Data-Science-Learning-Journey

My Data Science Learning Journey

Welcome to my structured learning repository. This is where I document my daily practice and progress in Data Science, focusing on Pandas, NumPy, and Data Visualization.

I follow a strict learning framework for every topic:
Objective → Concept → Code → Output → My Observation → What I Learned

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🐍 1. Pandas Library

Modules	Topic	Notebook Link	Key Learning
Module 1	Basics & Exploration	📓 View Notebook	head(), tail(), shape, info(), describe(), filtering, sorting
Module 2	Data Cleaning, Analysis & Feature Engineering	Learnings · Practice	Handling NaN,isnull(), fillna(), dropna(), astype(), groupby(), agg(), pd.cut()
Module 3	EDA Built Analytical thinking & Basic Analysis	Coming Soon	Boolean Filtering, Advanced filtering, (&-AND, OR), (~ Not), .loc[], .query(), Analysis - What, So What, Now What

🔢 2. NumPy Library

Module	Topic	Notebook Link	Key Learning
Module 1	Arrays & Basic Operations	Coming Soon	Creating arrays, slicing, broadcasting, operations

📊 3. Data Visualization (Matplotlib / Seaborn)

Module	Topic	Notebook Link	Key Learning
Module 1	Matplotlib Basics	Coming Soon	Line plots, bar charts, customization

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🚀 How to View the Notebooks

1. Browse the folders based on the topic.
2. Open the .ipynb files directly on GitHub (GitHub automatically renders the outputs).
3. Read my observations inside the notebook to understand my thought process.

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📬 Connect with Me

• Name: Kunwar Aryan
• LinkedIn: https://www.linkedin.com/in/kunwar-aryan-630b26358/
• GitHub: https://github.com/Aryan1727