Game Data Science

Magy Seif El-Nasr, Alessandro Canossa, Truong-Huy D Nguyen, Anders Drachen

Research output: Book/reportMonographResearchpeer-review

Abstract

This book is aimed at giving readers an introduction to the practical side of game data science and thus can be used a textbook for game analytics or game user research class or as a reference to self learners and enthusiasts. Game data science is a term that we use to denote a process composed of methods and techniques by which an analyst or a data scientist can make sense of data to allow decision makers in a game company to make informed decisions. This process involves: statistical analysis, visualization, abstraction of low-level data, machine learning and sequence data modeling. The book introduces different methods borrowing from different fields including human computer interaction, machine learning, and data science, focusing on methods and techniques used by both industry and researchers within the field of games. The book examples and case studies specifically focus on gameplay log data. The book takes a practical stance on the subject by discussing theoretical foundation, practical approaches, and delves deeply into the different techniques proposed and used through labs, examples, and comprehensive surveys of various case studies from both industry and academia. Topics range from simple approaches to more advanced ones. No prior knowledge is required. The book is developed to be self contained and can be used as a good way to introduce the reader to data science and how it is applied to the filed of games.
Original languageEnglish
PublisherOxford University Press
Number of pages416
ISBN (Print)9780192897879
ISBN (Electronic)9780191919466
DOIs
Publication statusPublished - 2021

Keywords

  • Game data science
  • game analytics
  • game visualization
  • player modeling

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