Data is changing the game for the professional sports industry, giving athletes new ways to track biometrics and optimize their performance. Competitive esports is no different: There’s high demand for Data Scientists who can interpret all of the data coming out of video games and help coaching staff make informed decisions.

Evil Geniuses is one of the oldest competitive esports gaming organizations, with rosters of pros in games like League of Legends, Dota 2, and Counter-Strike. Even if you’re familiar with esports, you might not know that the organization employs as many Data Scientists and Engineers as it does Coaches — a fun detail that CEO Nicole LaPointe Jameson recently shared with The Washington Post.

Ivan Sheng is one of the Data Scientists at Evil Geniuses, where he focuses on the games League of Legends and Counter-Strike. “I take data from video games and I create predictions and business use cases in order to have an edge over our competition,” Ivan explains. (Ivan is also a Codecademy contributor and worked on our recent course Introduction to Big Data with PySpark.)

Here’s how Ivan uses Python and SQL to help esports pros strategize and succeed, plus the programming languages you need to know to become a Data Scientist in competitive esports.

What got me interested in the job

“I’ve actually known about Evil Geniuses since I was in middle school. I had a friend who was really into this one game called StarCraft: Brood War. We started watching clips on YouTube and this concept out of South Korea called ‘esports’ popped up. I got addicted to it, and one of my favorite players was signed with Evil Geniuses.

My introduction to data was kind of interesting. As a junior in college, you’re kind of desperate for internships, so I just shot my resume everywhere. One day I got a call from an unknown number, and it was apparently an interview for a data analyst internship at a marketing agency that I forgot that I applied to. Going from a mechanical engineering major into a marketing agency where everyone’s super young and sociable was a culture shock for me. I kind of fell in love with it.

I had two really great mentors that I still keep in touch with today — one who is also a Data Scientist, and taught me pretty much everything I know about data. Once I entered the job market, I had a manager who gave me my foundations in Python and natural language processing, and then that’s pretty much where everything kind of just skyrocketed.”

How I got in the door

“I did some contract work for Evil Geniuses prior to actually going full-time with them. I would do some data science work, write about it, and submit it to one of our ESPN-like websites called Factor.

That eventually evolved into me doing some software development work; Monte Carlo simulations and things like that. And then that blossomed into a conversation of, ‘I’m graduating and I’m going to start interviewing.’ Our Chief Innovation Officer basically said, ‘Hey, do you want to come over here?’ I was like, ‘Yeah, I’ll entertain it. Of course!’

It’s super cool to work with products or companies that you’re familiar with, because you have some personal stake in it. I came to Evil Geniuses from Disney Streaming, where I got to see the birth of Disney+ and all that fun stuff. I had the same good feelings coming over to Evil Geniuses, because I’ve known about this since I was a kid as well.”

What I actually do every day

“I am basically sitting down in Python, just coding up lots of different things. As for what I’m coding, it depends on the day and the task. We’re a bit of a small company, so I’ve done work all the way from creating automated data pipelines to creating models in the deterministic and non deterministic side of things. On our team, we’re kind of split between gaming and marketing: I’m working with gaming data like 60% of the time, and then 40% on the business side of things.

I’ve also done some software development work in terms of flagging interesting in-game events to kind of let our coaches know like, This thing happened at this specific time point in the game. Maybe you guys could check it out? Maybe this was a big turning point in the game? I don’t directly talk to our pros. I usually talk to the coaches or an analyst who works directly with the team — those people are the main stakeholders.

Now I’m following more esports, because I have to learn about all these different games. Like, I never really got that into Counter-Strike before, but that’s my biggest responsibility now. So I’ve been watching that endlessly.”

Here’s what you need to get started

If you’re interested in becoming a Data Scientist, it’s a good idea to learn Python. Ivan strongly recommends that you learn SQL too, because it’s frequently used in Data Science careers. “I think it’s criminal to underrate SQL as a language,” Ivan says.

Inspired to start your journey to becoming a Data Scientist? Here are the courses and paths that will help you pick up the skills you need for the field:

• Getting Started with Python for Data Science: This free course is perfect for complete beginners. You’ll learn how to analyze and visualize data with Python and pandas, then put your skills to work with real datasets.
• Data Scientist: Analytics Specialist: In this Career Path, you’ll learn how to use SQL and Python to answer big questions with data. There’s even a portfolio project where you’ll be analyzing streaming data from players on Twitch.
• Analyze data with SQL: This beginner-friendly SQL path will have you querying data right away, plus includes prep for technical interviews.
• Analyze data with Python: With Python, you can present data in compelling visuals like graphs and charts. In this course, you’ll also learn how to use common Python libraries like NumPy, Matplotlib, and Pandas.
• Fundamental Math for Data Science: “Having a strong statistics foundation is just as important as your programming capabilities,” Ivan says. Need to brush up on your math? This skill path will review key statistics, algebra, and calculus concepts and help you build a foundation for more technical work.

There’s more to being a successful Data Scientist than just programming and software engineering: Communication skills are a must, because you’ll often have to translate data and findings for people who aren’t data-oriented, Ivan says.  

Once you reach a stage where you’re applying to jobs and landing technical interviews, don’t skimp on practice, Ivan says. “With a lot of these tests, especially the SQL ones, there’s a pattern that you can figure out — because they always test very similar concepts,” he says. Our skill path Data Analyst Interview Prep is a great way to get familiar with common interview questions and practice formulating answers.

Ivan’s other tip for slaying a technical interview or live programming test? “Be very open and communicative with your recruiter,” he says. “They’re there for you and want to see you succeed, so it’s important to have a back-and-forth with them if you’re ever confused.”