INTERACTIVE COURSE

Game Data Science Using R

Intermediate

151 Lessons

45h

Certificate of Completion

AI Explanations

283 Playgrounds

11 Quizzes

241 Illustrations

Takeaway Skills

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

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

Course Content

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

9 Lessons

Clustering Methods in Game Data Science

21 Lessons

Supervised Learning in Game Data Science

23 Lessons

Model Validation and Evaluation

11 Lessons

Introduction to Neural Networks

10 Lessons

Sequence Analysis of Game Data

14 Lessons

Advanced Sequence Analysis

13 Lessons

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

5 Lessons

Conclusion and Remarks

3 Lessons

Appendix A: Game Used in the Book

1 Lesson

COURSE AUTHOR

How You'll Learn

You don’t get better at swimming by watching others. Coding is no different. Practice as you learn with live code environments inside your browser.

Videos are holding you back. Educative‘s interactive, text-based lessons accelerate learning — no setup, downloads, or alt-tabbing required.

Learn faster and smarter with adaptive AI tools embedded in every Educative course.

Built-in assessments let you test your skills. Completion certificates let you show them off.

FOR TEAMS

Interested in this course for your business or team?

Unlock this course (and 1,000+ more) for your entire org with DevPath