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  1. Programs
  3. Computational Linguistics

Computational Linguistics

University of Utah

CertificateAcademic

Become a contributor for free to openly demonstrate student outcomes, industry alignment & eligibility criteria.

This undergraduate certificate is designed to help students acquire the knowledge and skills necessary to pursue a career path in an area of technology known alternatively as Computational Linguistics and Natural Language Processing (NLP). This is the science behind machine translation, text-to-speech, speech recognition, and many other important applications.

Credits

19 credits

Format

In-Person

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Program Pathways

Credentials this program stacks toward

No program pathways.

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Program Details

Detailed information about this program

No detailed information available.

Requirements

What you need to earn this credential

No requirements listed.

Financial Aid

Eligible funding programs

No funding information available.

Scholarships

No scholarships listed.

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Locations

Where this program is offered

  • Utah

    Utah

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Related Programs

Programs related to this one

No related programs.

Skills & Competencies

Skills developed through this program

Auto-populated·from O*NET via SOC 15-1221.00

Skills

Judgment and Decision MakingCritical ThinkingComplex Problem SolvingReading ComprehensionActive ListeningSystems AnalysisProgrammingSystems Evaluation

Knowledge

Computers and ElectronicsMathematicsEngineering and TechnologyEnglish LanguageAdministration and Management

Abilities

Deductive ReasoningInductive ReasoningOral ComprehensionOral ExpressionWritten ComprehensionFluency of IdeasProblem SensitivityWritten ExpressionInformation OrderingCategory Flexibility

Tasks

  • Analyze problems to develop solutions involving computer hardware and software.
  • Apply theoretical expertise and innovation to create or apply new technology, such as adapting princ
  • Assign or schedule tasks to meet work priorities and goals.

Technology

Graphics or photo imaging softwareDevelopment environment softwareAnalytical or scientific softwareData base management system softwareData base user interface and query software

Tools

Articulated robotsCluster systemsComputer laser printersComputer scannersDigital camerasDigital video camerasDistributed heterogeneous computersFile serversFree-field speakersGraphics workstationsHard disk drivesHigh end computer serversHigh-performance cluster HPC computersHigh-speed networking testbedsImage capture devices

Work Values

AchievementWorking ConditionsRecognitionIndependenceSupportRelationships
Career Pathways

Occupations this program prepares you for

Auto-populated·from O*NET + BLS
Occupations matched to this program, with median wage, top wage, growth, and openings
SOCOccupationMethodWageGrowthOpenings
Match confidence: medium15-1221.00Computer and Information Research Scientiststitle_inference$140,910 median$232,120 top+19.85%790
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

Mastery: developing (Level 2)(based on Certificate)

  • Computer hardware and software design specifications — develop with moderate independence by applying theoretical principles to targeted research problems in a corporate or government lab.
  • Mathematical and computational models — construct and validate routinely to represent engineering or scientific problems for computer-based solution in familiar research domains.
  • New technology applications — adapt existing principles to novel uses by conducting structured feasibility analyses on moderately complex research initiatives.
  • Project plans and proposals — evaluate for technical and resource feasibility using established assessment criteria within a defined research program.
  • Cross-functional meetings with managers and vendors — facilitate to resolve technical coordination issues and align deliverables on active R&D projects.
  • Systems analysis techniques — apply to identify performance gaps and improvement opportunities in computing systems operating within a known research environment.
  • Analytical and scientific software platforms — configure and deploy to process complex data sets and generate reproducible experimental results with limited oversight.
  • Research task scheduling — manage across multiple concurrent assignments by setting priorities and adjusting timelines to meet project goals in a team setting.
  • Technical reports and peer-reviewed manuscripts — draft and revise to communicate research methods and findings to scientific and engineering audiences.
  • Active learning strategies — apply by integrating emerging literature into ongoing research activities to keep methods current within a specialization area.

Some details on this page are auto-populated from public workforce data sources: O*NET (opens in new tab), BLS (opens in new tab), College Scorecard (opens in new tab), DOL Training Provider Results (opens in new tab), NSX (opens in new tab). Provided in partnership with LER.me Career Intelligence.

Student Outcomes

Performance metrics for this program

Auto-populated·from Scorecard + DOL
Completion Rate
80%
Placement Rate
64%