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Data Science in the Games Industry

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

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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Learners who complete Data Science in the Games Industry will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Science in the Games Industry.

Cassandra: The Definitive Guide

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As the most up-to-date edition of the definitive guide, this book must-read for gaining a comprehensive understanding of modern Cassandra. It covers the latest features, best practices, and integrations, making it essential for professionals and advanced students working with Cassandra today. It's a primary reference and often used as a textbook.

Data Science in the Games Industry

Topics Covered

Topics Covered

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Reviews summary

Data science for gaming professionals

Career center

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