Data Science | University of Minnesota-Twin Cities

Skills & Competencies

Skills developed through this program

Auto-populatedfrom O*NET via SOC 15-2051.00

Skills

Critical ThinkingReading ComprehensionActive ListeningComplex Problem SolvingSpeakingJudgment and Decision MakingWritingActive Learning

Knowledge

Computers and ElectronicsEnglish LanguageMathematicsEngineering and TechnologyCustomer and Personal Service

Tasks

Troubleshoot program and system malfunctions to restore normal functioning.
Provide staff and users with assistance solving computer-related problems, such as malfunctions and
Test, maintain, and monitor computer programs and systems, including coordinating the installation o

Technology

Business intelligence and data analysis softwareData base user interface and query softwareStorage networking softwareCloud-based management softwareProcedure management software

Career Pathways

Occupations this program prepares you for

Auto-populatedfrom O*NET + BLS

Occupations matched to this program, with median wage, top wage, growth, and openings
SOC	Occupation	Method	Wage	Growth	Openings
Match confidence: high15-2051.00	Data Scientists	title_inference	$112,590 median$194,410 top	+33.51%	8,250

Program Pathways

Credentials this program stacks toward

No program pathways.

What You'll Learn

Key competencies developed through this program

Auto-populatedfrom NSX Competency Framework

Mastery: advanced (Level 4)(based on Master's Degree)

Enterprise-wide data science strategy and capability roadmap — define and champion, aligning analytical investments with long-term organizational objectives across all business units.
Organizational standards for model development, validation, and governance — establish and enforce, setting the technical direction that all data science practitioners follow.
Senior data scientists and cross-disciplinary teams — mentor and develop through structured coaching, performance feedback, and deliberate career growth planning.
Novel methodological approaches and innovative tool adoption — lead evaluation and institutionalization of, driving competitive differentiation through intellectual curiosity and calculated risk-taking.
Executive leadership and board-level stakeholders — advise by synthesizing complex analytical insights into strategic narratives that directly inform high-impact business decisions.
Partnerships with engineering, product, and domain leadership — orchestrate to embed data science solutions into core operational and product development workflows at scale.
Organizational risk posture for data, privacy, and algorithmic accountability — shape by developing policies and oversight mechanisms grounded in integrity and regulatory compliance.
Large-scale system overhauls and data platform modernizations — sponsor and govern, ensuring technical excellence and business continuity throughout multi-year transformation programs.
Talent acquisition pipelines and workforce development programs for data science — design and lead, ensuring the organization attracts and retains top-tier analytical professionals.
Firm-wide culture of evidence-based decision-making — cultivate by modeling rigorous critical thinking and championing data-driven practices at every level of the organization.

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.