Lewis University

What you'll study

The programme covers foundations and advanced topics in computational and data science with an emphasis on hands-on problem solving. Core areas include statistical inference and applied probability, machine learning and predictive modelling, data mining, data engineering and database systems, and data visualisation and communication. You will also study programming for data science (commonly Python and R), practical use of big-data tools and cloud platforms, and ethical and legal issues in data use.

Instruction typically combines lectures, laboratory work and project-based assignments. Most students complete a capstone project or practicum that applies data science methods to a real-world dataset, supervised by faculty or an industry partner. Optional electives allow specialisation in areas such as natural language processing, deep learning, time-series analytics, healthcare analytics, or business intelligence.

Entry requirements

Applicants are normally expected to hold an undergraduate degree from an accredited institution. Degrees in computer science, mathematics, engineering, statistics, economics or related quantitative fields are commonly favoured, but candidates from other backgrounds with relevant coursework or professional experience are often considered.

Typical admissions materials include an application form, academic transcripts, a résumé or CV, and a personal statement outlining your objectives and experience with programming or quantitative work. Referees or letters of recommendation may be requested. Where applicants lack certain prerequisites—such as programming, calculus or introductory statistics—conditional admission with recommended preparatory courses or bridge modules may be offered.

Career prospects

Graduates move into roles across industry, government and research. Common career paths include data scientist, data analyst, machine learning engineer, data engineer, business intelligence developer and analytics consultant. The programme’s applied focus also prepares students for analytics roles in sectors such as finance, healthcare, manufacturing, retail and technology, and for further study at the doctoral level if desired.

Many students benefit from project work, internships and collaborations with local employers to build a portfolio of practical work that supports job search and career progression.

Why study at Lewis University

Lewis University emphasises an applied, student-centred approach with small class sizes and direct faculty engagement, making it suitable for hands-on learning and mentorship. The university’s programmes often support flexible scheduling to accommodate working professionals, including evening or hybrid course options. Proximity to the Chicago metropolitan area creates opportunities for industry connections, internships and guest lecturers from regional employers.

Students benefit from access to computing labs, data tools and faculty with experience in both academic research and industry practice, enabling a curriculum that balances theory and practical application.