Data Science in the Games Industry Certificate
Short course
In Dundee
Description
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Type
Short course
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Level
Intermediate
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Location
Dundee (Scotland)
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Duration
3 Weeks
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Start date
Different dates available
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.
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.
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
Facilities
Location
Start date
Start date
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Subjects
- Data analysis
- Design
- Industry
- Data Exhaust
- Tabular
- Big Data
- CAP Triangle
- Disappearances
- NoSQL
- CAP theorem
- Dark
- HDFS
- Technologies
- Analytical systems
- Architecture
Course programme
Week 1: Data in all its glory
- The Data Exhaust
- Tabular vs Big Data
- Disappearances in the CAP Triangle
- Why do NoSQL databases help overcome CAP theorem?
- What is Dark data and where is it hiding?
Week 2: Taming the Data Exhaust
- How can we analyse large data with distributed systems?
- Hadoop, HDFS, MapReduce, and other technologies including the Spark framework
- Distributed real time analytical systems
- Lambda Architecture
- Graphs and Graph databases
Week 3: Analysis is our answer
- Introduction to Statistics
- Introduction to R and Python
- Bayesian Statistics
- Goals of machine learning and data mining
- Neural networks
Data Science in the Games Industry Certificate