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  1. Programs
  3. Advanced Computational Genomics

Advanced Computational Genomics

National Institutes of Health (NIH)

Micro-CredentialCIP: 15.2041

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

Training on advanced computational methods for analyzing large-scale genomic datasets| including algorithm development and high-performance computing.

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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

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Locations

Where this program is offered

No locations specified.

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Related Programs

Programs related to this one

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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: emerging (Level 1)(based on Micro-Credential)

  • Computer hardware and software problems — analyze root causes under faculty or senior researcher direction in a university or corporate research lab setting.
  • Mathematical models of technical problems — formulate with guidance by applying foundational coursework to structured research problems in supervised project work.
  • Existing theoretical frameworks — review and summarize to support innovation efforts on an assigned research team in an academic or R&D environment.
  • Development environment software and analytical tools — operate following established lab protocols to run experiments and record results under close supervision.
  • Technical literature and research proposals — read and synthesize to identify relevant prior work in preparation for team meetings and literature reviews.
  • Research task priorities — track and report progress against assigned milestones under the direction of a principal investigator or project lead.
  • Basic programming scripts and algorithms — write and test in a supported codebase environment to implement well-defined research procedures.
  • Database management and query software — use to retrieve and organize research data sets according to project specifications under supervision.
  • Multidisciplinary project team meetings — participate in by contributing domain-specific knowledge in areas such as human-computer interaction or robotics.
  • Oral and written research summaries — prepare and present to supervisors and lab peers to communicate early-stage experimental findings clearly.

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

Completion Rate
Not reported
Placement Rate
Not reported