top of page
Game Analytics

Masterclass: Game Analytics

This course combines the Game Analytics Fundamentals and Advanced Game Analytics courses to provide a complete and comprehensive overview of game analytics.

8 Weeks
32 hrs Of Instruction

 

Next Course:     
July 2024

What You'll Learn

The Gaming Industry: A brief history of the gaming industry. The main motivation of today’s video and mobile games. Game industry strategy, understanding business needs and innovation types.


Game Analytics Database Setup: How to Install a local MySQL database server, setup tables and load course data. How to install an SQL client tool (DBEAVER) for querying and accessing the data. How to validate your database installation.


Jupyter Notebooks: How to install and use Jupyter, a web-based notebook for analytics. How to use Jupyter to analyze data using SQL and Python. How to use Python libraries for game analysis. Common Python libraries for statistics, visualization, machine learning and more. 


Analysis Frameworks: What are analysis frameworks. How are analysis frameworks used in gaming. Framework examples used in mobile gaming including registration and game launch. 


Game Development Lifecyle: The stages of game development. What roles does a game analyst have for each stage. What are soft launch, pre-production and live service. 


Data Preparation: How data is prepared for analysis. Common data issues and challenges in gaming. Root-cause analysis. Data cleaning, transformation and reduction processes. 


Top 10 Metrics & KPIs:  The most important gaming metrics, how they are derived,  where they are used. The difference between metrics, KPIs and OKRs. Different categories of metrics: acquisition, engagement, retention and monetization and how they are derived.


Player Engagement: What is player engagement. Why is it critical for a successful game title. How we measure player engagement effectively.  


Return Rates: Advanced concepts in retention. Engagement segments and grouping. Engagement forecasting for marketing campaign spend. How to understand inflows and outflows for subscriber growth monitoring.


Player Segmentation: Example segments common in mobile gaming. Motivation and compilation loops. How to discover player segments. Advanced analysis  techniques like K-means clustering and decision trees. How to use SQL and Python libraries to segment players and build models. 


Economies & Monetization: How economies work with compulsion loops. What is TRUE and FREE spend. The inflows and outflows of a game economy. How to analyze a game economy and how to assess economy balance. Example economy analysis and how it relates to monetization.


A/B Testing: How A/B testing is effectively used in game design and development. Basic statistical testing and confidence intervals. Which test is for which data. Experiment design best practices. 


Predictive Model Building: The model building process. Basic techniques for predictive model building. How models are used in gaming. Examples of models for churn, payer prediction and revenue forecasting. 


Feature Lift Analysis: How to deconstruct a feature and apply analysis techniques to measure its performance. How to use feature lift analysis to predict the impact of marketing campaigns and understand their impact. 


Capstone Project: Capstone project requirements. Capstone project template and recommended approach. How to utilize assignments and course work for your capstone project.

Success strategies

Masterclass: Game Analytics

This course combines the Game Analytics Fundamentals and Advanced Game Analytics courses to provide a complete and comprehensive overview of game analytics.

8 Weeks
32 hrs Of Instruction

Next Course:

July 2024

This Course Includes

  • Office Hours With Instructor, Schedulable Instructor 1-On-1s

  • 32 Hours of Instruction Delivered Over 16 Lessons

  • Downloadable Lesson Slides, Downloadable SQL and Python code

  • Lesson Assignments, Lesson Quizzes, In-Class Exercises

  • Final Course Quiz, Capstone Project For Analytics Portfolio

  • Certificate Of Completion, Digital Certificate Credentials

Course Content

LESSON 01: Game Analytics Essentials

  • Course Introduction, Overview & Structure

  • Why Does Analytics In Gaming Matter?

  • History & Background Of Game Analysis

  • Game Analyst Main Tasks

  • Game Industry Business Models

  • Freemium vs Paid Games

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 09: Advanced Retention & Engagement

  • Weekly & Monthly Inflows/Outflow

  • Lapsers And Return Rates

  • Committed Users (Subscribers)

  • Forecasting With DAU & Retention

  • Workshop: Minimum Install Rate

  • Lesson Assignment & Quiz

LESSON 02: Analysis Frameworks & Tracking Strategies

  • Analysis Frameworks

  • Top Game Metrics By Category

  • Event Capture & Tracking Strategies

  • Example Game Capture: Wordle

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 10: Advanced Monetization & Economies

  • Pricing/IAP Analysis 

  • The Layer Cake Model

  • Economy Inflow/Outflow

  • Understanding Economy Balance

  • True vs Free Spend

  • Workshop: LTV Calculation

  • Lesson Assignment & Quiz

LESSON 03: Development Lifecycle & Analytics Process

  • Game Development Lifecycle

  • Planning, Pre-production, Testing

  • Soft Launch, Production & Live Service

  • Game Analysis Process Overview

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 11: Integrating Jupyter Notebook & Python

  • Installing Jupyter Notebooks

  • Access To MySQL Database

  • Python Libraries For Data Analysis

  • Workshop: Gaming Analysis With Python

  • Lesson Assignment & Quiz

LESSON 04: Game Analysis & Business Needs

  • Types Of Innovation & Mission Statements

  • Business Needs & Analysis Strategies

  • Example: Acquisition & LTV

  • Asking The Right Questions

  • Root Cause Analysis

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 12: Groups & Relationships - Part II

  • How To Discover Player Segments

  • Workshop: Segment Identification

  • Cluster Analysis Using Python

  • Lesson Assignment & Quiz

  • Workshop: Payer Segmentation Analysis

LESSON 05: Player Groups & Relationships - Part I

  • Game Groups & Relationships

  • Personalization & Its Importance

  • Gaming Group: Examples

  • Compulsion Loops & Economies 

  • How To Define & Analyze Groups

  • Data Classification

  • Explorative Analysis

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 13: Feature Analysis

  • Deconstructing Features

  • Feature Analysis Frameworks

  • Example: Fortnite Loot Chest

  • Workshop: Exploratory Analysis

  • Workshop: Flash Sale Analysis

  • Lesson Assignment & Quiz

LESSON 06: Data Pre-Processing

  • Data Pre-processing Concepts

  • Data Quality & Data Cleaning

  • Data Transformation & Reduction

  • Common Data Challenges In Gaming

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 14: Experimentation (A/B Testing)

  • Statistical Significance

  • Which Statistical Test

  • Confidence Intervals

  • How To Setup An Experiment

  • Experimentation Best Practices

  • Lesson Assignment & Quiz

LESSON 07: Game Metrics & KPIs - Part I

  • Data Metrics & KPIs

  • OKRs

  • Top 10 Game Metrics - Part I

    • Sessions, Session Days

    • Acquisition (Installs, Funnels)

    • Engagement (DAU, MAU, etc.) 

  • Analysis Workshop 

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 15: Advanced Analysis Workshop - Part I

  • Advanced SQL Query Techniques 

  • Efficient Sampling & Querying

  • Predictive Model Building

  • Workshop: Payer Prediction Model

  • Capstone Project - Part I

  • Lesson Assignment & Quiz

LESSON 08: Game Metrics & KPIs - Part II

  • Setting Your Own KPIs

  • Defining Your Own Metrics

  • Top 10 Game Metrics - Part II

    • Retention

    • Monetization

    • Churn

  • Analysis Workshop 

  • In-Class Exercise

  • Lesson Quiz & Assignment

LESSON 16: Advanced Analysis Workshop - Part II

  • Workshop: Churn Prediction Model

  • Workshop: Decision Tree Analysis

  • Workshop: NLP For Game Reviews Analysis

  • Capstone Project - Part II

  • Lesson Assignment & Quiz


Requirements

  • Ideal candidates for this course should have at least 1-2 years of analysis experience, either as a data analyst, product manager or business analyst.

  • While not a prerequisite, candidates taking this course would benefit from some experience using SQL for data extraction and analysis. 

  • Candidates with no prior SQL experience should review the Game Analysis With SQL Course, which we recommend before taking this course. 

  • Learners who purchase multiple courses are entitled to bundle discounts. 

  • Learners will need to be able to install MySQL, DBeaver, Python and Jupyter Notebook software on their Windows or Mac PC to access and use the course data and code.

Description

This course is ideal for anyone looking to start or transition to a career as a game analyst in the mobile, console or PC games industry. This course is a great fit for anyone passionate about gaming and currently in a data analyst role, in the games industry, or adjacent such as a product manager, game designer, or producer who needs to learn more about the game analysis process. 


In this course, learners will learn game-specific analytics requirements, including terms, metrics and analytical frameworks and use these to develop the analytical skills required to derive insights from game data. 


Learners will install game data from real and simulated games to use throughout this course in various workshops and assignments, as well as their capstone project, to enhance their practical understanding of game metrics. 


Furthermore, learners will understand how to build predictive models for Lifetime Value (LTV), player churn, player payer propensity prediction, as well as how to segment users into different groups based on engagement and spending behavior.


In addition, learners will understand how experimentation, or A/B testing, is used in gaming to optimize the player experience through statistical inference, as well as how feature lift analysis can provide insights that can be used in DAU and revenue prediction. 


The course features several assignments, quizzes to ensure learners develop a full understanding of the course materials and learners can complete a capstone project using all the knowledge and skills developed in the course to earn a course certificate with distinction.


bottom of page