Data Science — Learning Repository

This repository is a complete, beginner-to-intermediate Data Science learning collection containing math & statistics notes, plotting examples, and hands-on Python notebooks. The goal is to gather small, focused examples and datasets you can run locally to learn common data science workflows: exploratory data analysis, visualization, statistics, and basic machine learning.

Table of contents

• Overview
• Repository structure
• Quick start
• How to use the notebooks
• Learning roadmap and recommended order
• Contributing
• License
• Author / Contact

Overview

This repository organizes short, focused notebooks and datasets for learning Data Science concepts. It's intentionally lightweight and notebook-first so you can open examples, run cells, and tweak code. It includes:

• Math and Statistics lecture notebooks and notes
• Plotting examples (bar, box, line) with included datasets
• Python notebooks covering language basics and common patterns

Use this repo as a self-study kit or a teaching resource.

Repository structure

Top-level layout (files and folders you can expect):

• Readme.md — this file (project overview and instructions)
• Math/Statistics/ — lecture notebooks for statistics (e.g. Stats_Lec1_&_2.ipynb)
• Ploting/ — plotting examples organized into subfolders:
  • Bar/ — bar charts and Shoppings.csv
  • Box/ — box plots and datasets like Housing.csv, Products.csv
  • Line/ — line plots and Housing.csv
• Python/ — short Python notebooks covering variables, control flow, functions, arrays, dictionaries and basics of packaging

Files you'll likely open first:

• Math/Statistics/Lec1/Stats_Lec1_&_2.ipynb — introductory statistics notes and code
• Ploting/Bar/script.ipynb — quick bar plot examples using Shoppings.csv
• Python/01-varable-if.ipynb — Python basics notebook

If you add new notebooks or datasets, update this README with a short line describing them.

Quick start

Prerequisites: Python 3.8+ and Git. Recommended to use a virtual environment.

1. Clone the repository:

git clone https://github.com/zeeshanali90233/Data-Science.git
cd "Data-Science"

2. Create and activate a virtual environment (Windows PowerShell):

python -m venv .venv
.\.venv\Scripts\Activate.ps1

3. Install recommended packages (this repository includes a minimal requirements.txt):

pip install -r requirements.txt

4. Start Jupyter Notebook or Jupyter Lab and open the notebooks:

jupyter notebook
# or
jupyter lab

How to use the notebooks

• Open a notebook in Jupyter, run the cells from top-to-bottom, and experiment by changing small pieces of code.
• Notebooks contain short explanations and runnable examples. Look for # TODO cells where exercises may be placed.
• Datasets are small CSV files included in the Ploting/ subfolders. Load them with pandas:

import pandas as pd
df = pd.read_csv('Ploting/Bar/Shoppings.csv')

Learning roadmap and recommended order

For newcomers, a suggested path through this repo:

1. Python/01-varable-if.ipynb — Python basics
2. Python/02-logical-loops-func.ipynb — control flow and functions
3. Python/03-arrays-dict-oop.ipynb — data structures and basic OOP
4. Math/Statistics/Lec1/Stats_Lec1_&_2.ipynb — introductory statistics
5. Ploting/* notebooks — visualization practice with real CSVs

After that, try combining what you learned: load a dataset, compute summary statistics, and produce plots.

Suggested exercises

• Compute summary statistics (mean, median, quartiles) for one dataset.
• Create a simple exploratory data analysis (EDA) notebook: show distributions, correlations, and a short write-up of findings.
• Transform an example into a small ML task: predict a numeric column with a simple regression model using scikit-learn.

Contributing

Contributions are welcome. A few guidelines:

• Add one small, focused notebook or dataset per PR.
• Include a short description at the top of the notebook explaining what the example demonstrates.
• Keep datasets small (under 1–2 MB) or link to external sources instead of storing very large data here.
• If you add a new folder, update Readme.md with a one-line description.

Steps to contribute:

1. Fork the repository on GitHub.
2. Create a branch for your change.
3. Add your notebook or dataset and commit.
4. Open a pull request describing the change.

License

This repository does not include a license file by default. If you want to make the repository open-source, consider adding an LICENSE (for example, the MIT License). If you want me to add a standard license file, say which license you prefer and I will add it.

Author / Contact

• GitHub: https://github.com/zeeshanali90233

If you want changes to the README (different sections, badges, or a different tone), tell me what style you prefer and I'll update it.