Game Data Science Using R
Intermediate
151 Lessons
45h
Certificate of Completion
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 f...Show More
Course Content
Getting Started
1 Lesson
Introduction to Game Data Science
12 Lessons
Data Preprocessing
10 Lessons
Introduction to Statistics and Probability Theory
10 Lessons
Data Abstraction
8 Lessons
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
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. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.
Start learning immediately instead of fiddling with SDKs and IDEs. It‘s all on the cloud.
Built-in assessments let you test your skills. Completion certificates let you show them off.