University of La Verne

What you'll study

Coursework focuses on the end-to-end analytics pipeline. Topics typically include statistical inference and applied regression, data wrangling and databases, data visualization and storytelling, supervised and unsupervised machine learning, and ethical considerations around data use. Students practice with programming languages and libraries commonly used in the field, and complete project work that applies methods to real datasets.

Entry requirements

Applicants are expected to hold a bachelor’s degree from an accredited institution. Admissions commonly look for evidence of quantitative preparation (for example, coursework in calculus, linear algebra, statistics or programming) or relevant professional experience. Some programmes may require test scores, a statement of purpose, résumé and letters of recommendation; applicants without a strong quantitative background are often advised to complete preparatory courses.

Career prospects

Graduates typically move into roles such as data analyst, business intelligence analyst, junior data scientist or analytics consultant across sectors like finance, healthcare, marketing, public sector and technology. The programme develops skills useful for tasks such as building predictive models, creating dashboards and translating analytic results into business or policy decisions. With experience, alumni can progress to senior data scientist, analytics manager or specialized roles in machine learning engineering and applied research.

Scholarships & funding angle

Many universities offer merit-based scholarships, graduate assistantships, or tuition discounts for returning alumni and employer-sponsored learners. Prospective students should explore institutional graduate funding pages, department-level awards, and external scholarships for analytics and STEM fields. Additionally, employers may provide tuition reimbursement for staff pursuing relevant master’s credentials.