Rice University

What you'll study

The programme combines core training in mathematics, statistics and computer science with applied coursework and project work in data analytics. Students typically take foundational modules in calculus, linear algebra, probability and statistical inference, together with programming in Python and/or R, data structures and introductory machine learning. Coursework emphasises practical skills: data cleaning and integration, exploratory data analysis, statistical modelling, predictive analytics, data visualisation, and database design.

Beyond core modules, students choose electives or domain-focused courses from areas such as business analytics, bioinformatics and health data, natural language processing, geospatial analysis, finance, or public policy analytics. The curriculum often culminates in a capstone project or practicum in which students tackle a real-world data problem, working individually or in teams with faculty, industry partners, or research labs.

• Core topics: calculus, linear algebra, probability, statistical inference
• Computing: programming for data science (Python/R), data structures, SQL and databases
• Applied methods: regression and classification, time series, clustering, dimensionality reduction
• Tools & practice: data cleaning/ETL, version control, reproducible research, visualisation libraries
• Capstone/practicum: industry- or research-based projects, internships, or undergraduate research

Entry requirements

Admission to Rice is competitive and decisions are based on an applicant’s overall academic record, preparation, and fit with the university. For a data analytics pathway, applicants are expected to demonstrate strong preparation in mathematics (calculus and algebra) and to have exposure to quantitative or computational work. Practical programming experience and coursework in statistics, computer science or related subjects strengthen an application.

Typical application materials include transcripts, personal statements, and letters of recommendation. Successful applicants usually show strong grades in STEM subjects, evidence of analytical projects or extracurricular activities (such as coding or data competitions, research, or internships), and a clear interest in working with data. International applicants must meet Rice’s standard academic and English language requirements for undergraduate admission.

Career prospects

Graduates with a bachelor’s in data analytics are prepared for roles that require translating data into insight across a wide range of sectors. Common entry-level job titles include data analyst, business analyst, operations analyst, data engineer (junior), and analytics consultant. With additional domain experience or graduate study, alumni move into data science, machine learning engineering, quantitative finance, health analytics, product analytics, and research roles.

Rice’s location in Houston and its network of industry and institutional partners provide opportunities for internships and employment in energy, healthcare and biomedical institutions, finance, technology, and consulting. Graduates also pursue further study in statistics, computer science, business analytics, public policy and other related disciplines.

Why study at Rice University

Rice offers a close-knit undergraduate experience with small class sizes, strong faculty access and an emphasis on hands-on learning and research. The residential college system fosters community and interdisciplinary collaboration, while faculty across departments support data-focused projects and undergraduate research.

Studying in Houston gives students ready access to a large and diverse job market — including hospitals and research centres, energy firms and startups — which is valuable for internships and applied projects. Rice’s entrepreneurial culture and institutional resources help students translate analytic skills into real-world impact, whether in industry, academia or public service.