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Andy Cobley,
Mark Whitehorn,
Patrick Blackwill,
Stuart Anderson,
Alex Taylor,
Iain Kay,
Gary Bowerbank,
and
James Frost
Week 1: Data in all its glory Week 2: Breaking the CAP Triangle Week 3: Taming the Data Exhaust Week 4: Analysis is our answer Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at those who already work in the games industry, but may also be of interest to those looking to work in the sector. You can use the hashtag #FLgamingdata to talk about this course on social media.
Topics Covered
Read more
Week 1: Data in all its glory Week 2: Breaking the CAP Triangle Week 3: Taming the Data Exhaust Week 4: Analysis is our answer Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at those who already work in the games industry, but may also be of interest to those looking to work in the sector. You can use the hashtag #FLgamingdata to talk about this course on social media.
Topics Covered
Read more
Week 1: Data in all its glory Week 2: Breaking the CAP Triangle Week 3: Taming the Data Exhaust Week 4: Analysis is our answer Most FutureLearn courses run multiple times. Every run of a course has a set start date but you can join it and work through it after it starts. Find out more This course is aimed at those who already work in the games industry, but may also be of interest to those looking to work in the sector. You can use the hashtag #FLgamingdata to talk about this course on social media.
Topics Covered
- The Data Exhaust
- Tabular vs Big Data
- Disappearances in the CAP Triangle
- NoSQL
- Cassandra
- MongoDb
- Graphs and Graph Databases
- Dark Data’s Hiding Place
- Big Data and Distributed Systems
- Hadoop, HDFS, MapReduce and Other Technologies
- Real-time Systems
- Lambda
- Introduction to Statistics
- Consumer Testing
- Introduction to R and Python
- Bayesian Statistics
- Machine learning and data mining
- The Future of Data Science
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Reviews summary
Data science for gaming professionals
According to learners, this course offers a solid introduction and valuable foundation for understanding data science specifically within the games industry. Students frequently highlight its ability to bridge core data science concepts with game development, making complex topics such as NoSQL databases, Big Data, and distributed systems more accessible. Many find the instructor's explanations clear and engaging, providing a useful overview for those looking to integrate data practices into their roles. However, some learners feel it is more of an overview than an in-depth dive, with a common desire for more hands-on coding exercises and a deeper exploration of advanced topics like machine learning, particularly for those with existing data science backgrounds.
Generally relevant, though some older mentions of dated tools.
"Decent course but felt a bit dated in some areas, especially the tools mentioned."
"Despite some older tools, the core concepts covered are still highly relevant and useful today."
Ideal for newcomers, less for experienced data professionals.
"Good for someone completely new to data science, but not much for those with some background."
"Good course for beginners or those needing a high-level overview. It certainly opened my eyes... Just don't expect to become a data scientist after this."
"I gained a very basic introduction to data science concepts from this course, which was great for me as a beginner."
Provides a comprehensive introduction to key data concepts.
"Excellent course, really enjoyed the content on NoSQL databases and how they apply to player data... The instructor made complex topics easy to grasp."
"Solid introduction. Covered a lot of ground in a short time. I liked the discussion on the CAP Triangle."
"Well-structured and easy to follow. Provides a good general understanding of data science principles and how they relate to the game industry."
Directly applicable for game industry roles.
"I appreciate the focus on practical applications within the gaming context. Highly recommend for game industry professionals."
"As a game developer looking to understand data, this course was perfect. It truly bridges the gap between data science and game development."
"It's a great starting point for anyone in the games industry looking to integrate data practices."
Could benefit from more advanced topics and practical coding.
"Could have used more hands-on coding exercises, especially for the R and Python parts. Felt a bit theoretical at times, but still valuable."
"Content was okay, but some parts felt a bit rushed. The section on machine learning was very brief; I wish it went deeper."
"I was hoping for more actual data science *in* games, like specific case studies or advanced analytics. This felt very basic."
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Data Science in the Games Industry.
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Reading list
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Provides a comprehensive overview of machine learning, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. Written by leading experts in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Provides a comprehensive overview of big data analytics, covering topics such as data management, data mining, and data visualization. It valuable resource for students and practitioners who want to gain a better understanding of big data analytics.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.
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An excellent overview of Bayesian statistics, this book provides a comprehensive introduction to the theory and practice of Bayesian data analysis. The focus on practical applications and real-life examples makes it a great choice for students and practitioners alike.
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A classic text in the field of data mining, this book provides a comprehensive overview of techniques and algorithms used for extracting knowledge from large datasets. Written by leading experts in the field, it valuable resource for students and researchers.
A hands-on guide to data analysis using Python, this book covers a wide range of topics, including data cleaning, transformation, visualization, and modeling. Written by the creator of Pandas, it practical resource for students and professionals in various fields.
A widely-used textbook for undergraduate and graduate-level statistics and data science courses. It provides a comprehensive overview of statistical learning methods with practical applications in R. While it can be challenging, it solidifies understanding of key modeling and prediction techniques. This core textbook for those seeking a deeper understanding.
This online book provides a comprehensive overview of machine learning concepts and techniques. Written by a leading expert in the field, it valuable resource for students and practitioners who want to gain a deep understanding of machine learning.
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A comprehensive introduction to data analysis using R, this book covers a wide range of topics, including data manipulation, visualization, and statistical modeling. Written by leading experts in the field, it valuable resource for students and practitioners.
Provides a comprehensive overview of statistical methods for data analysis, covering topics such as probability distributions, hypothesis testing, and regression analysis. Written by a leading expert in the field, it valuable resource for students and practitioners in various fields.
This comprehensive handbook provides a wide range of topics in data science, including data mining, machine learning, and data visualization. Written by experts in the field, it valuable resource for students and practitioners who want to gain a broad understanding of data science.
Is an excellent starting point for anyone new to data analysis or statistics. It demystifies core statistical concepts without relying heavily on mathematical formulas, making it highly accessible for high school and undergraduate students. It provides a strong foundation in the intuition behind statistical analysis and helps readers understand how data can be used and misused. This is valuable background reading that builds prerequisite knowledge.
A timeless classic that remains highly relevant today. exposes common ways statistics can be manipulated or misinterpreted, fostering a critical eye essential for anyone working with data. It's valuable for all levels, from high school to professional, as it highlights the importance of data integrity and ethical considerations. This serves as crucial additional reading to develop data literacy.
Explores the world of prediction and forecasting across various fields, demonstrating how data analysis and statistical modeling are used in practice. It's particularly engaging for undergraduate and graduate students interested in the application of data analysis in real-world scenarios. It adds breadth by showcasing diverse applications and the challenges involved in making accurate predictions.
Written by the creator of the pandas library, this practical, hands-on guide to manipulating, processing, cleaning, and crunching data in Python. It is essential for anyone using Python for data analysis, from undergraduates to professionals. It serves as an invaluable reference tool and is commonly used as a textbook or supplementary material in data analysis courses focusing on Python.
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Provides a comprehensive introduction to data analysis using R and the tidyverse package collection. It's highly recommended for students and professionals using R, offering a structured approach to data manipulation, visualization, and modeling. It functions well as a textbook and a practical reference.
Builds data science tools and algorithms from the ground up using Python, providing a deeper understanding of the underlying mechanics. It's suitable for those with some programming experience and a desire to understand how data analysis techniques work internally. It helps solidify understanding by revealing the foundational code.
A classic text in the field of statistical learning, this book covers a wide range of topics, including linear and nonlinear regression, classification, unsupervised learning, and model selection. It comprehensive resource for students and practitioners in various fields.
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Via
FutureLearn
Institution
University of Dundee
Instructors
Andy Cobley
Mark Whitehorn
Patrick Blackwill
Stuart Anderson
Alex Taylor
Iain Kay
Gary Bowerbank
James Frost
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