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: proficient (Level 3)(based on Bachelor's Degree)

Complex, non-routine system and program malfunctions — diagnose root causes autonomously and implement durable fixes across interconnected data systems in enterprise environments.
End-to-end analytical solutions for strategic business problems — design and deliver, integrating data from disparate sources using advanced modeling and business intelligence platforms.
Machine learning and predictive models — develop, deploy, and monitor at full production scale, exercising independent judgment on algorithm selection and validation strategy.
Data infrastructure spanning databases, cloud services, and storage networks — architect and optimize to ensure reliability, performance, and security across the organization.
Ambiguous, high-stakes analytical questions — frame, investigate, and resolve by applying inductive and deductive reasoning across novel data environments.
Cross-functional teams and senior stakeholders — advise by translating complex quantitative findings into accessible recommendations through expert oral and written communication.
Procedure management and content workflow software — configure and govern to standardize data science processes and ensure consistency of analytical outputs.
Ethical, legal, and technical risks in data projects — evaluate with high attention to detail and integrity, applying cautious judgment before deployment decisions.
Custom analytical tools and automation scripts — engineer independently within development environments to accelerate team productivity on recurring research tasks.
Organizational data literacy gaps — assess and address by designing training materials and knowledge-sharing sessions that build analytical capability across user groups.

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.