Saint Louis University

What you'll study

The programme combines core training in statistics, programming and data engineering with applied modules that emphasise domain-specific problem solving. Core topics typically include statistical inference, predictive modelling and machine learning, database design and SQL, data visualisation, and big data technologies. Coursework balances theory and practice through lab assignments and project work using languages and tools such as Python, R, SQL, and commonly used libraries and platforms.

Students normally complete a mix of required courses and electives. Typical modules and experiences include:

• Statistical Methods for Data Analysis — probability, regression, hypothesis testing and model diagnostics.
• Machine Learning and Predictive Analytics — supervised and unsupervised learning, model evaluation and tuning.
• Data Management and Big Data — relational databases, ETL processes, distributed data processing concepts.
• Data Visualisation and Communication — principles of visualisation, dashboard design and communicating insights to stakeholders.
• Applied Analytics Project or Capstone — a client-based or faculty-supervised project that applies analytics methods to a real dataset.
• Electives — options commonly cover domains such as healthcare analytics, marketing analytics, time series forecasting, natural language processing and ethics in data science.

Practical learning is emphasised: students work on hands-on labs, team projects and industry-linked assignments. The capstone or practicum provides an opportunity to integrate skills in a sustained applied project.

Entry requirements

Applicants are expected to hold a recognised bachelor’s degree. A degree in a quantitative or technical discipline (for example mathematics, statistics, computer science, engineering, economics) is advantageous because of the programme’s quantitative content. Applicants with degrees in other fields who can demonstrate quantitative ability through prior coursework, professional experience or preparatory study are also considered.

Typical application materials include academic transcripts, a personal statement outlining interest and goals in data analytics, a current CV or résumé, and letters of recommendation. International applicants must meet the university’s English language proficiency requirements. In some cases, admissions may require completion of prerequisite coursework in calculus, linear algebra, statistics and introductory programming before or during the programme.

Career prospects

Graduates from this programme go on to roles that require the ability to extract actionable insight from data. Common job titles include data analyst, business intelligence analyst, data scientist, analytics consultant, machine learning engineer and data engineer. Because Saint Louis University is located in a diverse regional economy, graduates find opportunities across sectors such as finance, healthcare, manufacturing, nonprofit organisations and technology firms.

Alumni typically pursue further specialisation or leadership roles where technical skills are combined with domain knowledge and project experience. The programme’s applied project component and career services help students build portfolios and prepare for data-focused recruitment processes.

Why study at Saint Louis University

Saint Louis University offers a learning environment that blends technical training with ethical reflection and interdisciplinary collaboration, reflecting its Jesuit mission. Students benefit from faculty who are active in applied research and from partnerships within the St. Louis business and healthcare communities that can support internships and project work.

The university emphasises small class sizes and personalised advising, which helps students tailor the curriculum to their career goals. Access to campus computing resources, research centres and career services supports hands-on learning and transition into employment. The programme’s focus on applied problems, ethics in data use and communication skills aims to prepare graduates to make data-driven decisions responsibly in complex organisational settings.