What is difference between analytics and data science? How does game analytics differ from regular analytics?
Data science includes data analytics, machine learning, and data cleansing/data preparation. Data scientists focus discovering the questions that are unasked, and exploring potential areas of study.
Data analytics is more about doing analysis on existing data sets and trying to answer questions that have already been asked. Analytics is concerned with capturing, organizing, and performing statistical analysis on large data sets to create immediately actionable insights.
Both data science and data analytics provide insights that can assist in key business decisions but the approach to uncovering those insights is different. Data science is better suited to a larger scope, whereas analytics thrives in a smaller, targeted one.
One of the fundamentals of game analytics is game telemetry. Telemetry is the act of gathering data remotely from a game that you can then perform analytics on. It is vital when developing the game to determine early on what measurements to track and how to make that data usable once needed. Telemetry data can be used to analyze game servers, mobile devices, user behavior, etc.
Analyzing this data can produce valuable game metrics like average daily users, average monthly new users, player retention, win rates etc, as well as provide insights into how users interact with the game.