As the capstone for my Google Data Analytics Certificate, I used RStudio to analyze 2 Kaggle datasets. One aggregated hundreds of thousands of ARAM games from 7 different regions into win rate and other champion stat data in ARAM for each region, and the other contains the innate attributes for each champion in League of Legends (links to both at the bottom of this section). My goal was to find out what truly makes a winning ARAM team.
Tools Used: RStudio, Google Slides
Objective: Assist a fictitious League of Legends organization competing in a fictitious international tournament in champion selection and gameplay strategies.
Process: Used RStudio to analyze and visualize ARAM data as well as compare it to champion attributes.
Outcome: Identified high impact champion and roles, and discovered the strength of playing for KDA over gold or CS.
Skills Applied: Data cleaning, visualization, trend analysis, presentation
This was my first attempt at applying data analysis to my gaming life. Using Excel tables and basic organization, I analyzed how Fire Mage spell usage and boss mechanics affected damage during a specific encounter.
Tools Used: Google Sheets
Objective: Understand how to optimize Fire Mage damage output on the Queen Ansurek encounter.
Process: Aggregated data from specific top Fire Mage players and used Google Sheet tables to track damage and casts at key times.
Outcome: Identified high impact abilities and rotational improvements.
Skills Applied: Data cleaning, basic aggregation, spreadsheet visualization