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πŸš€ My Data Science Learning Journey

Welcome to my personal Data Science knowledge base. This repository is a collection of my learning notes, practice exercises, and code snippets, designed for both my personal reference and to help fellow learners.


🚧 Work in Progress

Please note that I am actively building this repository. I will be adding files and notes to each module one by one as I progress through my learning roadmap. Some folders might be empty for now, but they will be filled soon!


πŸ“‚ Repository Structure

Below is the organized structure of my learning path:

My-Data-Science-Learning/
β”œβ”€β”€ πŸ“ Python/
β”‚   β”œβ”€β”€ πŸ“ basics_syntax_python_1.ipynb
β”‚   β”œβ”€β”€ πŸ“ data_cleaning_pandas_2.ipynb
β”‚   β”œβ”€β”€ πŸ“ array_operations_numpy_3.ipynb
β”‚   β”œβ”€β”€ πŸ“ data_visualization_matplot_4.ipynb
β”‚   └── πŸ“ Statistics_Scipy_5.ipynb
β”œβ”€β”€ πŸ“ SQL/
β”‚   └── πŸ“
β”œβ”€β”€ πŸ“ Advance_excel/
β”‚   └── 
β”œβ”€β”€ πŸ“ Power_BI/
β”‚   └── 
└── πŸ“ Tableau_dashboard/
    └── 

🎯 Objectives

  • Structured Learning: Documenting every step of my data science roadmap.
  • Quick Reference: A digital diary for core concepts in Numpy, Pandas, SQL, Statistics, Tableau, Power BI.
  • Knowledge Sharing: A helpful resource for beginners to follow along.

πŸ› οΈ Tech Stack

  • Programming: Python (NumPy, Pandas, Matplotlib, SciPy)
  • Database: SQL
  • BI Tools: Power BI, Tableau, Advance Excel

"Documenting the process, mastering the data." 🌟

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A structured collection of my Data Science learning notes and practice exercises, designed to serve as a personal reference and a helpful guide for fellow learners starting their journey.

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