Earlier this year, we partnered with Black and Brilliant to launch an Artificial Intelligence Accelerator program, aimed at upskilling the next generation of AI superstars. Over the course of ten weeks, participants explored topics like responsible AI and social justice, the business of AI, programming and data engineering, and more with mentorship and coaching from industry experts.
We’re excited to share with you some of the key highlights from the program.
Spotlighting the role of Data Engineer
When it comes to the world of AI, data engineers play an important role in getting machine learning models into production. And AI Accelerator learners were able to get insights into the role of the data engineer from accelerator coaches.
“We work with [data scientists] to understand what they’re trying to build. And we help them set up the foundations. Then we work with them — when they’ve actually built the models — to take what they’ve built and deploy it,” explains Nana Essuman, Director of Data Engineering at Condé Nast.
What does it take to become a data engineer? The coaches walked learners through the core skills that data engineers need to know, including data processing, data storage, containerization, caching, and more.
For aspiring data engineers who are just starting out, Femi Anthony, a Lead Data Engineer at Capital One, shares his advice: “I would learn Python, take some introduction to data science courses, and make sure you have a GitHub presence. There are lots of open data sets out there. Use the skills that you’ve learned to come up with some sort of project that you can put on GitHub and advertise your skills. That way, you’re reinforcing what you’re learning. And because you’re learning by doing, you get the actual practical experience.”
To learn more about this role and how it differs from that of a Data Scientist, check out our article on data science vs. data engineering.
Diving into AI and cybersecurity
Participants also got to learn about AI and cybersecurity. Coach Ruth Ikwu dove deep into the world of cybersecurity, and explored possible career paths for anyone interested in this space in a session over Zoom. Ruth is a Research Associate at Cardiff University’s Center for Cyber Security Research and works as a data engineer.
Ruth explained the current approach to cybersecurity, called proactive cyber defense. “The proactive approach seeks to understand hacker behavioral intent, and the use patterns in previous attacks and new occurrences. And then you develop models that rely on previously observed data to establish norms for your network and search for deviations from the norm,” Ruth says.
But when it comes to predicting cyber attacks, there’s still progress to be made. “We are still not able to predict cyber attacks happening in the future. Organizations are more interested in solutions that help them either reduce response time — that is, time between when we find out that we have been attacked, and when we’re able to start mitigating against that attack. Or they want to use their cyber resources more efficiently,” Ruth explains.
For anyone interested in pursuing a career in AI and cybersecurity, Ruth shares that you’ll need to know about hacking, math and statistics, machine learning and data science, and have some domain knowledge (like knowing how to implement machine learning models without breaking the law). Key roles in this field include AI and Cyber Security Researcher and Threat Intelligence Analyst.
If you’re interested in this field, check out our Introduction to Cybersecurity course.
Storytelling with data visualization
Learners also attended a panel discussion in which designers and data visualization experts looked at different ways to present data, and the skills needed for working on data-related projects in any domain.
What are the most important things for data scientists to keep in mind when presenting data? “You should just look at it and get what the story is. You shouldn’t have to analyze the fact that it is a large amount of data being presented to you. Simplicity and legibility — to me, those are the big things,” says Marc Maleh, GVP of Emerging Experiences at Huge.
When it comes to creating interactive experiences, Erick Gaston, a UX Designer at Google, explains that there are a lot more factors to consider when presenting data: “It’s one thing if you’re working on a print project, and you’re creating something for a magazine or an article, because it’s static. But once you introduce that interactivity, there’s a lot of other factors that will come into play about how to make that experience more effective. If you have a really large dataset, you can’t display everything at the same time. So how do you make those decisions about what you filter, what you display, how you build consistency, and how your data works across devices?”
Large projects can require collaboration across domains like engineering, data, product, design, and research. Mahir Yuvaz, Director of Engineering at Etsy, shares some of the hard and soft skills to know for successful collaboration. These include hard skills like data engineering, insight generation, and system design. And soft skills such as being a data ambassador, storytelling, and ethics. “These are important for running any type of project in any domain,” Mahir says.
Interested in learning more? Visit the Black and Brilliant website or join The Black and Brilliant Advocacy Network on LinkedIn to stay in the loop! And if you’re interested in learning more about AI, machine learning, and data science, check out our Data Scientist Career Path, which AI Accelerator participants worked through during the program.
