University of Hartford

What you'll study

This programme blends core computing and engineering foundations with statistics, applied mathematics and domain‑focused data science. Study begins with programming and discrete mathematics and progresses through calculus, linear algebra and probability to form a rigorous quantitative base.

• Programming and software engineering: introductory and advanced courses in Python, data structures, algorithms, version control and software development practices.
• Mathematics and statistics: calculus, linear algebra, probability theory, statistical inference and experimental design for analysing and modelling data.
• Data technologies: databases and SQL, data wrangling, data cleaning, ETL processes, and relational/non‑relational data stores.
• Machine learning and AI: supervised and unsupervised learning, model evaluation, neural networks and applied machine learning techniques.
• Systems and engineering: high performance and parallel computing concepts, cloud computing fundamentals, and data engineering pipelines.
• Visualisation and communication: data visualisation, storytelling with data and communicating technical results to non‑technical stakeholders.
• Ethics and policy: privacy, fairness, responsible data use and the societal impact of data systems.
• Capstone project / practicum: a year‑long or semester capstone where students complete a team project with real data, often in partnership with industry, healthcare, finance or university research labs.

Students can typically choose electives or a minor to specialise—examples include business analytics, computational biology, robotics, cybersecurity or applied economics. Laboratory sessions, team projects and internship opportunities are integrated into the curriculum to build practical skills.

Entry requirements

Applicants should demonstrate strong quantitative ability and programming preparation. Typical expectations include:

• Secondary school completion with strong performance in Mathematics; further science or computing subjects are advantageous.
• For international students, an equivalent secondary qualification (for example IB Diploma with higher‑level mathematics) is appropriate; English proficiency testing may be required if English is not the first language.
• For domestic applicants, a high‑school transcript showing strength in maths and science; standardised tests (SAT/ACT) may be optional or considered where submitted.
• Transfer applicants will be assessed on college coursework in calculus, linear algebra, programming or statistics; relevant AP/IB credits can be evaluated for placement.
• No formal portfolio is required, but demonstrable programming experience (self‑study, coursework, coding bootcamps or projects) strengthens an application.

Admission is holistic: motivation, relevant coursework and the ability to succeed in a demanding STEM programme are the primary considerations.

Career prospects

Graduates are prepared for roles that require building, evaluating and deploying data‑driven solutions across sectors. Common career paths include:

• Data scientist or machine learning engineer—developing predictive models and analytics products.
• Data engineer—designing and maintaining data pipelines and infrastructure.
• Business intelligence and analytics specialist—translating data into decision‑making insights for organisations.
• Software developer with a data focus—implementing scalable data systems and services.
• Research assistant or analyst in academia, government labs or industry R&D.
• Further study—graduates often progress to master’s programmes in data science, computer science, statistics, engineering or specialised applied domains such as bioinformatics or financial engineering.

Internships, capstone projects and regional employer connections help students build practical experience and professional networks that support employment after graduation.

Why study at University of Hartford

University of Hartford offers a hands‑on, interdisciplinary approach with relatively small class sizes that allow close faculty mentorship. The programme emphasises project‑based learning and provides access to computing and engineering laboratories, enabling students to work with real datasets and contemporary tools.

• Strong connections to local industry and the Hartford regional technology and insurance sectors for internship and project opportunities.
• Faculty with applied research and industry experience across computer science, applied mathematics and engineering disciplines.
• Opportunities for interdisciplinary collaboration with business, health sciences and engineering departments, allowing students to apply data science to diverse real‑world problems.
• Support services including career development, undergraduate research opportunities and advising to help students plan internships, co‑ops and graduate progression.

Taken together, these elements prepare graduates to move directly into technical data roles or to continue into postgraduate study.