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Game Data Science Using R

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Getting Started

Why Game Data Science?

Introduction to Game Data Science

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

Data Preprocessing

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

Introduction to Statistics and Probability Theory

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

Data Abstraction

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

Data Analysis through Visualization

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

Clustering Methods in Game Data Science

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 DBSCAN Lab 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

Supervised Learning in Game Data Science

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

Model Validation and Evaluation

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

Introduction to Neural Networks

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

Sequence Analysis of Game Data

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

Advanced Sequence Analysis

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

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

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

Conclusion and Remarks

Game Data Science Process Other Topics in Game Data Science Copyright Information

Appendix A: Game Used in the Book

Lab for SPADE

Learn to code SPADE in this lab

We'll cover the following

Goal

Goal

In this lab, we’ll apply the SPADE algorithm discussed in the chapter on the dataset we introduced in the last lab.

For the purposes of clarity and demonstration, we’ll use a small subset of the data containing only 50 sequences (or sessions).

The first step is for us to load the right libraries to use the package. To use the SPADE algorithm, we’ll use the arulesSequences library.

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