required; this update is purely visual and documentation-based.

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Breaking Changes

• None

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New Features

• Updated: Recommended Tools section in the README

  • Added professional tool badges for Python, VS Code, Jupyter Notebook, and Google Colab
  • Enhanced overall table design for better readability and visual structure
  • Added clear, action-oriented descriptions for each tool

• Enhanced: “Why Choose This Repository” section

  • Rewrote content for improved clarity and structure
  • Removed emojis and special characters for a more professional tone
  • Added optional table-style and minimal-text alternatives for cleaner presentation
  • Improved consistency with the overall README formatting and style

• Improved: Markdown structure throughout the README for consistency

• Updated: Redesigned the badge section at the top of the README

  • Cleaned up the badge layout for a more balanced and visually aligned appearance
  • Fixed several badges that were not loading or displaying correctly
  • Grouped badges under clear categories (Social Metrics, Repo Health, Dev Tools, Commit Activity)
  • Added a more professional spacing and alignment using a centered <p> block
  • Ensured consistent sizing, color usage, and rendering across GitHub's dark and light themes

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Documentation Preview

1. README Visual Improvements

Before	After
Plain Markdown table with text-only tool descriptions.	Visually enhanced layout using badges, icons, and concise descriptions.

2. Badge Section Redesign

Before	After
Unaligned badges with inconsistent sizing, spacing, and missing icons.	Clean, centered badge block with fixed icons, consistent styling, and clearer grouping.

3. Contact List Redesign

Before	After
Original layout with basic formatting and limited visual structure	Updated layout with improved styling, alignment, and visual clarity.

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Refactor

• Replaced the basic Contact List with a premium table-style contact section.
  • Improved layout using badge-style labels and cleaner formatting.
  • Increased visual consistency with the rest of the README.
  • Enhanced readability and professionalism of the contact information block.

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Bug Fixes

Fixed GitHub Notebook Rendering Error (metadata.widgets issue)
A user reported that 2 of the Jupyter Notebooks failed to render on GitHub with the following message:

“There was an error rendering your Notebook: the 'state' key is missing from 'metadata.widgets'. Add 'state' to each, or remove 'metadata.widgets'.”

Cause:
Google Colab automatically injects a metadata.widgets block into .ipynb files.
GitHub’s notebook renderer does not support this widget metadata, leading to a rendering failure.

Resolution:
The invalid widget metadata was manually removed from the affected notebook.

Steps performed:

1. Opened the .ipynb file in VS Code as raw JSON
   (Right-click → Reopen Editor With → Text Editor)
2. Searched for all occurrences of "widgets" inside the metadata.
3. Deleted the entire "widgets" block, including all nested content and its trailing comma.
4. Saved the file and reopened it in Notebook mode.
5. Committed and pushed the cleaned notebook back to GitHub.

Result:
The notebook now renders correctly on GitHub with no errors.

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Performance Improvements

• Optimized Markdown rendering for improved readability on GitHub and mobile devices.

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Other Changes

• Improved tone and professional writing style across the README.
• Added a Python 3.x badge to highlight the repository’s supported runtime.
• Enhanced formatting consistency in multiple sections, including “Why Choose This Repository.”
• Established a formatting baseline for future documentation updates using badge-based and visual layouts.
• Added a new LinkedIn folder to organize social posts, including two new articles
  • Clean Data Builds Smart Models
  • The Art of Asking Better Data Questions
  • The Power of Simple Data Checks

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Release v1.1.0 - November 08, 2025

Version 1.1.0 (2025-11-08)

This release expands the repository's educational scope by introducing new coding resources, interactive data science notebooks, and a dedicated section highlighting published Medium articles. Minor typos in the main README file have also been fixed.

Upgrade Steps

• No action required for users; all additions are documentation and learning-based.

Breaking Changes

• None

New Features

• Added Advanced Python Interview Questions and Answers
• Added Python Engineering Design Principles
• Added Exploratory Data Analysis (EDA)
• Published new Medium article: 10 Must-Know Pandas Tricks Every Data Science Beginner Should Learn
• Published new Medium article: The First Step to Becoming a Data Scientist
• Improved Installation Tools section by removing outdated content and adding clearer explanations with direct hyperlinks to essential tools.
• Added Featured Articles on Medium section in the main README to showcase published programming and tech articles for increased engagement and visibility.

Bug Fixes

• Fixed several typos in the main README file.
• Corrected minor hyperlink issues and Markdown inconsistencies in documentation.

Performance Improvements

• Optimized Markdown and notebook readability for better GitHub preview and Colab execution.

Other Changes

• Beginner-friendly guides include hands-on examples in Google Colab for practical learning.
• Streamlined visuals and repository structure for a cleaner GitHub presentation.

Release v1.0.0 - October 04, 2025

New Additions

• Added Telling Stories With Data
• Added Advanced Python Interview Questions (Sept 21)
• Added Python Interview Questions (September Edition)
• Added 10 Must-Know Pandas Tricks for Data Science Beginners

Notes

• This release expands the Python Interview Prep and Data Science sections.
• All new materials include practical examples and explanations for better understanding.