Data Science in the Games Industry


Use data analysis to build better gaming experiences

The video games industry collects vast amounts of data from its users. But most of this data is disregarded despite its value to the gaming industry.
This course will show you how to store and analyse data effectively and gain insights into game users’ actions and behaviours.

Note: Pricing is in USD


You’ll find out about the different models of data, such as tabular data, atomic data, and relational data.
You’ll understand how to store non-relational data at scale, and why data can be hard to distribute.
You’ll learn how to build better gaming experiences and increase profits.

What topics will you cover?

Week 1: Data in all its glory

  • The Data Exhaust
  • Tabular vs Big Data
  • Disappearances in the CAP Triangle

Week 2: Breaking the CAP Triangle

  • NoSQL
  • Cassandra
  • MongoDb
  • Graphs and Graph Databases
  • Dark Data’s Hiding Place
  • Week 3: Taming the Data Exhaust

  • Big Data and Distributed Systems
  • Hadoop, HDFS, MapReduce and Other Technologies
  • Real-time Systems
  • Lambda
  • Week 4: Analysis is our answer

  • Introduction to Statistics
  • Consumer Testing
  • Introduction to R and Python
  • Bayesian Statistics
  • Machine learning and data mining
  • The Future of Data Science
  • What will you achieve?

    By the end of the course, you’ll be able to…

  • Assess new techniques of data analysis
  • Synthesise knowledge to be able to describe the types of data that techniques can best be applied to
  • Design data stores that can manage data at scale
  • Classify data in context, to select the most appropriate technique for data analysis
  • Compare and evaluate new techniques for data analysis for a number of given scenarios in the games industry
  • Design data stores that can manage complex data at scale for a number of given scenarios in the games industry
  • Who is the course for?

    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.

    What software or tools do you need?

    In order to get the best out of this course, you should have a laptop or desktop computer (Windows or Mac) that can run virtual machine software such as VirtualBox or Docker. You should be happy to install software on your machines such as Python or R Studio. Links and instructions for installation and use will be included during the course.

    Who will you learn with?

    Andy Cobley
    Andy Cobley is a senior lecturer at the school of Science and Engineering at the University of Dundee. He is the program director for the MSc programs in Data Science and Data Engineering.

    Mark Whitehorn
    Professor Mark Whitehorn specialises in Analytics, Data Science and Machine Learning. He splits his time between the commercial and academic worlds.


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