This repository contains the assignments, notebooks, resources, and supporting files for the Google Data Analytics Professional Certificate (Coursera). It is organized to make it easy to browse each module’s materials, run the Jupyter notebooks locally, or open them directly on GitHub/Binder.

Repository: https://github.com/ADVAIT135/GOOGLE-DATA-ANALYTICS

• About
• Repository structure
• Quick start — view notebooks
• Run locally (recommended)
• Suggested dependencies
• Tips for working with the course materials
• Contributing
• License
• Author / Contact

This repo collects the exercises and project work for the Google Data Analytics Professional Certificate. The notebooks demonstrate common data analysis steps using Python and Jupyter, such as data cleaning, exploration, visualization, and basic analysis techniques relevant to the course.

• Browse notebooks directly on GitHub (click any .ipynb file).
• Use NBViewer to render notebooks: https://nbviewer.org/ — paste the raw GitHub notebook URL.
• Launch an interactive session with Binder (if you add configuration files like environment.yml or requirements.txt and a binder badge).

1. Clone the repo:

git clone https://github.com/ADVAIT135/GOOGLE-DATA-ANALYTICS.git
cd GOOGLE-DATA-ANALYTICS

2. Create a Python environment (conda recommended):

conda create -n gda python=3.10 -y
conda activate gda

Or with venv:

python -m venv venv
source venv/bin/activate   # macOS / Linux
venv\Scripts\activate      # Windows

3. Install dependencies:

• If there is a requirements.txt:

pip install -r requirements.txt

• If not, install commonly used packages:

pip install jupyterlab notebook pandas numpy matplotlib seaborn scikit-learn openpyxl

4. Launch Jupyter Lab or Notebook:

jupyter lab
# or
jupyter notebook

Open the notebooks from the browser interface and run cells in order.

(Common for data analytics notebooks)

• jupyterlab, notebook
• pandas, numpy
• matplotlib, seaborn
• scikit-learn (for basic ML examples)
• openpyxl (reading/writing Excel files)
• ipywidgets (optional, for interactive widgets)

If you want a reproducible environment, create environment.yml or requirements.txt and add it to the repo.

• Always run notebooks from top to bottom to ensure cell outputs and variable states are correct.
• If a notebook expects dataset files, check the data/ folder or update file paths to point to local copies.
• Use nbconvert to export notebooks to PDF/HTML for submission or sharing:

jupyter nbconvert --to html path/to/notebook.ipynb

• Consider .gitignore for large datasets to avoid pushing big files to GitHub.

Contributions and corrections are welcome. Suggested workflow:

1. Fork the repo.
2. Create a branch: git checkout -b fix/some-issue
3. Make changes (fix typos, improve notebooks, add requirements).
4. Commit and push, then open a pull request with a clear description of changes.

Please avoid committing large raw datasets; instead add scripts to download or generate sample data.

This project is licensed under the MIT License — see the LICENSE file for details.

• Repository owner: ADVAIT135