My name is Polina Guskova, and I work as a data scientist at Electronic Arts (EA). My role involves developing machine learning components for the player and developer experience team. This means I help create systems that enhance how players and developers interact with our games.
If I could offer any advice, it would be to pursue your passions and explore your interests. If you’re passionate about gaming, that’s fantastic! Dive into understanding how games are made. Many people at EA started by wanting to learn the intricacies of game development, with the dream of creating their own games.
Personally, I was captivated by machine learning, and the prospect of applying it to video games was incredibly exciting. I love this field because it still holds vast opportunities for innovation and growth.
Growing up, I was inspired by my father, who was a graduate student. I spent countless hours in a robotics lab, witnessing the latest technological advancements as they emerged. I remember the era of dial-up internet and the evolution of MP3 players. My father was an early adopter of technology, which instilled in me the belief that coding was an essential skill.
I pursued engineering in college, focusing on science and math, and took all the necessary courses. However, what truly fascinated me was machine learning and its incredible applications—teaching computers to perform tasks that were once exclusive to humans.
At Electronic Arts, we are moving towards creating personalized gaming experiences. Imagine a game that is tailored specifically to you, with content that aligns with your interests and difficulty levels adjusted to maximize your enjoyment. This is the future direction of the gaming industry.
I recall taking a programming class in high school, where there were only two girls in a small group, and it was often seen as a “nerdy” pursuit. Fast forward to today, programming has gained immense popularity, with numerous resources available to help anyone get started. The applications of programming are vast and extend beyond traditional tech roles.
Even if you don’t aspire to be a programmer, coding skills can be invaluable in fields like biology or political science. Programming is a crucial skill that I highly recommend everyone explore, as it opens up a world of possibilities and applications.
Engage in a project where you apply machine learning techniques to a simple game. This will help you understand how data science can enhance player experiences. Use platforms like Unity or Python libraries to get started. Reflect on how these techniques could be scaled to larger projects.
Create a passion map to identify your interests and how they align with potential career paths in tech. Consider how your hobbies, like gaming, could translate into a fulfilling career. Share your map with peers to get feedback and new ideas.
Research and create a timeline of technological advancements that have influenced gaming and machine learning. Present your findings to the class, highlighting key innovations and their impact on the industry. This will deepen your understanding of tech evolution.
Participate in a workshop where you design a game with personalized elements. Use player data to adjust difficulty levels and content. This hands-on activity will give you insights into the future of personalized gaming experiences.
Join a coding challenge that focuses on solving real-world problems using programming. Collaborate with peers from different fields to see how coding skills can be applied in diverse areas. This will enhance your problem-solving abilities and coding proficiency.
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[Music]
My name is Pauline Guskova, and I’m a data scientist at Electronic Arts. My job is to build the machine learning components for the player and developer experience team.
If I were to give some advice, it would be to follow your passion and explore your interests. If you love gaming, that’s great! Figure out how games are built. I think that’s how a lot of people actually started out at EA; they wanted to learn how games were created and aimed to build their own games.
For me, I was really interested in machine learning, and the idea of applying that in the context of video games sounded really exciting. Right now, I love it because there’s still so much opportunity left in this field.
When I was younger, I remember my dad was a graduate student, and I would spend hours at a robotics lab, seeing all the newest technology as soon as it came out. I remember dial-up connections and every version of the MP3 player. Since my dad was an early adopter, I grew up with the expectation that I would be coding, and I viewed coding as a necessary skill.
I went to engineering school, studied science and math, and took all the necessary classes. What really excited me, though, was machine learning and its cool applications—how you can teach computers to do things that only humans could do before.
At Electronic Arts, you can think about how instead of just getting a game that’s the same as everyone else’s, you can get a game that’s personalized to you, where the content is unique to your interests, and the difficulty is adjusted to what’s fun for you. That’s the direction the industry is moving towards.
I remember taking programming in high school; it was a small class with only two girls, and it had some nerdy connotations. Now, many years later, programming has become so much more popular. There are many resources available to help you get into it, and there are numerous applications for the skill.
Even if you’re not interested in being a programmer, if you’re interested in biology or political science, there are many applications for programming. It’s such an important skill that I would highly recommend everyone try it out.
Data – Information processed or stored by a computer, which can be in the form of text, images, audio, or other types of files. – The data collected from user interactions is crucial for improving the software’s user interface.
Scientist – An expert in a field of science, often involved in research and experimentation to discover new information. – The data scientist analyzed large datasets to extract meaningful insights for the company’s marketing strategy.
Machine – A device or system that performs tasks or computations, often used in the context of computers and automated processes. – The virtual machine allowed developers to test their applications in different operating environments.
Learning – The process of acquiring knowledge or skills through study, experience, or teaching, often used in the context of machine learning in computing. – Machine learning algorithms are used to predict user behavior based on historical data.
Gaming – The act of playing electronic games, often involving interaction with a user interface to generate visual feedback on a device. – The gaming industry has seen significant growth with the advent of virtual reality technology.
Development – The process of creating, designing, and programming software applications or systems. – Agile development methodologies have become popular for their flexibility and efficiency in software projects.
Coding – The act of writing instructions for a computer to execute, using a programming language. – Coding in Python has become increasingly popular due to its simplicity and readability.
Programming – The process of designing and building an executable computer program to accomplish a specific computing task. – Object-oriented programming allows developers to create modular and reusable code.
Technology – The application of scientific knowledge for practical purposes, especially in industry, including the development and use of computers and software. – Emerging technology trends like artificial intelligence are reshaping the future of many industries.
Experiences – Interactions or events that provide knowledge or skills, often used in the context of user experiences in software design. – Enhancing user experiences is a key focus for developers aiming to increase customer satisfaction.
