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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

Course Overview

What You'll Learn

Course Content

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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

Game data science is emerging as a significant field of study due to the emergence of social games embedded in online social net...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

Ability to use machine learning applications in game data science

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Course Content

1.

Getting Started

1 Lessons

Get familiar with the basics and significance of game data science using R.

Why Game Data Science?

2.

Introduction to Game Data Science

12 Lessons

Walk through game data science concepts, from game analytics to metrics and player profiling.

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

10 Lessons

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

10 Lessons

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

8 Lessons

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.

Overview of the Chapter

Introduction to Heatmaps

Spatio-Temporal Visualization Systems

Exploring Spatio-Temporal Player Behavior in Dota 2

State-action Visualization Systems

Using State-action Transition to Compare Players’ Choices

Exploring Players' Dialogue Choices (Lab)

Summary: Data Analysis Through Visualization

Quiz on Data Analysis Through Visualization

7.

Clustering Methods in Game Data Science

21 Lessons

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

Introduction to Clusters

What is Cluster Analysis?

Clustering Models

The Clustering Process

Challenges in Applying the Clustering Process

Evaluating and Tuning the Results

Introduction to The K-means Algorithm

An Example of K-Means From the Wild: Tera Online

The k-Means lab

Introduction to Fuzzy C-Means

Fuzzy C-Means Lab

Basics of DBSCAN

Introduction to Hierarchical Clustering Methods

Hierarchical Clustering Lab

Introduction to Archetypal Analysis (AA)

Archetypal Analysis Lab

Introduction to Model-based Clustering

Advice on Applying Cluster Methods

Summary: Clustering Methods in Game Data Science

Quiz on Clustering Methods

8.

Supervised Learning in Game Data Science

23 Lessons

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

Introduction to Machine learning

Supervised Machine Learning

Ways to Categorize Prediction Models

Introduction to Regression Methods

Linear Regression Lab

Introduction to Classification

K-nearest-Neighbour(KNN) Lab

Introduction to Naive Bayes Theorem

Naive Bayes Lab

Introduction to Logistics Regression

Logistic Regression Lab

Introduction to Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA) Lab

Introduction to Support Vector Machines (SVMs)

Support Vector Machine Lab

Kernel Methods with Support Vector Machines

Lab of Support Vector Machines with Kernel Methods

Introduction to Decision Trees

Decision Tree Lab

Introduction to Random Forests

Random Forests Lab

Summary: Supervised Learning in Game Data Science

Quiz on Supervised Learning

9.

Model Validation and Evaluation

11 Lessons

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

Introduction to Machine Learning Pipeline

How to Determine if the Model Is Good or Bad

Lab of Confusion Matrix

Introduction to Area Under the Curve (AUC)

Introduction to Regression Metrics

Regression Metrics Lab

What is Model Validation Process?

Cross-Validation Lab

How to Debug a Model That Is Already Trained

Summary: Model Validation and Evaluation

Quiz on Model Validation and Evaluation

10.

Introduction to Neural Networks

10 Lessons

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

What are Neural Networks?

What is a Feedforward Neural Network?

Working of a Feedforward Neural Network

How to Train Feedforward Neural Networks

Usage of Deep Neural Networks in Game Data Science

Introduction to Convolutional Neural Network

How Does the Convolutional Layer Work?

Usage of CNNs in Game Data Science

Summary: Neural Networks

Quiz on the Neural Networks

11.

Sequence Analysis of Game Data

14 Lessons

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

Introduction to Sequence Analysis

Why Sequence Data Analysis?

Sequence Mining Enables Player Profiling: A Case Study

Exploratory Sequence Data Analysis

Inspecting Frequent Patterns in Explorative Analysis

The Entropy of States in Explorative Analysis

Lab of Exploratory Sequence Data Analysis

Introduction to Sequence Pattern Mining

Introduction to Clustering of Sequences

Lab of Clustering Sequences

Summary: Sequence Analysis of Game Data

Quiz on Sequence Analysis of Game Data

12.

Advanced Sequence Analysis

13 Lessons

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

Introduction to Advanced Sequence Analysis

Probabilistic Planning-Based Approach

Introduction to PHATT planning approach

Introduction to Bayesian Networks

What are Hidden Markov Models (HMMs)?

Introduction to Markov Decision Process (MDP)

Usage of the Markov Decision Process in Games

Extension of Markov Decision Process

Introduction to Markov Logic Networks (MLNs)

Introduction to Recurrent Neural Networks (RNNs)

Usage of Recurrent Neural Networks (RNNs) in Games

Summary: Advanced Sequence Data Analysis

Quiz: Advanced Sequence Analysis

13.

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

5 Lessons

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

Introduction to the Case Study

Game: Tom Clancy’s The Division (TCTD)

Data Analysis in the Case Study

Results of the Case Study

Discussion and Conclusion of the Case Study

14.

Conclusion and Remarks

3 Lessons

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

Game Data Science Process

Other Topics in Game Data Science

Copyright Information

15.

Appendix A: Game Used in the Book

1 Lessons

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

Course Author

Oxford University Press

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