A student sits at a laboratory bench, working intently on a robotic project.

Why Dabble in Data Science? Turning Information into Impact

Why dabble in data science? Dr. Catherine Quinlan, associate professor of Science Education at Howard University and collaborator on LabXchange's Data Science–Driven Science Education Project, highlights the many benefits that learning data science can bring to a student's educational and professional careers.


When I first began my research project on black cultural representation in the science curricula, there were big questions I wanted answered. Most of all, I wanted to offer students the flexibility of asking their own questions and getting scientific answers to life’s questions, as it related to learning about black heritage and lived experiences.

So, I met with representatives from Esri (Environmental Systems Research Institute, Inc.), a global organization that leads the way in geographic information systems (GIS) software. The long and short of it was that, in order for me facilitate student learning using this approach to big ideas, I needed data science to help me turn different kinds of data into visualizations that students could explore.

After beginning one of Esri’s free introductory course modules on data science and spending time in front of spreadsheets that showed me how to turn raw data into visualizations, I decided that I should hire someone professionally trained in data science and data visualization to work alongside me. What an amazing feat it would be to explore and explain complex questions using enormous amounts of data and turn them into visualizations that people could comprehend! I am fortunate that, more recently, two STEM students volunteered to train using Esri's data science modules out of sheer interest and because they believed it would enhance their future job prospects.

Data Science Is for Everyone

Whether your goal is to major in STEM to be a doctor or a scientist, to work in public health, to focus on a social science, or to explore art, music, mathematics, computer science—and so on—there are endless reasons why you’ll need to understand some data science.

As science and society continue to accumulate enormous amounts of data, we have to increasingly ask “so what,” “how,” and “why” questions, especially when we are trying to solve bigger global problems. For example: how do we continue to live lavish lives without burying ourselves and our Earth in enormous amounts of non-biodegradable trash? With great advancements come great responsibilities (partial quote credit to Spider-Man), and with increasing amounts of knowledge and data, our next step within society—across all fields—is to figure out how to use it all to benefit society.

With increasing advancements in technology, it will become more unforgiving for a doctor to make an error when it could have been avoided with data science. One of the things I learned as a clinical research assistant in cardiovascular pathophysiology was that a primary physician may prescribe a drug to treat one thing, while the cardiologist might find that the drug interferes with the patient’s heart function. With increasing access and ways of sharing information and treating patients, there is increasing accountability and, therefore, a need to be able to interpret available and accessible data for treating patients.

DNA, Data Science, and Disease

As a biology student, you might have learned about the controversial fight for credit over the discovery of the DNA model, which Rosalind Franklin got right, and Francis and Crick got credit for (hence the controversy). In some ways, this was a data science issue. Based on the prior data and understandings about DNA at the time, Franklin was able to create a model of the DNA that would continue to influence what we know and learn about the DNA's structure and function.

Today, data science is central to the advancement of work on the human genome and its application in society. Cataloging all the genes formed by the over 3 billion combinations of nucleotide base pairs was the goal of the Human Genome Project, which was completed in 2003. With these advancements in technology and data science, some of us from African descent can now definitively trace our origins and our ancestors, allowing us to gain insights into where we came from. Do you see my fascination with data science as I combine science with social science in my research? As our interests in diseases grow, especially because of the Covid-19 pandemic, many questions remain unanswered about the spread and still unseen repercussions.

In my NSF-funded project, one of my interests in using data science was to understand where there were food deserts in the United States, and more specifically to combine this with various other factors that connect the social and economic conditions, factors, and indicators with various historical and political indicators. Complex! I also wanted to connect historical data by location that would give students access and opportunities to explore questions of interest to them. This is somewhat like the forensic anthropology approach that I used when I taught high school anatomy and physiology.

Turning Information into Impact

In data science and data visualization, there is the art, the science, the math, and the maps. This combination engages so many different parts of our brains and invites collaborations even between teachers and students, training all to be critical thinkers. Data science can keep you open to possibilities. You might not dabble in data science if your mindset is fixed, or if you have affixed answers to questions, or are not interested in the evidence. The beauty of data science visualization is that, as you use your creativity to come up with a visualization, you experience your biggest "aha!" moments. There are multiple ways to view the same thing! Now you need to tackle what questions are important to you and what messages you want to convey.

This is where it can be a blessing and a curse (in the words of Adrian Monk from the Monk TV series). We have seen how data can be used for both good and evil, or how we can draw different inferences and conclusions from the same data. So, let’s equip everyone with these skills so that we can all be empowered to make choices rather than be manipulated by data visualizations for political or economic gain.

As a matter of fact, in the future, I’d like to delve into data science further to reveal those areas where societal perspectives have been manipulated by data and where many are still unaware. I find this to be particularly true when looking at economic data, which is often provided without context. Alternatively, context might be provided without the data to back it up.

In some ways, fields such as economics have had a head start on honing in on the influence of data, such as the use of data and data visualizations to talk about economic trends, the impact of socioeconomic status on inventions, or to show how consumerism and trends affect our perspectives about our own successes today. What would it look like if data were not only able to answer the “what” questions, but also the “why” and the “how”? Perhaps data science might even help us to ask new and different questions!

For the Classroom: An Exercise in Data Science

Here's an exercise that can help students think more deeply about data science. Work in pairs or groups of twos. Consider the following questions and discuss with each other.

Part One: 

  1. What large sets of data might you want access to? Why? Write a list of data that you would want to explore.
  2. What questions might you be interested in exploring, and in what fields?
  3. What would you want to show and prove? Compare and share your responses with each other.

Part Two:

Give each other feedback and advice on what kinds of data you think would help the other person. Share what assumptions you think the other person might be making. Share how you view the other person’s response differently. Discuss where there might be overlap, if any.

Written by
Catherine Quinlan
Associate Professor of Science Education, Howard University

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