City University of New York

What you'll study

This Bachelor of Science in Data Science and Artificial Intelligence covers core computational and quantitative subjects alongside hands-on applied work. Early years focus on calculus, linear algebra, probability and introductory programming; later years emphasise statistical modelling, machine learning, data engineering, databases and software development practices. Coursework balances theory, practical labs and project-based learning with options for electives and a culminating capstone or internship.

• Core quantitative foundations: calculus, linear algebra, probability and statistics.
• Computer science fundamentals: data structures, algorithms, software engineering and programming in languages such as Python and SQL.
• Data methods and machine learning: exploratory data analysis, supervised and unsupervised learning, deep learning basics, model evaluation and interpretability.
• Data management and engineering: relational and NoSQL databases, data cleaning, ETL pipelines, big-data processing and cloud basics.
• Applied topics and electives: natural language processing, computer vision, time-series analysis, AI ethics and fairness, computational statistics.
• Practical experience: laboratory coursework, collaborative projects, a capstone project applying methods to real datasets and opportunities for internships with industry or public-sector partners.

Structure

The programme is structured over four years of full-time study, with a progression from general education and introductory STEM courses to specialised upper-division modules. Students typically complete general education requirements alongside major courses in the first two years, then concentrate on advanced electives and a capstone or internship in the final year. There are options to take interdisciplinary electives and to participate in research with faculty where available.

Entry requirements

Applicants are expected to have a strong high-school background in mathematics and science and basic programming experience is advantageous. Typical preparation includes courses in algebra, precalculus or calculus and exposure to computer science or coding. Admissions consider academic transcripts, personal statements and any relevant experience such as coding projects or work placements. Transfer applicants should present college-level coursework in mathematics and computing. English language proficiency evidence is required for applicants whose prior education was not in English.

Career prospects

Graduates are prepared for roles that require quantitative analysis, statistical reasoning and software skills. Common entry-level job titles include data analyst, junior data scientist, machine learning engineer, data engineer, business intelligence developer and analytics consultant. Alumni also move into specialised areas such as natural language processing, computer vision, product analytics, and roles in finance, healthcare, government and tech startups. Graduates who wish to deepen theoretical knowledge or pursue specialised research often progress to master’s or PhD programmes in data science, computer science or statistics.

Why study at City University of New York

City University of New York provides an urban, diverse learning environment with strong links to industry, public institutions and a large regional technology ecosystem. The university offers access to faculty with applied and research experience, practical lab facilities and opportunities for internships and collaborations across CUNY colleges. The programme’s practical orientation, combined with CUNY’s emphasis on accessibility and experiential learning, makes it suitable for students seeking hands-on preparation for data-intensive careers or further graduate study.