• Introduction
• Data Visualization Techniques
• Technologies Used
• Acquiring Data from the Web

Data visualization is a powerful tool that enables us to represent complex data in a visual format, making it easier to interpret and comprehend. The biggest challenge is to gather the data in a format, that will be easy for visualization. Managing extracted data is an essential aspect of data-driven decision-making and insights.

Depending on what type of data you want to present, you might consider using specific chart visualization for given example. A heatmap might be a better option for data that shows where an archer had hit the target, than a simple line chart.

Here are a couple of most popular chart examples:

• Line charts and area charts
• Bar charts and column charts
• Pie charts and donut charts
• Scatter plots and bubble charts
• Heatmaps and treemaps
• Geographic maps and choropleth maps

Most popular data visualization tool is MS Excel, as it's common, easy to use and is great for creating simple charts. I wouldn't recommend it as a data managment tool (that is extracting chunks of data), as it's hard to preserve the cohesion between the data.

For data managment and extraction I would suggest using:

• Python (Google Collab is a great starting point for begginers)
• JavaScript
• MySQL

There are many free datasets online that you can use for your projects. Here are some of the most popular websites providing datasets:

• Kaggle
• Data.gov
• Google Dataset Search (Although it's not a dataset repository itself, it helps you discover datasets hosted on various websites)

If you want to make your own dataset for specific purposes, you can scrape the data you want from the Internet. Bare in mind that some websites such as Amazon.com, Twitter.com etc. have special protection for programms trying to accuire data from them.