Data Analysis Series: An Overview of FIFA 19 Players Dataset
By: Efe Buyuk
Tags: Python, Data Science, Tableau, Football Analytics, Sql

Photo by Tevarak Phanduang on Unsplash

Intro

FIFA is one of the most popular football simulation video games of all times. I am sure that not only me but most of the people (I guess the majority would be males) probably played this game once in their life. Especially if you are a football fan, then you played this game not only once. I, as a football fan, have played FIFA’s series for a long period of time, especially during my school years, and never got bored. :)

If you are still interested in this article after reading the intro, then let’s continue…

About the Content

Before going any further, let me give you the content of this series so that you can also always click any link below which you might be interested in more. Otherwise, I suggest you read the series in order. :)

About This Series

This series is all about football! Football is a big part of our life whether we like it or not. However, some of us like me really like it! :) Because of its importance, football fans also play football video games in order to maintain and live its excitement over and over again. EA’s FIFA football simulation video games have a very long history in game industry and their FIFA series have been playing from many users. Recently, while I was doing my research about finding a good dataset, I found FIFA 19 players dataset on Kaggle and got very excited about it. Because I love football and my job in data space, I wanted to do my own analysis in this dataset.

In essence, I am going to use above mentioned FIFA 19 Complete Player Dataset and you can also check it from this link.

Data Storytelling for Your Data Analysis

Photo by Teemu Paananen on Unsplash

In this introductory article of the series, I would also like to emphasize the importance of data storytelling in data science or generally speaking in data space. Throughout the history, humankind loved stories. They were risen by stories and have still been by in that way. Stories are important because they make things interesting for people. There are mainly three parts in a story. First part is an introductory which gives people a brief content about the story which is going to be told. After, there is a middle or progress part that a storyteller shares interesting, frustrating or exciting information about the story itself or characters in it. In this way audiences become more open for listening rest parts of stories. Lastly, there is an end part in stories. In this last part, storytellers aim to change the perspectives of audiences as a result.

Who wants to listen a story which gives you no clarity and makes you no sense?

Data storytelling is not different than normal storytelling. It should still be a storytelling but as data scientists, you should use data of course in your case. You should use your data in a way that your audiences should clearly understand what you are trying to tell them in first look. The whole point of telling a story there is to make a point that people should understand the important situation which has been constructed and emphasized throughout the story.

Conclusion

FIFA 19 Complete Player Dataset will be examined in the next three articles of this series. I am going to use Tableau, SQL and Python in order to get my results and form my analysis. In future, I think it will be a good work if I can write a detailed article or a mini-series only about data storytelling because of its importance in data space.

Note: You can also check this blog post and some other interesting topics on my personal web page. Additionally, you can also find me in LinkedIn via this link.

Next: I Need a Striker for My Team! Who Is He Going to Be? Examining the Dataset in Tableau