Computing and Linguistics, B.A./B.S.

4 Years On Campus Bachelors Program

Yale University

Program Overview

Yale’s Computing and Linguistics, B.A./B.S. combines computer science, statistics and data science with the study of human language, preparing students to understand language computationally, develop natural language processing systems, and analyse textual data across the humanities, social sciences and sciences. Students build from programming, mathematics, statistics and linguistic structure into advanced work in natural language processing and machine learning, with the B.S. offering a stronger research focus through an extended senior project.

Curriculum Structure

First Year: Students establish the foundations needed for the major through introductory statistics, programming and linguistics, with Yale recommending some single-variable calculus preparation as well. The academic foundation can include S&DS 1000/1230/2200, an introductory programming course and a 1000-level Linguistics course before progressing into the major requirements.

Second Year: Students move into the mathematical, computational and linguistic foundations of the discipline. Core study includes CPSC 2010 Introduction to Computer Science, CPSC 2230 for computational foundations, a proof-based discrete mathematics option such as MATH 2440 Discrete Mathematics, and MATH 2220 or MATH 2250 Linear Algebra, alongside statistics and linguistic structure.

Third Year: Students deepen their understanding of how language can be represented and processed computationally through courses such as LING 2320 Phonology 1, LING 2530 Syntax 1, and LING 2630 Semantics 1. Advanced study then moves toward LING 3350, 3540 or 3640 in linguistic structure, together with CPSC 4770 or LING 2270 in natural language processing and S&DS 2650, S&DS 3650 or CPSC 3810 in machine learning.

Fourth Year: The final year centres on research, synthesis and independent application of computational-linguistic methods. All students take CSLI 4900, the capstone seminar where they discuss research and hear from researchers in the field; B.A. students complete a senior project within the seminar, while B.S. students continue their project into the spring and must complete independent research.

Focus Areas

Computational linguistics, natural language processing, machine learning, computer science, statistics and data science, phonology, syntax, semantics, computational language analysis, textual data analysis, linguistic structure, language technology

Learning Outcomes

Students develop the ability to analyse human language computationally, apply programming and statistical methods to linguistic problems, understand phonological, syntactic and semantic structure, develop and evaluate natural language processing approaches, apply machine-learning techniques to language data, analyse textual data across disciplines, conduct independent research, and communicate computational-linguistic findings through a substantial senior project.

Professional Alignment (Accreditation)

Yale’s official 2026–27 catalog does not state a separate professional or program-specific accreditation for the Computing and Linguistics B.A./B.S. Instead, the major is an interdisciplinary Yale College program combining recognised academic areas in computer science, statistics/data science and linguistics, with the B.S. specifically providing additional research preparation for graduate study.

Reputation (Employability Rankings)

Yale’s official career data provides a strong university-level employability indicator: 95.3% of the Yale College Class of 2025 was employed or in graduate school within six months of graduation, while graduates working full-time in the United States reported a mean starting salary of $94,028. Yale also reports that 81.5% of the Class of 2025 working full-time in the U.S. reported starting salaries above $50,000, and 63.6% reported salaries above $70,000. These figures cover Yale College overall rather than Computing and Linguistics specifically.

Experiential Learning (Research, Projects, Internships etc.)

Students gain practical experience by applying computational and linguistic methods to research rather than studying the disciplines separately. The major's senior requirement provides a particularly strong hands-on component: students conduct research through CSLI 4900, either independently with a Yale faculty supervisor or collaboratively with other capstone students, while Yale's research environment also provides access to computational-linguistics and digital-humanities communities:

Computational Linguistics at Yale (CLAY Lab): An interdisciplinary research group involving faculty, postdoctoral researchers and students working on computational approaches to language learning, linguistic structure, processing and representation. The group meets weekly during the academic year to discuss current research and recent papers.
Linguistics research labs: Yale Linguistics identifies CLAY alongside the Language and Brain Lab, Phonetics Lab, Yale Grammatical Diversity Project, Child Language Development Lab and other research groups. Students in labs may receive close mentorship and work individually or in groups on projects.
Franke Family Digital Humanities Lab: Located in Sterling Memorial Library, the lab provides consultations, training and collaborative support for computational research, including text analysis, data visualization, digital exhibitions and machine learning.
Yale Library computational resources: The Digital Humanities collection supports research involving humanities computing, data visualization, network and text analysis, programming, web development, text mining and data mining, making it particularly relevant to computational-linguistics projects.
Linguistics Library resources: Yale's linguistics collection includes resources covering computational and mathematical linguistics, phonetics, phonology, syntax, semantics, psycholinguistics, neurolinguistics and language documentation, supporting both coursework and independent research.
Research projects and capstone work: B.A. students complete a senior project through CSLI 4900, either under faculty supervision or as part of a group capstone effort. B.S. students undertake a longer, independent research project extending into the spring semester.
Internships and experiential opportunities: Yale's Office of Career Strategy provides access to internships and experiential opportunities through Yale Career Link. Yale also reports that 75.1% of students in its reported cohort undertook experiential learning such as internships, research or service experiences.
Summer research and project funding: Yale provides resources including the Summer Experience Grant, Office of Fellowships advising and a Student Grants Database to help eligible students fund research, independent projects, public-service experiences and other summer opportunities.
Clinical Natural Language Processing Lab: Students interested in applied NLP can encounter Yale research focused on developing NLP methods, software and real-world applications involving clinical notes, scientific literature and social-media data.

Progression & Future Opportunities

Computing and Linguistics graduates can combine programming, statistical analysis, machine learning and language expertise for careers across language technology, artificial intelligence, data analysis, research and technology. The B.S. is particularly suited to students considering research-intensive careers or graduate study because Yale requires an additional semester of independent senior research, while the B.A. provides a more foundational route into the field.

Typical career directions: Natural Language Processing Engineer, Computational Linguist, Machine Learning Engineer, Data Scientist

Career development: Yale's Office of Career Strategy provides career advising, experiential-learning support, employer engagement, graduate and professional-school advising, and access to Yale Career Link. Students can use Yale Career Link to schedule advising appointments, research employers, attend workshops and employer events, search for internships and jobs, access on-campus recruiting, connect with alumni and practise interviews through Big Interview.
Employer connections: Yale facilitates employer engagement through career fairs, information sessions, networking events and on-campus recruiting. Yale's recruiting programme includes opportunities connecting students with employers for internships and full-time positions, while the Computer Science Career Fair connects students with employers recruiting for computing and technology roles.
Employment outcomes: For the Yale College Class of 2025, 95.3% were employed or in graduate school within six months of graduation, with a mean starting salary of $94,028 among graduates employed full-time in the United States. Yale identifies technology among the top five industries employing that graduating class.
Broader experiential preparation: Yale reports that 75.1% of students in its reported cohort participated in experiential learning such as internships, research or service experiences, while 55.3% received summer funding.
Research and industry relevance: Students can build experience in computational language research through the CLAY Lab, where students and researchers explore computational approaches to language learning and linguistic structure. Yale also has applied NLP research through its Clinical NLP Lab, connecting language technology with real-world biomedical data and software development.
Long-term academic value: Although Yale does not list a separate professional accreditation for this major, its curriculum provides a combination of computer science, statistics, machine learning and linguistics that can support further specialisation in computational linguistics, artificial intelligence, NLP, data science or related research fields.
Graduation outcomes: Yale's first-destination reporting tracks employment, graduate school, independent research and other postgraduate activities six months after graduation, giving students a formal framework for understanding postgraduate pathways.

Further Academic Progression: After completing the Computing and Linguistics B.A./B.S., students can pursue advanced study in areas such as computer science, linguistics, computational linguistics, artificial intelligence, machine learning or data science. At Yale, relevant graduate pathways include the M.S., M.Phil. and Ph.D. in Computer Science, while the Linguistics graduate programme offers a Ph.D. with research spanning computational and mathematical modelling alongside other approaches to language.