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| data science in gaming industry: Game Data Science Magy Seif El-Nasr, Truong-Huy D. Nguyen, Alessandro Canossa, Anders Drachen, 2021-09-30 Game data science, defined as the practice of deriving insights from game data, has created a revolution in the multibillion-dollar games industry - informing and enhancing production, design, and development processes. Almost all game companies and academics have now adopted some type of game data science, every tool utilized by game developers allows collecting data from games, yet there has been no definitive resource for academics and professionals in this rapidly developing sector until now. 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. It is the ideal resource for academic students and professional learners seeking to understand how data science is used within the game development and production cycle, as well as within the interdisciplinary field of games research. Organized into chapters that integrate laboratory and game data examples, this book provides a unique resource to train and educate both industry professionals and academics about the use of game data science, with practical exercises and examples on how such processes are implemented and used in academia and industry, interweaving theoretical learning with practical application throughout. |
| data science in gaming industry: Data Analytics Applications in Gaming and Entertainment Günter Wallner, 2019-07-11 The last decade has witnessed the rise of big data in game development as the increasing proliferation of Internet-enabled gaming devices has made it easier than ever before to collect large amounts of player-related data. At the same time, the emergence of new business models and the diversification of the player base have exposed a broader potential audience, which attaches great importance to being able to tailor game experiences to a wide range of preferences and skill levels. This, in turn, has led to a growing interest in data mining techniques, as they offer new opportunities for deriving actionable insights to inform game design, to ensure customer satisfaction, to maximize revenues, and to drive technical innovation. By now, data mining and analytics have become vital components of game development. The amount of work being done in this area nowadays makes this an ideal time to put together a book on this subject. Data Analytics Applications in Gaming and Entertainment seeks to provide a cross section of current data analytics applications in game production. It is intended as a companion for practitioners, academic researchers, and students seeking knowledge on the latest practices in game data mining. The chapters have been chosen in such a way as to cover a wide range of topics and to provide readers with a glimpse at the variety of applications of data mining in gaming. A total of 25 authors from industry and academia have contributed 12 chapters covering topics such as player profiling, approaches for analyzing player communities and their social structures, matchmaking, churn prediction and customer lifetime value estimation, communication of analytical results, and visual approaches to game analytics. This book’s perspectives and concepts will spark heightened interest in game analytics and foment innovative ideas that will advance the exciting field of online gaming and entertainment. |
| data science in gaming industry: Game Analytics Magy Seif El-Nasr, Anders Drachen, Alessandro Canossa, 2013-03-30 Developing a successful game in today’s market is a challenging endeavor. Thousands of titles are published yearly, all competing for players’ time and attention. Game analytics has emerged in the past few years as one of the main resources for ensuring game quality, maximizing success, understanding player behavior and enhancing the quality of the player experience. It has led to a paradigm shift in the development and design strategies of digital games, bringing data-driven intelligence practices into the fray for informing decision making at operational, tactical and strategic levels. Game Analytics - Maximizing the Value of Player Data is the first book on the topic of game analytics; the process of discovering and communicating patterns in data towards evaluating and driving action, improving performance and solving problems in game development and game research. Written by over 50 international experts from industry and research, it covers a comprehensive range of topics across more than 30 chapters, providing an in-depth discussion of game analytics and its practical applications. Topics covered include monetization strategies, design of telemetry systems, analytics for iterative production, game data mining and big data in game development, spatial analytics, visualization and reporting of analysis, player behavior analysis, quantitative user testing and game user research. This state-of-the-art volume is an essential source of reference for game developers and researchers. Key takeaways include: Thorough introduction to game analytics; covering analytics applied to data on players, processes and performance throughout the game lifecycle. In-depth coverage and advice on setting up analytics systems and developing good practices for integrating analytics in game-development and -management. Contributions by leading researchers and experienced professionals from the industry, including Ubisoft, Sony, EA, Bioware, Square Enix, THQ, Volition, and PlayableGames. Interviews with experienced industry professionals on how they use analytics to create hit games. |
| data science in gaming industry: Games User Research Anders Drachen, Pejman Mirza-Babaei, Lennart E. Nacke, 2018 Games live and die commercially on the player experience. Games User Research is collectively the way we optimise the quality of the user experience (UX) in games, working with all aspects of a game from the mechanics and interface, visuals and art, interaction and progression, making sure every element works in concert and supports the game UX. This means that Games User Research is essential and integral to the production of games and to shape the experience of players. Today, Games User Research stands as the primary pathway to understanding players and how to design, build, and launch games that provide the right game UX. Until now, the knowledge in Games User Research and Game UX has been fragmented and there were no comprehensive, authoritative resources available. This book bridges the current gap of knowledge in Games User Research, building the go-to resource for everyone working with players and games or other interactive entertainment products. It is accessible to those new to Games User Research, while being deeply comprehensive and insightful for even hardened veterans of the game industry. In this book, dozens of veterans share their wisdom and best practices on how to plan user research, obtain the actionable insights from users, conduct user-centred testing, which methods to use when, how platforms influence user research practices, and much, much more. |
| data science in gaming industry: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science in gaming industry: Data Analytics Applications in Gaming and Entertainment Günter Wallner, 2019 |
| data science in gaming industry: Sports Analytics and Data Science Thomas W. Miller, 2015-11-18 This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. This up-to-the-minute reference will help you master all three facets of sports analytics — and use it to win! Sports Analytics and Data Science is the most accessible and practical guide to sports analytics for everyone who cares about winning and everyone who is interested in data science. You’ll discover how successful sports analytics blends business and sports savvy, modern information technology, and sophisticated modeling techniques. You’ll master the discipline through realistic sports vignettes and intuitive data visualizations–not complex math. Every chapter focuses on one key sports analytics application. Miller guides you through assessing players and teams, predicting scores and making game-day decisions, crafting brands and marketing messages, increasing revenue and profitability, and much more. Step by step, you’ll learn how analysts transform raw data and analytical models into wins: both on the field and in any sports business. |
| data science in gaming industry: Entertainment Science Thorsten Hennig-Thurau, Mark B. Houston, 2018-08-01 The entertainment industry has long been dominated by legendary screenwriter William Goldman’s “Nobody-Knows-Anything” mantra, which argues that success is the result of managerial intuition and instinct. This book builds the case that combining such intuition with data analytics and rigorous scholarly knowledge provides a source of sustainable competitive advantage – the same recipe for success that is behind the rise of firms such as Netflix and Spotify, but has also fueled Disney’s recent success. Unlocking a large repertoire of scientific studies by business scholars and entertainment economists, the authors identify essential factors, mechanisms, and methods that help a new entertainment product succeed. The book thus offers a timely alternative to “Nobody-Knows” decision-making in the digital era: while coupling a good idea with smart data analytics and entertainment theory cannot guarantee a hit, it systematically and substantially increases the probability of success in the entertainment industry. Entertainment Science is poised to inspire fresh new thinking among managers, students of entertainment, and scholars alike. Thorsten Hennig-Thurau and Mark B. Houston – two of our finest scholars in the area of entertainment marketing – have produced a definitive research-based compendium that cuts across various branches of the arts to explain the phenomena that provide consumption experiences to capture the hearts and minds of audiences. Morris B. Holbrook, W. T. Dillard Professor Emeritus of Marketing, Columbia University Entertainment Science is a must-read for everyone working in the entertainment industry today, where the impact of digital and the use of big data can’t be ignored anymore. Hennig-Thurau and Houston are the scientific frontrunners of knowledge that the industry urgently needs. Michael Kölmel, media entrepreneur and Honorary Professor of Media Economics at University of Leipzig Entertainment Science’s winning combination of creativity, theory, and data analytics offers managers in the creative industries and beyond a novel, compelling, and comprehensive approach to support their decision-making. This ground-breaking book marks the dawn of a new Golden Age of fruitful conversation between entertainment scholars, managers, and artists. Allègre Hadida, Associate Professor in Strategy, University of Cambridge |
| data science in gaming industry: Freemium Economics Eric Benjamin Seufert, 2013-12-27 Freemium Economics presents a practical, instructive approach to successfully implementing the freemium model into your software products by building analytics into product design from the earliest stages of development. Your freemium product generates vast volumes of data, but using that data to maximize conversion, boost retention, and deliver revenue can be challenging if you don't fully understand the impact that small changes can have on revenue. In this book, author Eric Seufert provides clear guidelines for using data and analytics through all stages of development to optimize your implementation of the freemium model. Freemium Economics de-mystifies the freemium model through an exploration of its core, data-oriented tenets, so that you can apply it methodically rather than hoping that conversion and revenue will naturally follow product launch. - Learn how to apply data science and big data principles in freemium product design and development to maximize conversion, boost retention, and deliver revenue - Gain a broad introduction to the conceptual economic pillars of freemium and a complete understanding of the unique approaches needed to acquire users and convert them from free to paying customers - Get practical tips and analytical guidance to successfully implement the freemium model - Understand the metrics and infrastructure required to measure the success of a freemium product and improve it post-launch - Includes a detailed explanation of the lifetime customer value (LCV) calculation and step-by-step instructions for implementing key performance indicators in a simple, universally-accessible tool like Excel |
| data science in gaming industry: One Up Joost van Dreunen, 2020-10-06 What explains the massive worldwide success of video games such as Fortnite, Minecraft, and Pokémon Go? Game companies and their popularity are poorly understood and often ignored from the standpoint of traditional business strategy. Yet this industry generates billions in revenue by thinking creatively about digital distribution, free-to-play content, and phenomena like e-sports and live streaming. What lessons can we draw from its major successes and failures about the future of entertainment? One Up offers a pioneering empirical analysis of innovation and strategy in the video game industry to explain how it has evolved from a fringe activity to become a mainstream form of entertainment. Joost van Dreunen, a widely recognized industry expert with over twenty years of experience, analyzes how game makers, publishers, and platform holders have tackled strategic challenges to make the video game industry what it is today. Using more than three decades of rigorously compiled industry data, he demonstrates that video game companies flourish when they bring the same level of creativity to business strategy that they bring to game design. Filled with case studies of companies such as Activision Blizzard, Apple, Electronic Arts, Epic Games, Microsoft, Nexon, Sony, Take-Two Interactive, Tencent, and Valve, this book forces us to rethink common misconceptions around the emergence of digital and mobile gaming. One Up is required reading for investors, creatives, managers, and anyone looking to learn about the major drivers of change and growth in contemporary entertainment. |
| data science in gaming industry: Data Scientist Pocket Guide Mohamed Sabri, 2021-06-24 Discover one of the most complete dictionaries in data science. KEY FEATURES ● Simplified understanding of complex concepts, terms, terminologies, and techniques. ● Combined glossary of machine learning, mathematics, and statistics. ● Chronologically arranged A-Z keywords with brief description. DESCRIPTION This pocket guide is a must for all data professionals in their day-to-day work processes. This book brings a comprehensive pack of glossaries of machine learning, deep learning, mathematics, and statistics. The extensive list of glossaries comprises concepts, processes, algorithms, data structures, techniques, and many more. Each of these terms is explained in the simplest words possible. This pocket guide will help you to stay up to date of the most essential terms and references used in the process of data analysis and machine learning. WHAT YOU WILL LEARN ● Get absolute clarity on every concept, process, and algorithm used in the process of data science operations. ● Keep yourself technically strong and sound-minded during data science meetings. ● Strengthen your knowledge in the field of Big data and business intelligence. WHO THIS BOOK IS FOR This book is for data professionals, data scientists, students, or those who are new to the field who wish to stay on top of industry jargon and terminologies used in the field of data science. TABLE OF CONTENTS 1. Chapter one: A 2. Chapter two: B 3. Chapter three: C 4. Chapter four: D 5. Chapter five: E 6. Chapter six: F 7. Chapter seven: G 8. Chapter eight: H 9. Chapter nine: I 10. Chapter ten: J 11. Chapter 11: K 12. Chapter 12: L 13. Chapter 13: M 14. Chapter 14: N 15. Chapter 15: O 16. Chapter 16: P 17. Chapter 17: Q 18. Chapter 18: R 19. Chapter 19 : S 20. Chapter 20 : T 21. Chapter 21 : U 22. Chapter 22 : V 23. Chapter 23: W 24. Chapter 24: X 25. Chapter 25: Y 26. Chapter 26 : Z |
| data science in gaming industry: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science in gaming industry: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| data science in gaming industry: How To Be A Games User Researcher Steve Bromley, 2021-02-05 Love video games? Start your career making them better. Games user researchers run playtests to ensure games are understandable and enjoyable, and are a key part of making games that people love. The video games industry is full of passionate people who care about making fun experiences. If you love games, and want to make them better, consider a career in games user research. Drawing upon ten years of experience working on top games and helping people start their career in games How To Be A Games User Researcher is the essential guide on how to run professional quality playtest studies and get a job in the games industry. What's in the book? Discover How games development works and where research fits in How to plan, run, analyse and debrief professional quality playtests The importance of building relationships with game teams How to start a career in user research The skills required to excel at job interviews Who is this book for? This book is for: Students considering a career in games user research UX researchers looking to transition into games New games user researchers Academics studying games design, development, or HCI Game designers and developers looking to improve the quality of their playtests About the author Steve Bromley led research studies for many of PlayStation's top European games including Horizon: Zero Dawn, SingStar and the PlayStation VR launch lineup. He continues to work with games and VR studios to improve the player experience of their games. For the last five years, Steve Bromley has run a games user research mentoring scheme, which has partnered over one hundred students with more than fifty industry professionals from top companies such as Sony, EA, Valve, Ubisoft, and Microsoft, and helped many people get their first job in games. This book covers many of the topics that mentees have asked as they start their games user research career. |
| data science in gaming industry: The Psychology of Video Games Celia Hodent, 2020-10-07 What impact can video games have on us as players? How does psychology influence video game creation? Why do some games become cultural phenomena? The Psychology of Video Games introduces the curious reader to the relationship between psychology and video games from the perspective of both game makers and players. Assuming no specialist knowledge, this concise, approachable guide is a starter book for anyone intrigued by what makes video games engaging and what is their psychological impact on gamers. It digests the research exploring the benefits gaming can have on players in relation to education and healthcare, considers the concerns over potential negative impacts such as pathological gaming, and concludes with some ethics considerations. With gaming being one of the most popular forms of entertainment today, The Psychology of Video Games shows the importance of understanding the human brain and its mental processes to foster ethical and inclusive video games. |
| data science in gaming industry: Creating a Data-Driven Organization Carl Anderson, 2015-07-23 What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models--Publisher's description. |
| data science in gaming industry: Emerging Technologies and Applications for Cloud-Based Gaming Krishna, P. Venkata, 2016-07-13 Online gaming is widely popular and gaining more user attention every day. Computer game industries have made considerable growth in terms of design and development, but the scarcity of hardware resources at player or client side is a major pitfall for the latest high-end multimedia games. Cloud gaming is one proposed solution, allowing the end-user to play games using a variety of platforms with less demanding hardware requirements. Emerging Technologies and Applications for Cloud-Based Gaming explores the opportunities for the gaming industry through the integration of cloud computing. Focusing on design methodologies, fundamental architectures, and the end-user experience, this publication is an essential reference source for IT specialists, game developers, researchers, and graduate-level students. |
| data science in gaming industry: Data Engineering and Data Science Kukatlapalli Pradeep Kumar, Aynur Unal, Vinay Jha Pillai, Hari Murthy, M. Niranjanamurthy, 2023-08-29 DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library. |
| data science in gaming industry: Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions Matt Taddy, 2019-08-23 Use machine learning to understand your customers, frame decisions, and drive value The business analytics world has changed, and Data Scientists are taking over. Business Data Science takes you through the steps of using machine learning to implement best-in-class business data science. Whether you are a business leader with a desire to go deep on data, or an engineer who wants to learn how to apply Machine Learning to business problems, you’ll find the information, insight, and tools you need to flourish in today’s data-driven economy. You’ll learn how to: Use the key building blocks of Machine Learning: sparse regularization, out-of-sample validation, and latent factor and topic modeling Understand how use ML tools in real world business problems, where causation matters more that correlation Solve data science programs by scripting in the R programming language Today’s business landscape is driven by data and constantly shifting. Companies live and die on their ability to make and implement the right decisions quickly and effectively. Business Data Science is about doing data science right. It’s about the exciting things being done around Big Data to run a flourishing business. It’s about the precepts, principals, and best practices that you need know for best-in-class business data science. |
| data science in gaming industry: Data Science for Web3 Gabriela Castillo Areco, 2023-12-29 Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internet Key Features Build a deep understanding of the fundamentals of blockchain analytics Extract actionable business insights by modeling blockchain data Showcase your work and gain valuable experience to seize opportunities in the Web3 ecosystem Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionData is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3. You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data. The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients. By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What you will learn Understand the core components of blockchain transactions and blocks Identify reliable sources of on-chain and off-chain data to build robust datasets Understand key Web3 business questions and how data science can offer solutions Build your skills to create and query NFT- and DeFi-specific datasets Implement a machine learning toolbox with real-world use cases in the Web3 space Who this book is for This book is designed for data professionals—data analysts, data scientists, or data engineers— and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience. |
| data science in gaming industry: Inside the Video Game Industry Judd Ruggill, Ken McAllister, Randy Nichols, Ryan Kaufman, 2016-11-03 Inside the Video Game Industry offers a provocative look into one of today's most dynamic and creative businesses. Through in-depth structured interviews, industry professionals discuss their roles, providing invaluable insight into game programming, art, animation, design, production, quality assurance, audio and business professions. From hiring and firing conventions, attitudes about gender disparity, goals for work-life balance, and a span of legal, psychological, and communal intellectual property protection mechanisms, the book's combination of accessible industry talk and incisive thematic overviews is ideal for anyone interested in games as a global industry, a site of cultural study, or a prospective career path. Designed for researchers, educators, and students, this book provides a critical perspective on an often opaque business and its highly mobile workforce. Additional teaching materials, including activities and study questions, can be found at https://www.routledge.com/9780415828284. |
| data science in gaming industry: Data Science in Production Ben Weber, 2020 Putting predictive models into production is one of the most direct ways that data scientists can add value to an organization. By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment. After working through this material, readers will learn how to use these message brokers to create streaming model pipelines with PySpark and Dataflow that provide near real-time predictions. Excerpts of these chapters are available on Medium (@bgweber), and a book sample is available on Leanpub. |
| data science in gaming industry: Artificial Intelligence and Games Georgios N. Yannakakis, Julian Togelius, 2018-02-17 This is the first textbook dedicated to explaining how artificial intelligence (AI) techniques can be used in and for games. After introductory chapters that explain the background and key techniques in AI and games, the authors explain how to use AI to play games, to generate content for games and to model players. The book will be suitable for undergraduate and graduate courses in games, artificial intelligence, design, human-computer interaction, and computational intelligence, and also for self-study by industrial game developers and practitioners. The authors have developed a website (http://www.gameaibook.org) that complements the material covered in the book with up-to-date exercises, lecture slides and reading. |
| data science in gaming industry: Fundamental Of Data Science And Big Data Analytics N. Narayanan Prasanth, K. Vimala Devi, 2023-11-29 The book provides a thorough, accessible, and current comprehension of Big Data for both business people and engineers. This book presents essential ideas, theories, terminology, and technologies related to Big Data. It also covers important analysis and analytics approaches. The information is rationally organized, given in clear and simple language, and backed with easily comprehensible examples. The objective of “Fundamentals of Data Science and Big Data Science” is to enhance decision-making by analyzing data. Currently, data science plays a crucial role in determining the advertisements that appear on the internet, the recommendations you get for books and films, the classification of emails into your spam folders, as well as the pricing of health insurance. This book provides a brief description of the developing discipline of data science, elucidating its progression, present applications, data infrastructure concerns, and legal issues. The text adopts a conversational tone and stays clear of complex mathematical ideas often associated with data science, instead focusing on straightforward explanations and real-world use cases. Upon concluding the book, readers will have acquired proficiency in controlling data, using data in the context of business challenges, and implementing optimal methodologies for data analysis. This book functions as a practical guide for Science/Engineering/MBA students, including both undergraduate and graduate students, who have an interest in the field of Data Science. |
| data science in gaming industry: Biologically Inspired Artificial Intelligence for Computer Games Charles, Darryl, Fyfe, Colin, Livingstone, Daniel, McGlinchey, Stephen, 2007-11-30 This book examines modern artificial intelligence to display how it may be applied to computer games. It spans the divide that exists between the academic research community working with advanced artificial intelligence and the games programming community which must create and release new and interesting games, creating an invaluable collection supporting both technological research and the gaming industry--Provided by publisher. |
| data science in gaming industry: Data Science, Analytics and Machine Learning with R Luiz Paulo Favero, Patricia Belfiore, Rafael de Freitas Souza, 2023-01-23 Data Science, Analytics and Machine Learning with R explains the principles of data mining and machine learning techniques and accentuates the importance of applied and multivariate modeling. The book emphasizes the fundamentals of each technique, with step-by-step codes and real-world examples with data from areas such as medicine and health, biology, engineering, technology and related sciences. Examples use the most recent R language syntax, with recognized robust, widespread and current packages. Code scripts are exhaustively commented, making it clear to readers what happens in each command. For data collection, readers are instructed how to build their own robots from the very beginning. In addition, an entire chapter focuses on the concept of spatial analysis, allowing readers to build their own maps through geo-referenced data (such as in epidemiologic research) and some basic statistical techniques. Other chapters cover ensemble and uplift modeling and GLMM (Generalized Linear Mixed Models) estimations, both linear and nonlinear. - Presents a comprehensive and practical overview of machine learning, data mining and AI techniques for a broad multidisciplinary audience - Serves readers who are interested in statistics, analytics and modeling, and those who wish to deepen their knowledge in programming through the use of R - Teaches readers how to apply machine learning techniques to a wide range of data and subject areas - Presents data in a graphically appealing way, promoting greater information transparency and interactive learning |
| data science in gaming industry: Analyzing Future Applications of AI, Sensors, and Robotics in Society Thomas Heinrich Musiolik, Adrian David Cheok, 2020 This book explores the future challenges and hidden potentials of the application of artificial intelligence, sensors, and robotics in society-- |
| data science in gaming industry: The Predictive Casino - 2nd Edition Andrew Pearson, 2017-12-15 The Predictive Casino is a casino that utilizes the latest technological developments to connect with its customers to deliver an exceptional personalized experience that will keep them coming back. Today, technology such as AI, Machine Learning, Augmented Reality, facial recognition, IoT, Real-time stream processing, social media, and wearables are altering the Customer Experience (CX) landscape and casino operators need to jump aboard this fast moving technology or run the risk of being left out in the cold. The Predictive Casino reveals how these and other technologies can help shape the customer journey as well as provide insights into every facet of the business. The book details how the five types of analytics-descriptive, diagnostic, predictive, prescriptive, and edge analytics-affect not only the customer journey, but also just about every department in a casino operation. Facial recognition technology can spot a customer stepping onto a casino bus at the Macau/China border and that can set off alerts which will notify he necessary departments, where that is a host on the floor, a restaurant manager, a dealer, or even the hotel's GM should the player be a high-end VIP. A whole other sequence of events can get triggered as the player's favorite table is prepared, his favorite meal is cooked, and his or her Theo gets added to a real-time table games revenue management model that takes into account his personal play. Labor needs are also considered and alerts can be sent to the required or unneeded staff. An IoT connected casino can make its operations smart. Connected devices can help with inventory optimization, supply chain management, labor management, and waste management. With machine learning algorithms, data centers can be kept green and their energy use smart. For a casino operator, social media is no longer a vanity platform, but rather a place to both connect with current customers as well as court new ones. It is also a powerful branding channel that can be utilized to both understand a casino's position in the market, as well as a place to benchmark its position against its competitors. Today, technology moves at break-neck speed and it can offer the power of deep customer understanding and insight, but it also comes with a confusing variety of technology and technological terms--Big Data, Cognitive Computing, CX, Data Lakes, Hadoop, Kafka, Personalization, Spark, etc., etc. The Predictive Casino will help make sense of it all, so that a casino executive can cut through the confusing clutters of technological jargons and understand why a Spark-based real-time stream processing data stream might be preferable over a TIBCO Streambase one, or an even IBM InfoSphere one. Or maybe not. This book will help casino executives break through the technological clutter so that they can deliver an unrivaled customer experience to each and every patron coming through their doors. |
| data science in gaming industry: Game Programming Patterns Robert Nystrom, 2014-11-03 The biggest challenge facing many game programmers is completing their game. Most game projects fizzle out, overwhelmed by the complexity of their own code. Game Programming Patterns tackles that exact problem. Based on years of experience in shipped AAA titles, this book collects proven patterns to untangle and optimize your game, organized as independent recipes so you can pick just the patterns you need. You will learn how to write a robust game loop, how to organize your entities using components, and take advantage of the CPUs cache to improve your performance. You'll dive deep into how scripting engines encode behavior, how quadtrees and other spatial partitions optimize your engine, and how other classic design patterns can be used in games. |
| data science in gaming industry: Exodus to the Virtual World Edward Castronova, 2007-11-27 Virtual worlds have exploded out of online game culture and now capture the attention of millions of ordinary people: husbands, wives, fathers, mothers, workers, retirees. Devoting dozens of hours each week to massively multiplayer virtual reality environments (like World of Warcraft and Second Life), these millions are the start of an exodus into the refuge of fantasy, where they experience life under a new social, political, and economic order built around fun. Given the choice between a fantasy world and the real world, how many of us would choose reality? Exodus to the Virtual World explains the growing migration into virtual reality, and how it will change the way we live--both in fantasy worlds and in the real one. |
| data science in gaming industry: Industry 4.0, AI, and Data Science Vikram Bali, Kakoli Banerjee, Narendra Kumar, Sanjay Gour, Sunil Kumar Chawla, 2021-07-20 The aim of this book is to provide insight into Data Science and Artificial Learning Techniques based on Industry 4.0, conveys how Machine Learning & Data Science are becoming an essential part of industrial and academic research. Varying from healthcare to social networking and everywhere hybrid models for Data Science, Al, and Machine Learning are being used. The book describes different theoretical and practical aspects and highlights how new systems are being developed. Along with focusing on the research trends, challenges and future of AI in Data Science, the book explores the potential for integration of advanced AI algorithms, addresses the challenges of Data Science for Industry 4.0, covers different security issues, includes qualitative and quantitative research, and offers case studies with working models. This book also provides an overview of AI and Data Science algorithms for readers who do not have a strong mathematical background. Undergraduates, postgraduates, academicians, researchers, and industry professionals will benefit from this book and use it as a guide. |
| data science in gaming industry: Machine Intelligence and Data Science Applications Vaclav Skala, T. P. Singh, Tanupriya Choudhury, Ravi Tomar, Md. Abul Bashar, 2022-08-01 This book is a compilation of peer reviewed papers presented at International Conference on Machine Intelligence and Data Science Applications (MIDAS 2021), held in Comilla University, Cumilla, Bangladesh during 26 – 27 December 2021. The book covers applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, speech and gesture analysis, etc. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber physical system and smart agriculture, etc. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates. |
| data science in gaming industry: Machine Intelligence and Data Science Applications Amar Ramdane-Cherif, T. P. Singh, Ravi Tomar, Tanupriya Choudhury, Jung-Sup Um, 2023-10-03 This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications (MIDAS 2022), held on October 28 and 29, 2022, at the University of Versailles—Paris-Saclay, France. The book covers applications in various fields like data science, machine intelligence, image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber-physical system, and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in the machine intelligence discipline, and engineering graduates. |
| data science in gaming industry: Team Leadership in the Game Industry Seth Spaulding, 2009 This book provides game industry professionals management and leadership skills and tools to help them become better leaders. Focusing specifically on the unique challenges of the game industry, the book features examples, case studies, and successful strategies directly from the industry experts.--Publisher. |
| data science in gaming industry: The Gamer's Brain Celia Hodent, 2017-08-10 Making a successful video game is hard. Even games that are successful at launch may fail to engage and retain players in the long term due to issues with the user experience (UX) that they are delivering. The game user experience accounts for the whole experience players have with a video game, from first hearing about it to navigating menus and progressing in the game. UX as a discipline offers guidelines to assist developers in creating the experience they want to deliver, shipping higher quality games (whether it is an indie game, AAA game, or serious game), and meeting their business goals while staying true to their design and artistic intent. In a nutshell, UX is about understanding the gamer’s brain: understanding human capabilities and limitations to anticipate how a game will be perceived, the emotions it will elicit, how players will interact with it, and how engaging the experience will be. This book is designed to equip readers of all levels, from student to professional, with neuroscience knowledge and user experience guidelines and methodologies. These insights will help readers identify the ingredients for successful and engaging video games, empowering them to develop their own unique game recipe more efficiently, while providing a better experience for their audience. Key Features Provides an overview of how the brain learns and processes information by distilling research findings from cognitive science and psychology research in a very accessible way. Topics covered include: neuromyths, perception, memory, attention, motivation, emotion, and learning. Includes numerous examples from released games of how scientific knowledge translates into game design, and how to use a UX framework in game development. Describes how UX can guide developers to improve the usability and the level of engagement a game provides to its target audience by using cognitive psychology knowledge, implementing human-computer interaction principles, and applying the scientific method (user research). Provides a practical definition of UX specifically applied to games, with a unique framework. Defines the most relevant pillars for good usability (ease of use) and good engage-ability (the ability of the game to be fun and engaging), translated into a practical checklist. Covers design thinking, game user research, game analytics, and UX strategy at both a project and studio level. Offers unique insights from a UX expert and PhD in psychology who has been working in the entertainment industry for over 10 years. This book is a practical tool that any professional game developer or student can use right away and includes the most complete overview of UX in games existing today. |
| data science in gaming industry: Assetization Kean Birch, Fabian Muniesa, 2020-07-14 How the asset—anything that can be controlled, traded, and capitalized as a revenue stream—has become the primary basis of technoscientific capitalism. In this book, scholars from a range of disciplines argue that the asset—meaning anything that can be controlled, traded, and capitalized as a revenue stream—has become the primary basis of technoscientific capitalism. An asset can be an object or an experience, a sum of money or a life form, a patent or a bodily function. A process of assetization prevails, imposing investment and return as the key rationale, and overtaking commodification and its speculative logic. Although assets can be bought and sold, the point is to get a durable economic rent from them rather than make a killing on the market. Assetization examines how assets are constructed and how a variety of things can be turned into assets, analyzing the interests, activities, skills, organizations, and relations entangled in this process. The contributors consider the assetization of knowledge, including patents, personal data, and biomedical innovation; of infrastructure, including railways and energy; of nature, including mineral deposits, agricultural seeds, and “natural capital”; and of publics, including such public goods as higher education and “monetizable social ills.” Taken together, the chapters show the usefulness of assetization as an analytical tool and as an element in the critique of capitalism. Contributors Thomas Beauvisage, Kean Birch, Veit Braun, Natalia Buier, Béatrice Cointe, Paul Robert Gilbert, Hyo Yoon Kang, Les Levidow, Kevin Mellet, Sveta Milyaeva, Fabian Muniesa, Alain Nadaï, Daniel Neyland, Victor Roy, James W. Williams |
| data science in gaming industry: Big Data for Regional Science Laurie A Schintler, Zhenhua Chen, 2017-08-07 Recent technological advancements and other related factors and trends are contributing to the production of an astoundingly large and rapidly accelerating collection of data, or ‘Big Data’. This data now allows us to examine urban and regional phenomena in ways that were previously not possible. Despite the tremendous potential of big data for regional science, its use and application in this context is fraught with issues and challenges. This book brings together leading contributors to present an interdisciplinary, agenda-setting and action-oriented platform for research and practice in the urban and regional community. This book provides a comprehensive, multidisciplinary and cutting-edge perspective on big data for regional science. Chapters contain a collection of research notes contributed by experts from all over the world with a wide array of disciplinary backgrounds. The content is organized along four themes: sources of big data; integration, processing and management of big data; analytics for big data; and, higher level policy and programmatic considerations. As well as concisely and comprehensively synthesising work done to date, the book also considers future challenges and prospects for the use of big data in regional science. Big Data for Regional Science provides a seminal contribution to the field of regional science and will appeal to a broad audience, including those at all levels of academia, industry, and government. |
| data science in gaming industry: Data Science on AWS Chris Fregly, Antje Barth, 2021-04-07 With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more |
| data science in gaming industry: Software Source Code Raghavendra Rao Althar, Debabrata Samanta, Debanjan Konar, Siddhartha Bhattacharyya, 2021-07-19 This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues. |
| data science in gaming industry: The Analytic Hospitality Executive Kelly A. McGuire, 2016-08-29 Targeted analytics to address the unique opportunities in hospitality and gaming The Analytic Hospitality Executive helps decision makers understand big data and how it can drive value in the industry. Written by a leading business analytics expert who specializes in hospitality and travel, this book draws a direct link between big data and hospitality, and shows you how to incorporate analytics into your strategic management initiative. You'll learn which data types are critical, how to identify productive data sources, and how to integrate analytics into multiple business processes to create an overall analytic culture that turns information into insight. The discussion includes the tools and tips that help make it happen, and points you toward the specific places in your business that could benefit from advanced analytics. The hospitality and gaming industry has unique needs and opportunities, and this book's targeted guidance provides a roadmap to big data benefits. Like most industries, the hospitality and gaming industry is experiencing a rapid increase in data volume, variety, and velocity. This book shows you how to corral this growing current, and channel it into productive avenues that drive better business. Understand big data and analytics Incorporate analytics into existing business processes Identify the most valuable data sources Create a strategic analytic culture that drives value Although the industry is just beginning to recognize the value of big data, it's important to get up to speed quickly or risk losing out on benefits that could drive business to greater heights. The Analytic Hospitality Executive provides a targeted game plan from an expert on the inside, so you can start making your data work for you. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
