B.A. in Data Science and Artificial Intelligence

NA On Campus Bachelors Program

University of Miami

Program Overview

The University of Miami’s Bachelor of Arts in Data Science and Artificial Intelligence prepares students to understand how data and intelligent technologies can be used to solve real-world problems. Combining computer science, mathematics, programming, and ethical decision-making, the programme suits students who want to develop analytical and technical skills while exploring how artificial intelligence and data science can be applied across different industries.

Curriculum Structure

Year 1 – Building a Foundation

Students begin by developing programming, mathematical, and communication skills. Python Programming for Everyone introduces coding, while Data Science for the World explores the role of data in solving practical problems. Calculus I strengthens the mathematical foundation needed to understand more advanced data science and AI techniques.

Year 2 – Developing Programming and Analytical Skills

Students progress to Computer Programming I and Computer Programming II, building their ability to design and develop computer programmes. Introduction to Vectors and Matrices for Data Science introduces mathematical concepts that support data analysis and computational modelling, while general education courses, language study, and a minor help broaden their academic perspective.

Year 3 – Applying Data Science and Artificial Intelligence

Students explore the core principles and applications of both fields through Principles and Practices of Data Science and Principles and Practice of Artificial Intelligence. They also study ethical and social issues in computing and can select electives that develop specialised knowledge in areas such as machine learning, database systems, statistical programming, or data visualisation.

Year 4 – Specialisation and Career Preparation

In their final year, students complete programme electives alongside their minor, writing requirements, and other degree requirements. Depending on their interests and selected courses, they can deepen their knowledge through subjects such as Computer Science Project Implementation, Computer Science Internship, Computer Vision with Deep Learning, or Machine Learning with Applications, subject to course availability and programme approval.

The B.A. requires at least 120 credit hours, including a non-STEM minor or second major, giving students opportunities to combine technical knowledge with another academic discipline.

Focus Areas

Data science fundamentals, artificial intelligence, Python programming, computer programming, applied mathematics, statistical learning, machine learning, database systems, data visualisation, computer vision, ethical AI, interdisciplinary applications of data science.

Learning Outcomes

Students develop the ability to write efficient computer programmes, analyse large datasets using data analysis languages and libraries, apply AI techniques, connect mathematical concepts with computational algorithms, use specialised tools for statistical analysis and data visualisation, apply technical skills to problems beyond computing, and communicate about computer science topics while considering ethical responsibilities.

Professional Alignment (Accreditation)

The curriculum provides academic preparation in data science, artificial intelligence, programming, mathematics, and responsible computing. The university’s published programme information does not identify a specific programme-level professional accreditation for this B.A.; therefore, a dedicated accreditation should not be claimed.

Reputation (Employability Rankings)

The University of Miami provides an interdisciplinary degree that connects computing and AI with broader academic study. No specific programme-level employability ranking or verified graduate employment rate is identified in the official programme information, so a ranking or employment statistic should not be attributed to this degree without further official evidence.

Experiential Learning (Research, Projects, Internships etc.)

The University of Miami’s Data Science and Artificial Intelligence programme allows students to connect classroom learning with practical applications through specialised electives, approved projects, and internship opportunities. Students can explore how programming, statistical analysis, machine learning, and data visualisation help address challenges in scientific, social, and other professional fields. The university’s Frost Institute for Data Science and Computing also supports interdisciplinary research through advanced computing infrastructure and visualisation facilities.

Students can build practical experience through the following opportunities:

Programming and Data Analysis: Courses such as Python Programming for Everyone, Statistical Programming: R, Python, and SQL for Social, Behavioral and Educational Data, and Database Systems develop relevant programming and data-handling skills.
Machine Learning and AI: Electives including Machine Learning with Applications, Computer Vision with Deep Learning, and Advanced Artificial Intelligence allow students to explore specialised computational techniques.
Project Planning and Implementation: Computer Science Project Planning and Computer Science Project Implementation provide opportunities to develop a programme-related project, subject to the university’s approval requirements.
Internship Opportunities: Computer Science Internship is an application elective that can provide professional experience relevant to data science and AI, subject to programme approval and availability.
Research Computing Facilities: The Frost Institute for Data Science and Computing has a Visualization Lab with 2D and 3D display walls, computing workstations, and research computing infrastructure that supports data-intensive academic work.
Applied Interdisciplinary Learning: Electives such as Biomedical Data Science, Problem Solving for Bioinformatics, Computational Neuroscience, and Geographic Information Systems I let students investigate how data science can be applied in different fields.
Career Development Facilities: The Patricia and Harold Toppel Career Center offers career preparation resources, coaching, workshops, networking events, and interview practice to help students prepare for internships and employment.

Official Facilities and Resources

Frost Institute for Data Science and Computing facilities and equipment
Official programme curriculum and electives
Toppel Career Center

Progression & Future Opportunities

Graduates of the B.A. in Data Science and Artificial Intelligence can pursue careers that involve analysing information, developing computational solutions, and applying AI techniques to real-world challenges. The programme’s combination of technical coursework and interdisciplinary study can be valuable for students interested in technology, business, scientific research, and data-driven decision-making. Depending on their chosen electives, practical experience, and additional qualifications, graduates may explore roles such as data analyst, junior data scientist, AI analyst, and business intelligence analyst.

Students can strengthen their transition from university to employment through the following opportunities:

Career Services: The Patricia and Harold Toppel Career Center provides career coaching, workshops, networking events, practice interviews, employer presentations, and access to job and internship resources through HireACane and Handshake.
Internship and Project Experience: The programme includes Computer Science Internship, Computer Science Project Planning, and Computer Science Project Implementation as potential application electives. These opportunities can help students develop relevant experience and demonstrate their skills to prospective employers.
Industry and Research Connections: The Frost Institute for Data Science and Computing develops research and innovation partnerships with businesses, start-ups, and other external organisations. These collaborations create a wider institutional environment for interdisciplinary research and industry engagement; however, a guaranteed placement or a specific partnership for this B.A. is not stated in the programme information.
Employment Statistics and Salary Figures: The university’s Toppel Career Center provides a graduate outcomes dashboard where students can explore post-graduation destinations by school, college, or major. No verified employment rate or salary figure specifically for this B.A. is established by the programme page, so a programme-specific statistic should not be assumed.
Professional Recognition: The programme provides an academic foundation in data science, AI, programming, and ethical computing. The official listing does not identify a dedicated professional accreditation for this particular degree.
Graduation Outcomes: Depending on their career goals, graduates can seek entry-level employment, pursue research-related opportunities, or continue into postgraduate study. Advanced technical roles may require additional experience, a portfolio of projects, or further qualifications.

Further Academic Progression: After completing the B.A., graduates can consider postgraduate study in data science, artificial intelligence, computer science, statistics, business analytics, or a related interdisciplinary field. Students should compare each graduate programme’s prerequisites, as some technically intensive degrees may require additional mathematics, statistics, or computer science coursework. Research-oriented students may also explore master’s programmes before progressing to doctoral study.