DATA SCiEnCE
The Mathematics and Modeling emphasis prepares students to use data to predict future trends, model data, and support informed decision-making. Students develop skills in statistics, programming, data visualization, and predictive modeling.
Students in the Mathematics and Modeling emphasis develop a strong foundation in data science, programming, statistics, and mathematics. They learn to construct and organize data using statistical and computational techniques to address practical problems.
Students learn to develop, evaluate, and interpret mathematical and predictive models. Through coursework in calculus, differential equations, linear algebra, probability, and applied mathematical modeling, they develop the quantitative tools needed to represent complex problems and explore potential solutions.
Students also develop the ability to communicate quantitative information clearly. They learn to interpret data and analytical results and communicate their findings through appropriate written, oral, and visual formats.
The emphasis prepares students to translate complex quantitative analysis into meaningful conclusions. By combining mathematical reasoning with data science techniques, students learn to use evidence and models to understand problems, evaluate possibilities, and support informed decision-making.
The Data Science major provides students with a foundation in programming, data modeling, databases, statistics, data visualization, machine learning, and technology ethics. Students also have opportunities to expand their technical preparation through coursework in web development, application development, cloud computing, computational modeling, and other areas of data science.
Students who choose the Mathematics and Modeling emphasis build on that foundation with advanced mathematics courses in Calculus II, Differential Equations, Linear Algebra, Probability, and Applied Mathematical Modeling. Together, these courses provide students with the mathematical framework needed to analyze data, construct models, and investigate complex systems.
Data science and mathematical modeling are used across industries to understand complex systems, identify patterns, make predictions and support evidence-based decisions. Graduates can apply their skills in fields such as technology, finance, research, healthcare, engineering, government and other industries that rely on quantitative analysis.
The emphasis also provides strong preparation for graduate study in data science, mathematics, statistics, applied mathematics, computational science and related fields. Students can use their combination of mathematical and data science skills to pursue analytical and technical roles across a variety of industries.
Potential career paths include:
Students can gain professional experience through the Data Science Internship, applying statistical, programming and analytical skills to real-world problems. Internship experiences allow students to work with data while developing professional communication, problem-solving and technical skills.
Students can also use coursework in applied mathematical modeling and computational methods to develop projects that demonstrate their ability to translate complex problems into quantitative models and communicate their findings.
Students can build a portfolio of quantitative projects that demonstrates their ability to work with data, develop models, analyze results and communicate conclusions. Projects involving data visualization, predictive modeling, statistical analysis or mathematical modeling can help showcase both technical and analytical skills.
The Mathematics and Modeling emphasis can be especially valuable for students interested in research, technology, finance, engineering, mathematics, statistics or other fields where quantitative reasoning and computational analysis are essential.
