Magy Seif El-Nasr (Professor and Profe Vice Chair of Serious Games, Truong-Huy D. Nguyen (Software Engineer, Software Engineer, Google), Alessandro Canossa (Professor, Professor, The Royal Danish Academy
Game Data Science
Magy Seif El-Nasr (Professor and Profe Vice Chair of Serious Games, Truong-Huy D. Nguyen (Software Engineer, Software Engineer, Google), Alessandro Canossa (Professor, Professor, The Royal Danish Academy
Game Data Science
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Games Data Science delivers an excellent introduction to this new domain and provides the definitive guide to methods and practices of computer science, analytics, and data science as applied to video games.
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Games Data Science delivers an excellent introduction to this new domain and provides the definitive guide to methods and practices of computer science, analytics, and data science as applied to video games.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
- Produktdetails
- Verlag: Oxford University Press
- Seitenzahl: 416
- Erscheinungstermin: 14. Oktober 2021
- Englisch
- Abmessung: 238mm x 158mm x 25mm
- Gewicht: 860g
- ISBN-13: 9780192897879
- ISBN-10: 019289787X
- Artikelnr.: 62039666
- Verlag: Oxford University Press
- Seitenzahl: 416
- Erscheinungstermin: 14. Oktober 2021
- Englisch
- Abmessung: 238mm x 158mm x 25mm
- Gewicht: 860g
- ISBN-13: 9780192897879
- ISBN-10: 019289787X
- Artikelnr.: 62039666
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.
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
Chapter 13: Conclusions and Final Remarks
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
Chapter 13: Conclusions and Final Remarks
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
Chapter 13: Conclusions and Final Remarks
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
Chapter 13: Conclusions and Final Remarks