Lewis University

What you'll study

The programme blends core computer science and engineering foundations with applied statistics and data-focused electives. In the early years you study programming (commonly Python and/or R), discrete mathematics, calculus, data structures and computer architecture to build a solid computational base. Core data science topics include probability and mathematical statistics, databases and SQL, data cleaning and preparation, machine learning, data visualisation, and applied regression.

Advanced and elective modules commonly cover big data technologies, cloud computing, data engineering and pipelines, natural language processing, deep learning, optimisation and numerical methods. Students undertake laboratory-based assignments and team projects that emphasise reproducible analysis, version control and deployment of models. The degree typically culminates in a capstone project or practicum in which students work on a real dataset or with an industry partner to deliver an end-to-end data solution.

• Typical modules: Introduction to Programming, Data Structures and Algorithms, Calculus and Linear Algebra, Probability and Statistics for Engineers, Database Systems, Machine Learning, Data Visualisation, Big Data Systems, Numerical Methods, Ethics in Data Science.
• Practical work: laboratory sessions, team-based projects, use of contemporary tools (Python, R, SQL, Jupyter, cloud platforms), and a senior capstone or internship.
• Structure: a four-year curriculum combining general education requirements with major coursework, electives and experiential learning.

Entry requirements

Applicants are expected to hold a high-school diploma or equivalent with a strong background in mathematics. Typical academic preparation includes courses in algebra, precalculus or calculus, and, where available, high-school computer science. Admissions will consider overall academic record, recommendations and a personal statement outlining interest in data science.

Specifics you may be asked for include:

• Academic qualifications: competitive high-school grades with emphasis on maths and science subjects.
• Recommended preparation: familiarity with algebra, trigonometry and introductory programming; AP or IB credits in calculus, computer science or statistics can be advantageous.
• Additional materials: transcripts, letters of recommendation, and a personal statement. Some applicants may submit samples of coding or project work if available.
• International students: proof of English proficiency (e.g. TOEFL or IELTS) if English-language qualifications are not on record.

Career prospects

Graduates leave prepared for roles that require turning raw data into decisions and products. Common entry positions include data analyst, junior data scientist, business intelligence analyst, data engineer, and machine learning developer. The technical training also supports careers in software development, systems engineering and research assistantships.

Career destinations span sectors such as finance, healthcare, manufacturing, logistics, retail, government and tech startups. Graduates often progress into specialised roles in predictive analytics, natural language processing, computer vision or scalable data infrastructure, and may continue to postgraduate study in data science, machine learning or related engineering disciplines.

Why study at Lewis University

Lewis University offers a learning environment that emphasises small class sizes, close faculty mentorship and applied, career-focused education. The university’s proximity to the Chicago metropolitan area provides access to internships and industry partners across finance, manufacturing, health care and tech, enabling students to undertake practicum placements and capstone collaborations.

Students benefit from hands-on laboratory facilities, faculty with practical and research experience in computational and data science, and dedicated career services that support resume development, interview preparation and employer engagement. The university’s values-based mission fosters ethical awareness and professional responsibility in the use of data and technology.