Chapter 1: Game Data Science: an Introduction
Chapter 2: Data Pre-Processing
Chapter 3: Introduction to Statistics and Probability Theory
Chapter 4: Data Abstraction
Chapter 5: Visual Analytics of Game Data
Chapter 6: Clustering Methods in Game Data Science
Chapter 7: Supervised Learning in Game Data Science
Chapter 8: Model Evaluation and Validation
Chapter 9: Neural Networks
Chapter 10: Sequence Analysis of Game Data
Chapter 11: Advanced Sequence Analysis
Chapter 12: Social Network Analysis
Magy Seif El-Nasr is a Professor of Computational Media and Vice
Chair of Serious Games program at University of California at Santa
Cruz, where she also directs the Game User Interaction and
Intelligence (GUII) Lab. Dr. Seif El-Nasr earned her Ph.D. degree
from Northwestern University in Computer Science. Her work is
internationally known and cited in several game industry books.
Additionally, she has received several awards and recognition
within the game
research community. Truong-Huy Nguyen is currently working at
Google as a Software Engineer. Before making the move to the tech
industry, he was an assistant professor at the Department of
Computer and
Information Science at Fordham University, New York, NY. He
received his PhD in Computer Science from the National University
of Singapore. His research focuses on discovering how humans make
decisions and form strategies and tactics from behavioral data, as
well as building experimental and practical applications to
leverage such insights. His research work lies at the
cross-junction of machine learning, artificial intelligence, and
behavior analytics, with favorite applications being games
and robotics. Dr. Alessandro Canossa has been straddling between
the game industry and academia for many years. He has been
Assistant Professor at the IT University of Copenhagen, Associate
Professor at
Northeastern University in Boston and he's now Professor at the
Royal Danish Academy of Fine Arts. In his research, he employs
psychological theories of personality, perception, motivation and
emotion to design games with the purpose of investigating
individual differences in behavior among users of digital
entertainment. He's now involved with Modl.AI, a company providing
AI services to the game industry, where he's exploring how to
triangulate data-driven insights with surveys and lab
observations to advance the field of predictive analytics. Anders
Drachen, PhD, is Professor at the University of York and
Communications Director at the Department of Computer Science. He
is co-director of
the Digital Creativity Labs (digitalcreativity.ac.uk/), a UK
Digital Economy Hub and World Centre for Excellence. He is
recognized as one of the world's most influential people in
business intelligence in the Creative industries, and a core
innovator in the domain with 140+ publications across game
analytics and games user research. His work has assisted major
international game publishers, as well as SMEs, make better
decisions based on their data.
Game Data Science describes practical techniques to analyze user
data, which can be used to drive decision-making at multiple
levels. Analyzing user game data can offer designers and publishers
useful insights that inform decision-making processes from the very
early stages of game design, through the marketing process, to
potential patches and updates.
*Christopher Bartel, Metascience*
The book is certainly invaluable. Game Data Science offers a
practical guide to the currently available analytical tools. It
will establish itself as foundational work in the field.
*Christopher Bartel, Metascience*
The book is certainly invaluable. Game Data Science offers a
practical guide to the currently available analytical tools. It
will establish itself as a foundational work in the field.
*Christopher Bartel, Metascience*
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