106412 - DATA ANALYSIS

Subject type: Mandatory

Coordinator: Juan José Pons López

Trimester: First term

Credits: 6

• English

The course introduces the student to the world of data analytics, with application to the analysis of video game data. Data analysis becomes a fundamental aspect of game development, in many ways:

• It helps to understand the user's behavior and to be able to adapt to it to improve the user's experience.
• You can identify types of users, by their behavior, by the type of strategies they use, or by the type of monetization they choose or the money they spend.
• Knowing how the player plays, if there are significant difficulties at certain points in the game or is too simple, the time they spend finishing a certain level or the playing time in each session, etc., are important data to be able to adjust the game in testing and balancing phases.
• You can try alternative versions of a given game and analyze which one is "most successful", according to the parameters you want to measure as successful (number of players, playing time, revenue it generates ...)
• Data analysis is also important for adjusting the monetization of a video game.

The subject is contextualized in the area of ​​Production and Business of the Degree in Design and Production of Video Games. The contents are based on a review of the most common metrics in video game design and monetization and make an introduction to inferential statistics and data analysis with machine learning methods. The R language is used throughout the course for the exercises and practical examples. The methodology combines master classes with exercises and practical activities. The evaluation activities are practical exercises and an analytical project that counts 60% of the mark and the remaining 40% corresponds to a final exam. 

The subject has no prerequisites.

The content of the subject consists of the sections listed below:

1. Introduction to data analytics
   1. Importance of data analysis in video games
   2. What is data analysis?
   3. What is game analytics?
   4. Exercises and examples
2. Video game analysis metrics
   1. Types of metrics
   2. Game-specific metrics
   3. Population metrics
   4. Monetization metrics
   5. Marketing metrics
3. Introduction to the R tool
   1. Development environment R
   2. Data management in R
   3. Main orders
   4. Information display
4. Introduction to statistics
   1. Descriptive statistics
   2. Basic descriptive parameters
   3. Graphics
   4. Application of descriptive statistics to the analysis of video game metrics
5. Inferential statistics
   1. Introduction to hypothesis testing
   2. Hypothesis tests of a sample
   3. Two-sample hypothesis tests
   4. Application: A / B test of a video game design
6. Machine Learning
   1. What is machine learning?
   2. Main phases of a data mining process based on machine learning.
   3. Main approaches to machine learning: regression, classification, grouping.
   4. Application to video games
7. Reporting
   1. How to present data analytics information
   2. Drawing conclusions
8. Other visual data analysis tools: Tableau, Microsoft PowerBI.

The contents will be alternated with practical application cases in order to see the usefulness of the contents covered throughout the course. The subject integrates aspects of the sustainable development objectives using practical examples and sets of data that allow analysis and reflection on them.

The evaluation of the subject is:

1. Practical exercises at home or in class: 30%
2. Laboratory practices (analytical project): 30%
3. Final exam: 40%

Continuous assessment activities must be delivered on time within the course specified. Beyond the specified deadlines, the student will not be able to deliver the activities of continuous evaluation, running the risk of suspending the subject for this reason. In the call for recovery it will not be possible to deliver the continuous assessment activities.

The following aspects must be carefully considered:

• Class attendance is mandatory, with a required minimum of 70% attendance.
• The minimum grade for the final exam is 4. If the student gets a lower grade, he will not average with the activities and will have to go to a resit exam. In case of going to recovery, the average will be calculated in the same way, substituting the mark of the exam by the mark of the recovery exam.
• The practical exercises must be delivered on time. Otherwise, they will count a 0 on the note.
• In the analytical practices (analytical project) there will be two delivery dates: the ordinary call and the extraordinary call (for exceptional cases). The analytical practices delivered in extraordinary call will have a maximum of 5. A maximum delivery date will be specified for the extraordinary call beyond which it will not be possible to deliver the internships and therefore will count as a 0. It is recommended that the student does not plan to deliver in the extraordinary call because it involves a decrease in the note.

Garcia-Ruiz, MA (2016). Games User Research. A Case Study Approach. CRC Press.

Wallner, G. (2019). Data Analytics Applications in Gaming and Entertainment. CRC Press.

Ugarte, MD, Militino, Ana F., & Arnholt, AT (2020). Probability and Statistics with R (2nd edition). CRC Press.

de Vries, A., & Meys, J. (2015). R for Dummies. John Wiley & Sons.

Brett Lanz (2013). Machine Learning with R. Learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications. PACKT Publishing.

Magy Seif El-Nasr & Anders Drachen (2013). Game Analytics: Maximizing the Value of Player Data. Springer.

Witten, IH, Frank, E., & Hall, MA (2011). Data Mining. Practical Machine Learning Tools and Techniques. Third Edition. Morgan Kaufmann.

Bari, A., Chaouchi, M. & Jung, T. (2014). Predictive Analytics for Dummies. John Wiley and Sons.

Zumel, N. & Mount, J. (2014). Practical Data Science with R. Shelter Island: Manning.

Arun Sukumar, Lucian Tipi & Jayne Revill (2016). Applied Business Analysis. Available at: bookboon.com.

Brink, David (2010). Essentials of Statistics: Exercises. Available at: bookboon.com.