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Game Data Science Using R

Game Data Science Using R

Gain insights into game data science by learning data extraction, visualization, clustering, supervised learning, neural networks, and sequence analysis using R, impacting business decisions in social games.

Intermediate

151 Lessons

45h

Certificate of Completion

Gain insights into game data science by learning data extraction, visualization, clustering, supervised learning, neural networks, and sequence analysis using R, impacting business decisions in social games.

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This course includes

283 Playgrounds
11 Quizzes

This course includes

283 Playgrounds
11 Quizzes
Course Overview
What You'll Learn
Course Content

Course Overview

Game data science is emerging as a significant field of study due to the emergence of social games embedded in online social networks. The ubiquity of social games gives access to new data sources and impacts essential business decisions, given the introduction of freemium business models. Game data science covers collecting, storing, analyzing data, and communicating insights. This course will teach you game data extraction, processing, data abstraction, data analysis through visualization, data clusterin...Show More

What You'll Learn

Ability to use machine learning applications in game data science

Hands-on experience of the R programming language in real-world applications

Ability to build solid theoretical knowledge of game data science

Learn to collect, visualize, analyze, and transform game data

Learn about data clustering, supervised learning, neural networks, and sequence analysis

What You'll Learn

Ability to use machine learning applications in game data science

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Course Content

1.

Getting Started

Get familiar with the basics and significance of game data science using R.
Why Game Data Science?
2.

Introduction to Game Data Science

Walk through game data science concepts, from game analytics to metrics and player profiling.
Game Analytics
What is Game Data?
Historical Context for Game Data Science
Era of Game Data and Machine Learning
The Process of Game Data Science
What are Metrics in Game Data Science?
Introduction to Customer Metrics
Introduction to Gameplay Metrics
Key Performance Indicators
Importance of Metrics to Game Data Science
Summary: Basics of Game Data Science
Quiz on Basics of Game Data Science
3.

Data Preprocessing

Work your way through data preprocessing steps, from reading and parsing to cleaning and normalization.
About This Chapter
What is Data Preprocessing?
Data Example and Measurement Types
Process for Preprocessing
Reading and Parsing Files
Purpose of Cleaning and Data Type Checks
Data Consistency Processing
Data Normalization and Standardization
Summary: Data reprocessing
Quiz on Preprocessing of Data
4.

Introduction to Statistics and Probability Theory

Grasp the fundamentals of statistics, probability, and their applications in game data analysis.
Introduction to Probability and Statistics
Measures of Centrality
Measure of Spread
Introduction to Correlation Analysis
Introduction to Inferential Statistics
T-tests and its Types
Introduction to Analysis of Variance (ANOVA)
Introduction to Probability
Summary: Statistics and Probability Theory
Quiz on Statistics and Probability Theory
5.

Data Abstraction

Dig into data abstraction methods using feature engineering, extraction, and selection techniques.
Significance of Behavioral Telemetry
Introduction to Dataset
Introduction to Feature Extraction
How to Deal with Nominal and Ordinal Measures?
The Process of Feature Selection
Introduction to Entropy
Summary: Data Abstraction
Quiz on Data Abstraction
6.

Data Analysis through Visualization

9 Lessons

Investigate visualization techniques to analyze player behavior using heatmaps, spatio-temporal, and state-action systems.
7.

Clustering Methods in Game Data Science

21 Lessons

Build on clustering techniques to analyze player behavior and extract actionable insights.
8.

Supervised Learning in Game Data Science

23 Lessons

Learn how to use supervised learning methods in game data science for player behavior prediction.
9.

Model Validation and Evaluation

11 Lessons

Grasp the fundamentals of model validation, performance metrics, and evaluation techniques in machine learning.
10.

Introduction to Neural Networks

10 Lessons

Examine neural networks for predicting player behavior and purchases in game data science.
11.

Sequence Analysis of Game Data

14 Lessons

Grasp the fundamentals of sequence analysis in game data, exploring player behavior and patterns.
12.

Advanced Sequence Analysis

13 Lessons

Map out the steps for advanced sequence analysis techniques in game data science.
13.

Case Study: Tom Clancy's The Division (TCTD)

5 Lessons

Investigate SNA to identify influencers, impacting player retention and community engagement.
14.

Conclusion and Remarks

3 Lessons

Master the steps to understanding game data science, addressing ethical considerations, and exploring advanced methodologies.
15.

Appendix A: Game Used in the Book

1 Lesson

Learn how to use VPAL Game for data analysis and player behavior insights.

Course Author

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