B.S. in Data Science and Artificial Intelligence

4 Years On Campus Bachelors Program

University of Miami

Program Overview

The Bachelor of Science in Data Science and Artificial Intelligence at the University of Miami prepares students to turn complex data into meaningful insights and develop intelligent systems that can solve real-world problems. Combining computer science, mathematics, statistics, machine learning, and artificial intelligence, the program suits students who enjoy analytical thinking, programming, and innovation and want to build careers in data science, AI development, technology, or research.

Curriculum Structure

Year 1 – Programming and Mathematical Foundations

Students begin by developing essential programming and analytical skills through Python Programming for Everyone, Data Science for the World, and Computer Programming I. Alongside these subjects, Calculus I and Calculus II build the mathematical foundation needed to understand algorithms, analyse data, and prepare for more advanced computing and AI coursework.

Year 2 – Computing, Statistics, and Artificial Intelligence

In the second year, students strengthen their programming abilities through Computer Programming II and explore the mathematical principles behind computing through Discrete Mathematics I and Introduction to Probability and Statistics. They also study Introduction to Artificial Intelligence, gaining an initial understanding of intelligent systems while developing the analytical skills required for machine learning and advanced data applications.

Year 3 – Data Structures and Machine Learning

Students progress to more advanced technical subjects, including Data Structures and Algorithm Analysis, Introduction to Python for Scientists, and Introduction to Linear Algebra. They also study Machine Learning with Applications, connecting mathematical and computational concepts with practical methods for identifying patterns, analysing datasets, and building predictive models. Application and techniques electives allow students to explore areas such as databases, statistical learning, and computer vision.

Year 4 – Advanced AI and Applied Specialisation

In their final year, students take Advanced Artificial Intelligence and complete further application and techniques electives tailored to their interests. Options such as Computer Science Project Planning, Computer Science Project Implementation, and Computer Science Internship can provide opportunities to develop or apply their skills through approved data science and AI-related work. Students also complete the programme's remaining general education and degree requirements.

Focus Areas

Data science, artificial intelligence, Python programming, computer science, machine learning, probability and statistics, mathematical modelling, data structures and algorithms, statistical learning, database systems, computer vision, data visualisation, AI ethics, applied data analysis.

Learning Outcomes

Graduates are expected to be able to develop efficient computer programs using languages including Python and Java; analyse large datasets using appropriate data analysis tools and libraries; apply fundamental and advanced AI techniques; use mathematical and statistical concepts to understand algorithms; work with tools for data storage, statistical analysis, visualisation, and machine learning; apply data science and AI techniques to problems beyond computer science; and evaluate the ethical implications of AI applications.

Professional Alignment (Accreditation)

The curriculum combines programming, mathematics, statistics, AI, and ethical computing to support preparation for professional and research careers. The official programme information does not identify a separate, programme-specific professional accreditation, so one should not be assumed.

Reputation (Employability Rankings)

The University of Miami provides career-development resources through its Patricia and Harold Toppel Career Center, including career coaching, internship-search support, employer networking, workshops, and recruiting opportunities. These services can help students connect their technical education with employment opportunities; a specific employability ranking for this degree was not identified in the official sources reviewed.

Experiential Learning (Research, Projects, Internships etc.)

The University of Miami's programme combines technical foundations with opportunities to apply data science and AI concepts through computing projects, specialist electives, and approved internships. Students can develop practical programming and analytical skills in the Department of Computer Science's learning facilities, while project-based coursework encourages them to plan, implement, test, and document computing solutions. The curriculum also includes application options in fields such as biomedical data science, bioinformatics, and computational neuroscience, helping students connect technical knowledge with problems in other disciplines.

Students can explore the following programme-related learning opportunities:

Computer Science Lecture and Learning Lab: Located on the third floor of the Ungar Building, this facility supports interactive teaching, laboratory activities, and workshops. The department's published lab information describes computers equipped with NVIDIA RTX 3070 GPUs, suitable for graphics and deep-learning workloads.
Discovery Lab: The department also identifies a Discovery Lab, providing another university computing resource for students to explore.
Programming and Data Analysis: Courses in Python, computer programming, machine learning, and statistical analysis help students develop practical computational and analytical capabilities.
Project Planning and Implementation: Computer Science Project Planning and Computer Science Project Implementation provide routes for developing and implementing approved projects related to data science and AI.
Internship Opportunities: Computer Science Internship allows students to gain experience in a commercial computing environment. The course description specifies that students normally complete 50 internship hours per credit earned, subject to the course requirements.
Specialist Application Areas: Electives such as Biomedical Data Science, Problem Solving for Bioinformatics, Computational Neuroscience, and Computer Vision with Deep Learning enable students to explore applications of computing across different fields.

Progression & Future Opportunities

The B.S. in Data Science and Artificial Intelligence can prepare graduates for opportunities in technology, analytics, research, and industries that rely on data-driven decision-making. By combining programming, mathematics, machine learning, and AI, students build a foundation for technical roles such as data analyst, junior data scientist, machine learning engineer, or AI software developer, depending on their practical experience and the requirements of each employer.

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

Career Coaching and Professional Development: The Patricia and Harold Toppel Career Center provides individual and small-group coaching, résumé reviews, interview preparation, job-search advice, career workshops, and guidance on securing internships.
Employer Networking: Students can access networking events, career fairs, company presentations, on-campus recruiting, and career mentoring opportunities to build professional connections.
Online Job and Internship Resources: The university's Handshake platform connects students and alumni with job and employer information. The Toppel Career Center also offers career resources and industry-focused Career Circles.
Employment Outcomes: The university provides an online graduation-outcomes dashboard where students can explore graduate destinations by school, college, or major. No specific employment rate or average salary for this individual degree has been established in the official sources reviewed, so a programme-specific figure should not be assumed.
Industry Experience: The approved Computer Science Internship course offers a route to experience in a commercial computing environment. Students should confirm internship eligibility and placement arrangements with the department.
Long-Term Professional Value: The combination of programming, statistics, AI, and ethical computing provides a foundation for continued professional development. The official programme information reviewed does not specify a separate professional accreditation for this degree.

Further Academic Progression: Graduates can consider postgraduate study in areas such as data science, artificial intelligence, computer science, machine learning, statistics, or computational science. The University of Miami's Department of Computer Science offers graduate-level education and research in areas including data mining, data science, machine learning, neural networks, and related computing fields. Students interested in research-focused careers may pursue a relevant master's degree and, where appropriate, doctoral study.