LIVE ONLINE COURSE
26 JUL 2023 - 13 SEP 2023
DURATION:
7 WEEKS
MONDAYS & WEDNESDAYS
5 PM PST / 8 PM EST
Every smart decision is data-based. Learn how to leverage data that benefits all gaming departments and positions you higher in the industry.
Join Rick Evans, EA’s longtime data whiz, for a 7-week journey on game analytics and learn what it takes to thrive in the world of gaming.
THIS COURSE IS FOR YOU, IF...
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YOU’RE A DEVELOPER OR DESIGNER NEEDING TO MAKE BETTER DATA-BASED DECISIONS
Aquire analytical skills that’ll teach you about user behavior, industry trends, and monetization. Then take these newly adopted skills into your next analyst position.
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YOU HAVE A PASSION FOR GAMING
Recently graduated? Looking to become an analyst? Let your passion for the gaming industry be explored as you learn the ropes of game analytics.
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YOU’RE A JUNIOR DATA ANALYST LOOKING TO EXPAND YOUR CAREER PATH
Learn the skills needed to switch industries or be prepared to take on more responsibilities with training that can help you become a data scientist.
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YOU’RE A PRODUCT MANAGER IN THE GAME SPHERE
Support your game’s product development roadmap and launch cycles with insights you’ll be able to understand on your own.
Use tools to make data understandable and presentable.
Gamers generate over 50 terabytes of data each day. In the right hands, this data could tell a story, predict player behaviors, and increase revenue.
Invest in the skills that will make your next game a hit.
The most comprehensive analytics course for video games on the web.
Ask your questions live, get instant feedback, and have group discussions with people in your industry.
For 7 weeks you’ll be taught directly by Rick and work on real-world inspired data.
EVANS LinkedIn Profile
- VP of Data and AI Engineering, Pearson
- Spent 11+ years as a Sr. Director of Game Analytics at game studios including Zynga, Atari, and Electronic Arts
- Worked on games such as Plants vs Zombies, The Sims 3 & 4, Words With Friends, Star Wars Galaxy of Heroes, and Farmville (mobile)
- Experienced in diverse areas: education, risk management, e-commerce, and mobile gaming platforms
- Introduces ML-based analytics to foster engaging educational experiences and increase retention time
COURSE INTRODUCTION
What does a game analyst actually do? Get background information on the terms and tools the job entails and a realistic view of the day-to-day tasks that analysts complete.
- Why does data analytics in gaming matter?
- Main tasks of a game data analyst
- Freemium vs paid business models
- Console vs mobile game analytics
Assignment #1:
Pick a game you enjoy (mobile, PC, or console). Write down the top 5 questions you think the game publisher or developer would want to have answered. Then, identify the data points you’d need to collect or derive to answer the questions.
This class will get you to start thinking about how to track data, examine the different categories of metrics, and understand their nuances.
- Top 10 metrics
- Tracking strategies: Event, frequency, initiated
- In-class activity: Define a metric
Assignment #2:
Research vendors that provide software as a service for tracking game analytics to understand how their services work. What would a game studio need to use their services, and what do they specifically provide? Create a one-page summary.
The game dev pipeline is filled with stages. In this class, you’ll learn the different stages of building a game and see where and how data analysts get involved.
- Planning, pre-production
- Production, testing
- Pre-launch, soft launch, launch, post-launch
- Game analytics process: Main stages
- A/B testing & experimentation
Assignment #3:
Use mock data to carry out A/B tests and measure statistical differences. Turn in your results and interpretations.
Every department needs data to help them make decisions. Learn the priorities of different areas of your business and see how you can align data with each team’s needs.
- Understanding core business needs
- Asking the right questions
- Root cause analysis
There’s a way to keep players interested and regularly playing your game. Learn how to keep them engaged and have data to support it.
- Why do you need to understand groups?
- Common groups
- Motivational analysis (compulsion loops)
- How to define groups and relationships
- Explorative analysis
- Game data mining
Assignment #4:
Develop groups of players based on a motivational analysis.
Time to get hands-on and work with data as an analyst would. Practice your skills with today’s class assignment.
- Transforming a loading data into a database structure
- Preparing data for analysis
- Demo: Using SQL
- Workshop: Break out in groups, use the prepared mock data, query tables, and look at the results
Assignment #5:
Load some mock data into DBeaver and perform a data quality check. Create a descriptive analysis of the data to produce a report on each column.
In this lesson, you will challenge the way look at data and analyze how you define metrics.
- Top 10 Metrics
- Data vs metrics vs KPIs
- Transforming data into metrics
- Setting KPIs for your project
Assignment #6:
Use the dataset provided and pick 3-4 metrics from class and figure out what the values are.
Explore the reasons why players spend money and how you can get them to spend more.
- Monetization models
- Basic principles on how to monetize projects
- Monetizations funnel: Metrics (user generation, virality, retention, conversion, revenue)
- Analyzing financial and game indicators
- In-game economy (bottlenecks, churn rate)
- LTV
Assignment #7:
Analyze the game you’re currently playing and think how the in-game economy works. Determine the main spending factors.
Imagine you trying to get information on game features. You want to determine if the users enjoy the feature, if it’s bringing in more money, and if the players are having fun. In this class, you’ll analyze data and evaluate features to see what’s working.
- Selecting cases and features
- Running the analysis
- Evaluating the results
- Evaluating new features
Assignment #8:
It’s time to see if we should adjust certain features. Use mock data for feature lift analysis.
In this class, you’ll have the chance to familiarize yourself with 4 tools and take data from DBeaver and connect it to Tableau. And by the end of the lesson, you’ll know the difference between good and bad visualization.
- Principles of visualizing your data
- Helpful tools (e.g. Tableau, R/RStudio, Power BI, Looker)
- Demo: Using Tableau (Public)
- Workshop: Connect Tableau to the existing data. Create a report in Tableau.
Assignment #9:
Deliver impactful messages by turning data into clear visuals.
What does an experienced game analyst do differently when creating presentations? Get guidance through the reporting process and learn the right way to present your findings.
- Structuring a concise, clear presentation of findings
- Effectively communicating the findings
- Learn to deliver business impact & persuade
Assignment #10:
Analyze a presentation written by another analyst communicating their findings. Think outside the box to add your suggestions and comments on how to improve their presentation.
As an analyst, you’re the go-to person for insights. Learn how to be useful to your team and help them narrow down what their seeking. And walk into studio life prepared as you find out how to manage these requests and fit in to help.
- People on your team (game designers, engineers, analysts, producers, community managers, PMs, marketing)
- Managing analytics requests from different teams
- Art of collaboration / handling conflicts
- In-class activity (role play): Break out in teams, try to communicate on the project ideas from different standpoints (producer, analyst, developer)
There are certain words that game industry recruiters look for. Take a look at an analyst resumé to see how it applies to the industry and put the right words on your resume so it stands out.
- Polishing your resume
- Common interview questions
- In-class activity: Work in groups on a Fermi problem
- Do’s and don’ts during the interview
Assignment #11 (Optional):
Resumé redlining (create & review).
You’ve learned the ABCs of game analytics, now here’s an introduction to diverse types and sizes of data that you might come across in your career path.
- Machine learning and Big data
- Tools (Python, AWS, NLP, BigQuery, Snowflake)
- Prospects
- Future trends
What our students say
"It's extremely well planned and organized. I know exactly what we're doing each class and what's expected of me and most everything just seems to work as expected."
"The material is pretty easy to follow along, even if you do not have an analyst background or specialty coming in. I appreciate the clarity for the fundamentals we are covering."
"Rick is definitely an expert in the field but understands that not everyone here is at the same level of experience. I feel like the content is both helpful to those that are already many years into data analytic careers, and even those who have just started their data journeys."