Artificial intelligence (AI) and machine learning will undoubtedly shape the course of technology across fields ranging from health care to self-driving cars to financial investing. Data scientists and statisticians remain the oft-unsung heroes of the AI boom as they play a key role in extracting insights from complex datasets to design the algorithms and data processing methodologies that – put simply – make AI more intelligent.
But what draws someone to data science in the first place? Is a childhood love of math enough to drum up dreams of data mining and analysis?
“If you talk to middle school kids and ask them what they want to do when they grow up, surprisingly, nobody [would raise] their hand and say, ‘Data scientist!’ It’s not necessarily on their radar,” said Delfi Diagnostics co-founder and Head of Data Sciences Robert Scharpf during a recent webinar hosted by the University of Maryland’s Biocomputational Engineering degree program. “I started out more in traditional biology in basic science research and became more interested in data analysis than in laboratory experiments that were used to generate the data. And, that led me to a biostatistics program.”
Delfi Diagnostics is a Baltimore-based company committed to developing high-performing, affordable blood tests for early detection of cancer across multiple tumor types. Delfi’s technology utilizes low-cost, widely available sequencing technology and taps cell-free DNA fragments in the bloodstream for clues about whether a person may have early-stage cancer.
As manager of biomedical engineering & sciences at Exponent Christie Bergerson described in the same event – artificial intelligence represents the wider universe, and machine learning a portion of that universe. “Machine learning can be what’s considered supervised, unsupervised, or semi-supervised depending on the algorithms,” she said. “‘Unsupervised learning’ is where you let the algorithm find patterns for itself, and it’s potentially the scariest because it is the least controlled. But, it is also potentially the most useful because computers see things differently than humans. They have access to different data points.”
For those looking to branch into the world of AI and machine learning, a degree that merges biology, data science, and programming could be the gateway.
“The foundations of machine learning have their roots in statistics and computer science,” Bergerson said. “I encourage people to think beyond their bachelor’s degree about what would present them with the most opportunities for collaboration and mentorship from faculty – such as [deciding between] in-person and online programs. [Students in the latter] might miss out on those opportunities.”
If you’re interested in enrolling in a program that could set you on the path to a career in AI, the University of Maryland’s Biocomputational Engineering B.S. degree program may be the perfect fit for you!
By enrolling in this cutting-edge program, you’ll work toward your University of Maryland bachelor’s degree while studying at The Universities at Shady Grove campus – home to the new, state-of-the-art Biomedical Sciences and Engineering Education Facility. Here, you’ll not only work at the nexus of biology, engineering, data science, and computer programming, but you’ll network with STEM students and researchers from dozens of University System of Maryland programs.
The best – and easiest – way to initiate the Biocomputational Engineering degree application process is to book a meeting with our program coordinator, Emily Bailey, who can walk you through your personalized pathway – especially if you’re interested in enrolling in the fall of 2021 or spring of 2022.