Visualization?

What does data visualization really mean? Data visualization describes the art of finding important values and significant figures from a large data set. In the case of scouting data, the number of individual data points can easily grow into the tens of thousands for just one tournament alone. While you might enjoy searching through enormous text files, there are generally more optimal ways to filter the data so that you can get the insights you want quickly.

How to Excel at Excel

A spreadsheet? Why would I use a spreadsheet? While there are certainly more pretty ways to look at your data, Excel is still a reliable tool which offers many benefits. The ability to easily populate graphs and calculated data points from a generated CSV is an easy way to create basic visualizations with little programming knowledge. .xlsx files are also widely shareable and non-technical persons are still able to make some modifications and utilize a spreadsheet. Even if you don’t want to create your own spreadsheets, Caleb Sykes spreadsheets are still a valuable resource. The primary methods of data visualization within Excel are graphs, PivotTables, and a basic filtered and sorted sheet. If you would like to see pretty graphs I would reccomend using a program such as Tableau, which will be explained more in depth later, however, for quickly filtering and looking at large sets of raw data, Excel is generally your best bet.

Tableau

Tableau is a software designed specifically with user friendly data visualization in mind. No more columns and rows, just a drag and drop interface to quickly create nice looking graphs and charts. Tableau is a great tool if you would like to look at a visualization of many different types and sets of data quickly and efficently to glean insights, however it is not great for data processing.

Anaconda

More in line with what a data scientist might use in industry, the Anaconda distribution of Python and R is a Python distribution created for data science. While these tools are in general able to process the data well, perform a much more complicated and in depth analysis of the data, and visualize the data easily with tools such as matplotlib, there is a much higher level of technical knowledge to effectively utilize these tools to their fullest potential. It is also much harder to share with non-technical team members or other teams, such as your alliance partners, who are not prepared to utilize these tools.