Saint Louis University

What you'll study

The programme emphasises core computational and statistical foundations alongside applied data science techniques. Students follow a curriculum that covers probability and statistical inference, machine learning, data mining, database systems, and data visualisation, while also gaining practical experience with programming languages and tools commonly used in the field.

• Core quantitative foundations: courses in probability, statistical modelling and inference that provide the mathematical basis for rigorous analysis.
• Machine learning and applied algorithms: supervised and unsupervised learning methods, model evaluation, feature engineering and optimisation techniques.
• Data infrastructure and engineering: relational and NoSQL databases, SQL, data wrangling, ETL pipelines and an introduction to distributed computing and cloud-based data processing.
• Programming and tools: hands-on work in languages such as Python and R, libraries for data analysis and machine learning, and exposure to version control and reproducible research practices.
• Data visualisation and communication: methods for effective visual representation of data and communicating results to technical and non-technical audiences.
• Ethics, privacy and governance: coursework addressing ethical issues in data collection and use, algorithmic fairness and responsible deployment of models.
• Capstone, practicum or thesis option: students complete a substantial applied project or research thesis that integrates methods learned across the programme, often in collaboration with industry partners, campus research labs or local organisations.

Elective modules allow specialisation in areas such as natural language processing, time-series analysis, deep learning, computational biology, or business analytics depending on student interest and faculty offerings.

Entry requirements

Applicants are expected to hold a recognised bachelor’s degree. Typical admissions prerequisites include coursework or demonstrable competence in calculus, linear algebra and introductory statistics, together with some programming experience (for example in Python, R, Java or C++). Applicants without a formal background may be required to complete bridge or preparatory modules.

• Academic transcripts: evidence of a completed undergraduate degree from an accredited institution.
• Quantitative preparation: prior coursework in mathematics and statistics, or equivalent professional experience.
• Programming experience: familiarity with at least one programming language and basic data structures.
• Supporting materials: statement of purpose outlining career goals and research interests, and letters of recommendation.
• English language proficiency: where applicable, proof of English ability through recognised tests or equivalent qualifications.

The programme may consider applicants from diverse academic backgrounds and will evaluate each application holistically; some candidates may be admitted with conditions to complete specified preparatory work.

Career prospects

Graduates from the Master of Science in Data Science move into a broad range of technical and analytical roles. The combination of statistical rigour and computational skill prepares alumni for careers in both industry and research.

• Data scientist — designing models, conducting predictive analysis and translating insights into business or policy actions.
• Machine learning or AI engineer — developing and deploying scalable machine-learning systems.
• Data engineer — building and maintaining data pipelines, warehouses and infrastructure for large-scale analytics.
• Business intelligence analyst — creating dashboards and reports to guide organisational decisions.
• Research analyst or computational scientist — applying data-driven methods in domains such as healthcare, finance, public policy, and the life sciences.
• Further study — some graduates continue to doctoral programmes or specialised certifications in related fields.

The programme’s applied project and local industry connections also support internship opportunities and practical experience that are often leveraged into early career roles.

Why study at Saint Louis University

Saint Louis University offers this programme within an interdisciplinary environment that draws on expertise across computer science, mathematics, engineering and domain-specific departments. SLU emphasises hands-on learning, ethical practice and community engagement consistent with its Jesuit educational mission.

• Interdisciplinary faculty and research: access to faculty with expertise in machine learning, statistics, computational modelling and applied research collaborations.
• Hands-on facilities: teaching and research labs, computing resources and opportunities to work with real datasets on campus projects.
• Local ecosystem and partnerships: proximity to St. Louis’s health, finance and technology sectors provides routes to internships, practicum placements and collaborative capstone projects.
• Career support: dedicated career services and networking events that help graduates connect with employers and alumni in data-driven roles.
• Values-driven education: a curriculum that integrates technical training with consideration of ethical, social and policy implications of data science practice.