Data Science
- Department Interdisciplinary
- Academic Division The College
- Offerings Minor
We live in a world increasingly driven by data. Data science is a rapidly expanding, multidisciplinary field that draws on statistics, computer science and math, with applications in a wide variety of academic disciplines and industries. We extract meaning from data to learn more about the world and society.
Data Science
The data science minor allows students to complement discipline-specific skills and knowledge with a deeper understanding of how to analyze and extract meaning from data to learn more about the world, society and their discipline. We prepare students to be excellent data analysts in their major disciplines and to be prepared to effectively work with data after graduation. Students:
- Collect and analyze data in a reproducible and ethically responsible manner
- Obtain data through searching, scraping, mining or experimental methods
- Parse, transform and generate wide-ranging data sets for analysis
- Statistically analyze data to summarize, draw inferences and make predictions
- Identify patterns and relationships in datasets using visualization and algorithms
- Communicate data methods and conclusions to diverse audiences
Students trained in data science can apply their skills in a wide variety of ways including serving as central operations manager at Uber (Holley Beasley ’15), working as executive vice president at Microstrategy (Rich Cober ’96), and serving as head of industry at Google (Ames McArdle ’02).
DataCon
W&L hosts DataCon, where people from all corners of industry come to discuss the impacts of data science on industry and the world. Students from all over the university, from biology to sociology to business, participate in the Data Science Program to learn from each other and discover how to ethically learn from data.
What’s Next for Marshall Wilt ’26
Wilt is working in wealth management at J.P. Morgan Private Bank in Atlanta.
W&L’s Sarp Sahin ’26 Earns Fulbright Cardiff University Award
Sahin will continue his research of Parkinson’s disease at Cardiff University in Wales before attending medical school.
Three W&L Students Awarded Boren Scholarships for Global Language Study
With the scholarship, the graduating seniors will conduct intensive language studies in Indonesia and Japan.
W&L’s Sarp Sahin ’26 named ODK National Leader of the Year in Academics and Research
Sahin’s award will support his graduate studies at Cardiff University as a Fulbright scholar.
W&L’s Sandrine Uwantege ’27 Receives Davis Projects for Peace Grant
The grant will support Uwantege’s work to empower and expand opportunities for first-time teenage mothers in her home country of Rwanda.
Ammar Alhajmee ’26 to Receive the Global Learning Leadership Award
The accounting and German double major from Iraq will be presented with the award at the Center for International Education awards ceremony on May 26.
W&L’s Patrick Solcher Awarded Fulbright to Spain
For Solcher, teaching English in Spain is an opportunity to meaningfully immerse himself in a new culture and continue building his language skills.
Meet Toluwalope Bakare ’28
Bakare and her friends started a club on campus to celebrate African culture through dance.
Career Connections
The W&L network is perhaps most illustrative in the early-career assistance and opportunities our alumni provide to current students.
Jon Eastwood to Deliver Lecture in Honor of His Appointment to the William P. Ames Jr. Professorship in Sociology
Eastwood’s talk, titled “Reflections on the Sociology of Cynicism and Distrust,” will be held Feb. 19 in Northen Auditorium.
Meet Reagan Reiferson ’26
Reiferson has found her “homes” on campus in her sorority and the Outing Club.
Sample Courses
At W&L, we believe education and experience go hand-in-hand. You’ll be encouraged to dive in, explore and discover connections that will broaden your perspective.
- Data Science: Visualizing and Exploring Big Data
- Exploring Social Networks
- Data Science: Mind Analytics
- Introduction to Data Science for Business
- Modeling & Simulation
BIOL 1185
Data Science: Visualizing and Exploring Big Data
We live in the era of big data. Major discoveries in science and medicine are being made by exploring large datasets in novel ways using computational tools. The challenge in the biomedical sciences is the same as in Silicon Valley: knowing what computational tools are right for a project and where to get started when exploring large data sets. In this course, students learn to use R, a popular open-source programming language and data analysis environment, to interactively explore data. Case studies are drawn from across the sciences and medicine. Topics include data visualization, machine learning, image analysis, geospatial analysis, and statistical inference on large data sets. We also emphasize best practices in coding, data handling, and adherence to the principles of reproducible research.
SOAN 2011
Exploring Social Networks
This course will be a hybrid seminar/research lab that covers some of the most important findings and methods in the study of social networks (SNA), with a focus on analyzing sociocentric data (i.e., data about a whole network, as opposed to data about people’s personal networks, about which I teach a different course), with an emphasis on the application of network methods to the study of social inequalities. In the lab portion of the class, we will learn how to do network analysis in R, covering topics like (a) basic network descriptive statistics; (b) visualization of networks; and (c) community detection and the identification of subgroups and roles in network data, along with other tools and ideas.
CBSC 3090
Data Science: Mind Analytics
Psychological tests promise to match you with your soul mate, reveal the hidden depths of your personality and attitudes, and predict your success in college. How would you determine if these promises are being kept? Students build data-science skills while teaming on how to assess a test’s reliability and validity, including tests of abilities, personality, attitudes, and more. No programming experience is required while we use R, a popular open-source programming language, to learn data management, data visualization, model-comparison metrics, and statistical inference in a reproducible and ethically responsible manner.
BUS 3010
Introduction to Data Science for Business
This course covers organizational concerns related to data science such as artificial intelligence, machine learning, predictive algorithms, Big Data, cloud computing, security and privacy, and the digitization of products and processes. Through readings, students develop a strong conceptual understanding of concepts prior to developing technical proficiency in some of them. Students learn SQL and the Exploratory UI (user interface) for R to quickly access capabilities including data wrangling and machine learning without programming. Assignments focus on how organizations can improve decision making and create new business opportunities using Data Science.
CSCI 2550
Modeling & Simulation
Standard practices and applications of modeling and simulation. We explore ways to model complex systems that incorporate disciplines of biology, chemistry, and physics. Students learn critical-thinking skills when reading, comprehending, and analyzing real-world systems that they then create models for. Readings are supplemented by projects which reflect scenarios where modeling and simulation would be useful. Students are evaluated on a series of coding projects, class discussion, weekly quizzes, and exams measuring the ability to identify opportunities for application and to simulate models and their environments. A final project focuses on an open-modeling opportunity in biology, chemistry, or physics.
Meet the Faculty
At W&L, students enjoy small classes and close relationships with professors who educate and nurture.
Gregg Whitworth
Associate Professor of Biology, Data Science Program Head
Whitworth’s courses include Data Science: Visualizing and Exploring Big Data and The Molecular Mechanics of Life.
Jonathan Eastwood
Department Head, Sociology and Anthropology; Professor of Sociology
Professor Eastwood is a social theorist who also has a strong interest in quantitative methods. He teaches seminars on classical and contemporary theory as well as a series of courses that train students how to use quantitative and computational tools to answer sociological questions.
Bright Frimpong
Assistant Professor of Business Administration
Lingshu Hu
Assistant Professor of Business Administration
- P: 540-458-8383
- E: lhu@wlu.edu
With a PhD in journalism focusing on computational methods and a graduate certificate in AI and Machine Learning, Professor Hu’s primary teaching interests include making data analytics accessible to students in the social sciences and helping students master storytelling skills with data analytics and visualization.
Dan Johnson
Professor of Cognitive and Behavioral Science
Johnson’s courses include Psychology Mythbusters and Introduction to Data Science: Mind Analytics. His lab uses computational models and empirical data to investigate the mechanisms underlying creativity processes like the generation of novel ideas.
Keri M. Larson
Associate Professor of Business Administration
- P: 540-458-8601
- E: larsonk@wlu.edu
Larson teaches courses that help students learn to understand and use data in areas of management. She has researched the analytics of unstructured textual data to support organizational decision making.
David Marsh
Professor of Biology
- P: 540-458-8176
- E: marshd@wlu.edu
Marsh teaches Intro to Behavioral Ecology, Microbiome, Field Herpetology, Animal Behavior and Statistics for Biology and Medicine. His research includes effects of climate change on endemic mountaintop salamanders, effects of roads and land use on frog and toad populations across the Eastern and central U.S., and population dynamics of terrestrial salamanders.
Sybil Prince Nelson
Associate Professor of Mathematics
Prince Nelson ’01 teaches courses in calculus, probability and statistics. Her research is focused on creating tree-based models for classifying and predicting outcomes from complex data.
Sarah Petersen
Assistant Professor of Mathematics and Data Science
Petersen teaches a variety of mathematics and data science courses. Her research focuses on computational aspects of algebraic topology. She particularly enjoys teaching interdisciplinary courses and mentoring undergraduate projects involving mathematics and art.
Sara Sprenkle
Associate Professor of Computer Science and Department Head
Sprenkle teaches courses in programming, software development, and upper-level electives in software engineering. Her research focuses on automatically testing web applications to make sure they are behaving properly.
Natalia Toporikova
Associate Professor of Biology
Professor Toporikova’s courses include Biological Clocks and Rhythms, The Architecture of Living Systems, Dynamics of Biological Systems and Pregnancy: A Kiss in Time? In her research, she applies methods of computational modeling to study a wide range of biological systems. Some recent projects include neural control of breathing, pregnancy initiation in rats, and daily circadian cycle.