My Python Data Science Journey Welcome to my repository! This space is dedicated to documenting my learning journey through the world of data science, with a primary focus on mastering Python's core data manipulation and analysis libraries: NumPy and Pandas.

The goal of this repository is not just to store code, but to build a structured, real-world portfolio of my skills, showing my progress from fundamental concepts to more complex machine learning applications.

🎯 Goals Master the Fundamentals: Gain a deep and practical understanding of NumPy for numerical computation and Pandas for data manipulation.

Build a Portfolio: Create a collection of clean, well-commented, and practical examples that showcase my abilities.

Explore Machine Learning: Apply data science libraries to solve real-world problems and build introductory machine learning models.

Track Progress: Visibly track my development and create a log of my journey for myself and others to see.

📂 Repository Structure

.
├── .gitignore
├── README.md
├── numpy_examples/
│   ├── 01_array_basics.py
│   ├── 02_indexing_and_slicing.py
│   └── 03_math_and_stats.py
├── pandas_examples/
│   ├── 01_series_and_dataframes.py
│   ├── 02_data_loading_and_cleaning.py
│   └── 03_data_aggregation.py
└── ml_projects/
    └── 01_simple_linear_regression.py

numpy_examples/: Contains scripts focused on core NumPy functionalities.

pandas_examples/: Holds scripts dedicated to Pandas operations, from data structures to data cleaning.

ml_projects/: A place for more comprehensive projects where NumPy and Pandas are used for building and training simple machine learning models.

🗺️ Learning Roadmap This is the path I am following. Each new concept and project will be added to the repository as it's completed.

➡️ NumPy Deep Dive

Understanding the ndarray object.

Array creation, manipulation, and broadcasting.

Indexing, slicing, and boolean operations.

Mathematical and statistical functions.

➡️ Pandas Mastery

Working with Series and DataFrame objects.

Importing data from various sources (CSV, Excel).

Data cleaning: handling missing values, duplicates, and incorrect data types.

Data wrangling: grouping (groupby), merging, and reshaping data.

Time series analysis.

➡️ Introductory Machine Learning

Using scikit-learn with NumPy and Pandas.

Project 1: Simple Linear Regression.

Project 2: Classification (e.g., Logistic Regression or KNN).

Data visualization with Matplotlib or Seaborn.

📫 Connect with Me I'm always open to feedback and collaboration. Feel free to connect with me!

LinkedIn: https://www.linkedin.com/in/safwansaba/