Saint Louis University

What you'll study

This programme builds a strong foundation in computational thinking, statistical reasoning and engineering principles, then moves into applied data science topics and ethical practice. The curriculum typically spans four years, with a progression from core mathematics and programming to specialised modules and a culminating capstone project.

• Year 1: introductory programming, calculus, linear algebra, introductory statistics, and foundations in computing and scientific reasoning.
• Core subjects: data structures and algorithms, probability and statistical inference, numerical methods, databases and data management, and software engineering principles.
• Applied and advanced topics: machine learning, data mining, data visualisation, high-performance and parallel computing, optimisation, time series analysis, and Bayesian methods.
• Engineering and systems: computational modelling, systems design, and courses emphasising reproducible research and production deployment of data-driven systems.
• Ethics and context: courses covering data ethics, privacy, bias and responsible use of algorithms in social contexts.
• Capstone and experiential learning: team-based capstone projects with real datasets, opportunities for undergraduate research with faculty, internships with local industry partners and optional study-abroad or exchange experiences.
• Electives and concentrations: students can often choose electives or minors in areas such as bioinformatics, finance, social sciences, public health or engineering to gain domain-specific expertise.

Entry requirements

Applicants are expected to hold a recognised secondary school diploma or equivalent. Successful candidates typically demonstrate strong performance in mathematics (including calculus where available) and coursework in science or computer science. Prior experience with programming is beneficial but not always mandatory; introductory bridge courses are often available for students new to coding.

Competitive applications commonly include a record of related coursework, good overall academic performance, and a personal statement that explains interest in data science and relevant experiences. Saint Louis University also considers transfer applicants, mature students and those with relevant work experience; these applicants may be asked to provide transcripts and references showing preparedness for a rigorous STEM programme.

Career prospects

Graduates from this field are prepared for a broad range of roles in the public and private sectors. Typical career paths include:

• Data scientist or data analyst in technology, finance, healthcare, consulting and government.
• Machine learning or artificial intelligence engineer developing predictive models and production systems.
• Business intelligence developer or analytics consultant translating data into strategic decisions.
• Data engineer or database administrator focusing on pipelines, storage and scalable architectures.
• Research assistant or continued academic study at the graduate level in data science, computer science, statistics or related disciplines.

Alumni also enter industry-specific roles where domain knowledge plus data skills are valuable, such as bioinformatics, public health analytics, and urban or environmental modelling. Career services and industry partnerships at Saint Louis University support internship placement and employer connections.

Why study at Saint Louis University

Saint Louis University offers an education grounded in its Jesuit mission with emphasis on critical thinking, ethical responsibility and service. The university provides small to medium-sized classes that allow close interaction with faculty who are active in research and applied projects. Students benefit from an interdisciplinary approach that integrates mathematics, computing and domain applications.

Located in a metropolitan region with a diverse economy, SLU gives students access to internships and collaborations with healthcare institutions, technology companies, startups and government agencies in the St. Louis area. The programme’s focus on experiential learning — through capstones, research opportunities and community-engaged projects — helps students build practical portfolios and professional networks. Additional support comes from dedicated career services, academic advising and student organisations related to computing and data science.