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Author: Magy Seif El-Nasr
Publisher: Oxford University Press
ISBN: 0192652710
Category : Computers
Languages : en
Pages : 320
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Book Description
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.
Author: Magy Seif El-Nasr
Publisher: Oxford University Press
ISBN: 0192652710
Category : Computers
Languages : en
Pages : 320
Get Book Here
Book Description
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.
Author: Magy Seif El-Nasr
Publisher: Springer Science & Business Media
ISBN: 1447147693
Category : Computers
Languages : en
Pages : 792
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Book Description
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.
Author: Thomas W. Miller
Publisher: FT Press
ISBN: 0133887413
Category : Business & Economics
Languages : en
Pages : 576
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Book Description
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.
Author: Boi Mirsky
Publisher: Springer Nature
ISBN: 3031015770
Category : Computers
Languages : en
Pages : 135
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Book Description
Intelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Category : Computers
Languages : en
Pages : 521
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Book Description
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Author: Mark H. Maier
Publisher: M.E. Sharpe
ISBN: 0765629836
Category : Business & Economics
Languages : en
Pages : 330
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Book Description
This book introduces students to the collection, uses, and interpretation of statistical data in the social sciences. It would suit all social science introductory statistics and research methods courses. Separate chapters are devoted to data in the fields of demography, housing, health, education, crime, the economy, wealth, income, poverty, labor, business statistics, and public opinion polling, with a concluding chapter devoted to the common problem of ambiguity. Each chapter includes multiple case studies illustrating the controversies, overview of data sources including web sites, chapter summary and a set of case study questions designed to stimulate further thought.
Author: Paola Zuccolotto
Publisher: CRC Press
ISBN: 0429894260
Category : Business & Economics
Languages : en
Pages : 245
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Book Description
Using data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player's shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers. Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real world case studies and examples, such as estimations of scoring probability using the Golden State Warriors as a test case Provides the source code and data so readers can do their own analyses on NBA teams and players
Author: John W. Foreman
Publisher: John Wiley & Sons
ISBN: 1118839862
Category : Business & Economics
Languages : en
Pages : 432
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Book Description
Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
Author: Cathy O'Neil
Publisher: "O'Reilly Media, Inc."
ISBN: 144936389X
Category : Computers
Languages : en
Pages : 320
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Book Description
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.
Author: Joel Grus
Publisher: "O'Reilly Media, Inc."
ISBN: 1491904399
Category : Computers
Languages : en
Pages : 336
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Book Description
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
