LER.me

Make All Learning Count.

Get Connected

  • What is a LER?
  • FAQs (opens in new tab)
  • Partner with Us
  • Visit EBSCOed (opens in new tab)

View our Policies

  • Accessibility (opens in new tab)
  • Standards (opens in new tab)
  • Terms of Use (opens in new tab)
  • Privacy Policy (opens in new tab)
  • Opt out (opens in new tab)

Get the app

Get it on Google PlayDownload on the App Store

© 2026 All rights reserved.

Powered by EBSCOed

Skip to main contentSkip to footer
  • Live Data
My LER
My LER
  1. Programs
  3. Advanced Genomic Data Mining

Advanced Genomic Data Mining

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 data mining techniques for large-scale genomic datasets| including machine learning and pattern recognition.

Loading Skills & Competencies
Program Pathways

Credentials this program stacks toward

No program pathways.

Loading What You'll Learn
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.

Visit Program Website
Locations

Where this program is offered

No locations specified.

Loading Student Outcomes
Related Programs

Programs related to this one

No related programs.

Skills & Competencies

Skills developed through this program

Auto-populated·from O*NET via SOC 15-1243.01

Skills

Reading ComprehensionCritical ThinkingProgrammingComplex Problem SolvingSystems AnalysisActive ListeningJudgment and Decision MakingSpeaking

Knowledge

Computers and ElectronicsMathematicsEnglish LanguageDesign

Abilities

Written ComprehensionInformation OrderingOral ComprehensionDeductive ReasoningInductive ReasoningNear VisionOral ExpressionWritten ExpressionCategory FlexibilitySpeech Recognition

Tasks

  • Develop data warehouse process models, including sourcing, loading, transformation, and extraction.
  • Verify the structure, accuracy, or quality of warehouse data.
  • Map data between source systems, data warehouses, and data marts.

Technology

Metadata management softwareDevelopment environment softwareObject or component oriented development softwareData base management system softwareData base user interface and query software

Tools

Computer serversData warehouse appliancesDesktop computersLaptop computersMainframe computersMulti-line telephone systemsPersonal computersStorage servers

Work Values

AchievementWorking ConditionsIndependenceRecognitionSupportRelationships
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: medium15-1243.01Data Warehousing Specialiststitle_inference———
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

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

  • Data warehouse process models — identify and document sourcing, loading, transformation, and extraction steps under direct supervision in a structured enterprise environment.
  • Warehouse data quality — perform basic verification checks on structure and accuracy using predefined validation scripts in a team-supported data environment.
  • Data mapping documentation — assist in mapping data fields between source systems and data warehouses following established templates and guidelines.
  • Data extraction procedures — execute existing ETL scripts from administration or billing systems under senior specialist direction in a production support setting.
  • Warehouse database structures — assist in designing simple schemas by applying foundational relational database principles in a supervised project environment.
  • Data warehouse standards — review and apply existing organizational naming conventions and structural standards when working with warehouse elements.
  • Troubleshooting support — document and escalate data warehouse incidents by following established triage protocols in a helpdesk-supported environment.
  • Programming tasks — modify small, well-defined segments of existing ETL or reporting code using current languages under code-review oversight.
  • Metadata management software — navigate and query metadata repositories to locate data lineage information under guided instruction in a warehouse environment.
  • Technical documentation — read and interpret system specifications, data dictionaries, and warehouse design documents to support assigned task completion.

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