Data Analysis Toolkit for Food and Nutrition Sciences

Data Analysis Toolkit for Food and Nutrition Sciences 🦛

Welcome to the Data Analysis Toolkit for Food and Nutrition Sciences, a comprehensive resource for project students mastering data analysis in nutrition research. This toolkit features a number Jupyter notebooks across six modules (and some examples), rendered as interactive HTML tutorials using Quarto and hosted on GitHub Pages. Run the notebooks in Google Colab with one click or explore the rendered tutorials below. WIt covers Python basics, data handling, statistical analysis, advanced methods, and qualitative research.

Quick Start 🚀

View the Site: Browse tutorials at https://ggkuhnle.github.io/data-analysis-projects/.
Run in Colab: Use the “Open in Colab” badges below to run notebooks in the cloud.
GitHub: Clone or fork the repo at github.com/ggkuhnle/data-analysis-projects.

Modules 📚

Explore the six modules, each with notebooks you can view as HTML or run in Colab:

Infrastructure
Set up Python, Jupyter, and Quarto for data analysis.
Explore Module
Programming Python
Master Python syntax and programming basics.
Explore Module
Data Handling
Import, clean, and transform nutrition datasets.
Explore Module
Data Analysis
Visualise data and build regression models.
Explore Module
Advanced Topics
Dive into Bayesian methods, SQL, and dashboards. Explore Module
Qualitative Research
Analyse text data for nutrition studies.
Explore Module
Mini projects Some mini projects, e.g. RCT Explore Module

Get Involved 🧑‍💻

Clone the Repo:

git clone https://github.com/ggkuhnle/data-analysis-projects.git
cd data-analysis-projects

Set Up Locally:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Install Quarto: quarto.org.

Render Locally:
```
rm -rf _site/
quarto render
```

License 📝

Created by Gunter Kuhnle. Licensed under MIT.

Happy analysing! 🚀