Across a wide variety of industries, SAS users are leaning into the power of data to glean relevant insights and identify new opportunities. Through analytics, they are solving complex problems, discovering practical insights, and making a difference in their fields and communities.

How can others join their efforts and use data for good? SAS users reflect on their own experiences and offer three pieces of advice.

1. Pursue your passions

For Geeta Kersellius, a business and health care data analyst for the Defense Health Agency’s (DHA) Analytics and Evaluation Division, the opportunity to use data to improve the well-being of others started with recognizing her interests and passions. “I always had a desire to be involved in the medical profession and decided to pursue a pre-medical track while in college,” Kersellius says.

After earning a bachelor’s degree in biological sciences from Rutgers University, Kersellius worked as a clinical laboratory technician for an infectious disease laboratory. This fulfilling role encouraged her to further her education. Then, while earning dual master’s degrees in biomedical sciences and public health, Kersellius was first introduced to SAS software.

Working in epidemiology and disease surveillance with the US Army furthered Kersellius’ passion for data, which led to an analyst position in the US Air Force School of Aerospace Medicine’s public health research department, where she monitored respiratory disease in the military. “It gave me a chance to use SAS skills that I learned in the MPH program,” Kersellius says, “and I loved it.”

Later, as a senior data analyst, Kersellius joined the World Trade Center Health Program Data Center at the Icahn School of Medicine at Mount Sinai before transitioning to the DHA. Looking back on her career journey, Kersellius credits her mentors with helping her find her niche – and says that their encouragement to follow her passions and interests as they evolved made all the difference. Today, she encourages others to do the same.

Related: 3 ways upskilling and continuous learning drive long-term career advancement

2. Think beyond the numbers

Jessica Rudd, senior data engineer with Intuit Mailchimp, adds that it is helpful for data science students to broaden their perspectives by studying or training in a non-STEM field.

“That kind of learning is important,” she says. “There are so many people who are excellent mathematicians or programmers, but they haven't had to answer questions that deal directly with people or the results of problems.”

Rudd speaks from experience. After earning a bachelor’s degree in anthropology and political science and a master’s degree in public health, she went on to work in biostatistics for the Centers for Disease Control and Prevention. Having a background in social sciences, she says, allows her to account for certain biases and think about data analytics from a human and ethical perspective.

“I think having a social science background helps you take those hard sciences – math, statistics, computer science – and understand that the result of data means something,” Rudd explains. “You can throw a data set into an algorithm on a deep learning project and get a 99.9% accuracy, but what does that really mean for the people in that data set?

“So there’s statistical significance and accuracy, but there’s also human significance. You need some extra background that’s not geared toward actual programming to allow people to understand the human nature of data science.”

3. Identify real-world problems

Carmen Vila, an aspiring legal tech lawyer and business analyst, agrees – data has vast significance for society, and it’s up to analysts to consider how they can use data insights to uplift the communities around them.

While earning her bachelor’s degree in law and business analytics from Universidad Francisco de Vitoria (UFV) in Madrid, Vila aimed to do just that. In collaboration with UFV, SAS and the Gender Violence Division of Spain’s Ministry of the Interior, Vila completed a thesis project centered on a prominent societal issue: gender-based violence and high-victim offenders.

“The core focus of my thesis was the meticulous examination of criminal profiles and assessing recidivism probabilities among high-victim offenders who repeatedly victimize multiple individuals,” Vila says. “Through thorough analysis, I explored their recidivism rates and the nature of the violence perpetrated.

“The Ministry of Interior expressed satisfaction with my findings and the applied models, as they provided valuable insights consistent with existing information. Knowing that my work could genuinely contribute to enhancing victim protection within the Ministry was immensely gratifying during both the research process and the defense of my thesis.”

For Vila, identifying a complex, real-world problem to solve allowed her to strengthen her analytics skills – and make a meaningful impact on her community.

Start your journey

More than numbers, spreadsheets and sophisticated technology, data analytics allows individuals to tackle pressing challenges and build a brighter future – which is why more and more people are turning to this in-demand skill.

Whether it’s protecting endangered species or responding to humanitarian crises, learning SAS can help you put analytics into action.

Learn more about how you can get started.


About Author

Alexis Mallis

Associate Marketing Specialist, SAS Education Product Marketing

Alexis is an Associate Marketing Specialist on the Education Product Marketing team at SAS. She graduated from NC State University with a B.S. in Business Administration concentrating in Marketing. She is excited to learn more and grow at SAS.


  1. Pursue your passions - For me, this is Life Long Learning. Or for the geeky ones L^3! 🙂 Or Level-3 as I like to refer to it - "The 3 L's"

    Think beyond the numbers - Sometimes, it is more about the underlying thought process (Think Beyond) , than the end state result of an analysis (The Numbers)

    Identify real-world problems - These usually are easy to find in my realm. I almost can always find a real-world question/problem to go with a generic example that I see others demonstrate or apply to their data.

Leave A Reply

Back to Top