Fields of Data Science: A Focus on Cybersecurity

Author: Staff

Group picture of Notre Dame students at Immersion in Palo Alto, California.

Data Science Roles Span All Fields and Industries

The fields of data science aren’t contained only to data modeling or mathematical knowledge. Data science spans every person, organization, and industry, which presents a big opportunity to turn information into value in almost every situation.

Hunter Kempf ’20, machine learning engineer, threat intelligence, at Cloudflare, is seeing first-hand how different industries ultimately intersect with data science.

Fields of Data Science: Experiencing Their Impact

After earning his undergraduate degree, Kempf joined the rotational Technology Development Program at AT&T, which helps recent graduates gain experience as software engineers, data analysts, technical business managers, and network engineers.

Right away, he noticed the emphasis leaders placed on data science skills. After seeing his coworkers use data science to develop actionable insights that led to real progress and helped the company realign its business strategy, he wanted to develop his own skills on the job and learn more about the fields of data science.

In his data science role, he was able to visualize labor, budgets, and actuals for development projects and unit costs for PxQ projects while also creating financial models to predict estimated spend per month for long-term projects.

“I ended up doing a fair amount of dashboard creation and Excel reporting, and that sparked my interest in building programming and coding into my workflow,” he explains.

To expand on these capabilities, he decided to earn his online master’s in data science at the University of Notre Dame. From the very first semester, he was able to apply what he learned to his work. This eventually led to a data scientist role at AT&T before he joined Cloudflare as a machine learning engineer in threat intelligence.

During his last few years at AT&T, he found himself focusing on fraud and cyber data use cases—and that emphasis continues at Cloudflare.

Today, he works on language processing, e-mail threat detection and prevention, and identifying and classifying network traffic. “In some cases, I’m finding rules that fit a situation and applying them,” he explains. “For example, I’ll take the data and determine whether we can find an if/else rule and apply it. If it’s complicated enough, then we create a model, so I determine what type of model will work best.”

Through these experiences at AT&T and Cloudflare, he has drawn a strong connection between the fields of data science and cybersecurity.

Finding a Connection: The Relationship Between Fields of Data Science and Cybersecurity

No company or industry is exempt from data breaches or cyberattacks. Bad actors are getting smarter every day, using new and more sophisticated techniques to gain access to the information they want.

As a result, many cybersecurity tools and experts now rely on various fields of data science to sort through data patterns, determine where and how companies are most vulnerable, make accurate predictions, and identify ways to reduce risk.

After thorough data analysis, for example, data science professionals—such as data analysts, machine learning engineers, and analytics engineers—can create algorithms to predict and respond to cyberthreats. Based on what the algorithms experience, they can inform artificial intelligence and machine learning systems so cybersecurity efforts continue to improve over time.

Because of the sheer amount of data being generated, Kempf believes that cybersecurity and data science will always be interrelated. “Plenty of machine learning models end up in production because humans don’t have the capacity to manage this much data,” he explains. “It would slow down the process to have people identifying malware or phishing attempts. The rule-based techniques used in the past no longer hold up in this changing cybersecurity environment, so data science is playing a bigger role every day—and becoming an important part of the cybersecurity toolbelt.”

Domain Expertise Starts with a Solid Data Science Foundation

Developing domain expertise in a certain area—such as cybersecurity—can make you highly marketable for data science roles. The key, however, is to build foundational data science knowledge first.

One of the foundational skills Kempf has found to be most important is communication. It’s critical whether you become a data analyst, machine learning engineer, analytics engineer, or other types of data science professional.

“Effectively communicating technical content is very difficult, but Notre Dame offers an entire course on technical storytelling and visualizations. These skills can be some of the biggest distinguishers between an average data scientist and a really good data scientist. You can have the best model with the best results, but if no one understands it, then you could lose funding or stall your project.”

-Hunter Kempf, Machine Learning Engineer, Threat Intelligence, Cloudflare

Other important foundational skills include:

  • Using powerful tools to analyze data
  • Unpacking the stories big data can tell
  • Approaching data analysis ethically
  • Creating linear models
  • Understanding databases, data security, and data visualization

Once your foundational skills are in place, then you can build domain expertise in a secondary area. In Kempf’s case, that area was cybersecurity.

“This is one way you can distinguish yourself,” says Kempf. “You can combine your data science skills with whatever you find interesting or have previous experience in. It could be media, sports, cybersecurity, healthcare, retail, agricultural, or anything else. That specialized expertise elevates you as a candidate, as well as the quality of work you can produce.”

For example, his understanding of cybersecurity helps him build better data science models. Especially in a world full of cyber threats, it’s important to find a balance between speed and safety. A model needs to be secure—but it also needs to be fast. Otherwise, as Kempf explains, the business may not use it.

“When you start with the fundamentals of data science first,” he explains, “then you’re prepared to jump into niche roles where you can apply your expertise.”

Is it time to start building your own foundation and discover the fields of data science? Learn more about Notre Dame’s online master’s in data science program. You’ll be prepared to fill critical data science roles like a data analyst, machine learning engineer, or analytics engineer.