Best Selling Music Artists of all Time

Final Project - Data Visualization - A.Y. 2022/2023

Group: TATANKA

- Edoardo Pastorino, 5169595

- Mahyar Sadeghi Garjan, 5283082

About Data and Project

This website, built with GitHub pages service, provides several visualization charts and graphs, based on the extracted information from a dataset about the best selling music artist of all time. All the visualizations here are created for educational purposes in Data Visualization course in @UniGe, academic year 2022/2023, made by the TATANKA group.

In the subpages relative to the different sections it is possible to find different charts with various techniques of data visualization, according to different kind of analysis, to demonstrate the information which gives the user the possibility to draw conclusions from them.

The data used for the creation of our charts are taken from a Kaggle Dataset, that provides information about music bands and artists, including their nationalities, their incomes, their period of activity and their musical genres.

Project Structure:

.

├── components

│   ├── footer.html

│   ├── navbar.html

│   └── sidebar.html

├── README.md

├── css

│   └── style.css

├── data

│   └── ...

├── iimages

│   └── ...

├── js

│   ├── genreAnalysis
    
│       ├── genreChart1.js

│       ├── genreChart2.js

│       ├── genreChart3.js

│       └── genreChart4.js

│   ├── geoAnalysis
    
│       ├── geoChart1.js

│       ├── geoChart2.js

│       └── geoChart4.js

│   ├── TimeAnalysis
    
│       ├── timeChart1.js

│       ├── timeChart2.js

│       ├── timeChart3.js
        
│       ├── timeChart4.js

│       └── timeChart5.js
        
│   └── utils

│       ├── mapLegend.js

│       └── radarChart.js

├── scripts

│   ├── dataManipulation.py

│   └── T_A_chart2.py
    
├── about.html

├── genre.html

├── geo.html

├── index.html

└── time.html

Basic Instruction

It is possible to clone the repo with the following command:

git clone https://github.com/mahyarsadeghi/mahyarsadeghi.github.io.git

and after launch a simple python local server for seeing the resulting website:

python -m http.server

Useful Links

• D3.js
• Used Data
• GitHub pages of the website