Statistical Science | University of Minnesota-Twin Cities

Skills & Competencies

Skills developed through this program

Auto-populatedfrom O*NET via SOC 15-2041.00

Skills

MathematicsReading ComprehensionCritical ThinkingSpeakingActive ListeningComplex Problem SolvingWritingActive Learning

Knowledge

MathematicsComputers and ElectronicsEnglish Language

Abilities

Mathematical ReasoningNumber FacilityWritten ComprehensionOral ComprehensionOral ExpressionWritten ExpressionInductive ReasoningNear VisionDeductive ReasoningInformation Ordering

Tasks

Analyze and interpret statistical data to identify significant differences in relationships among so
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicabilit
Report results of statistical analyses, including information in the form of graphs, charts, and tab

Technology

Data base user interface and query softwareData mining softwareData base management system softwareBusiness intelligence and data analysis softwareAnalytical or scientific software

Tools

Desktop computersLaptop computersPersonal computers

Work Values

IndependenceAchievementRecognitionWorking ConditionsRelationshipsSupport

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: medium15-2041.00	Statisticians	title_inference	$103,300 median$170,700 top	+8.39%	270

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 multivariable and longitudinal statistical models — design, validate, and interpret autonomously across diverse research domains including clinical trials, policy analysis, and industrial applications.
Full-scope data preparation workflows — architect and execute for large-scale or non-standard datasets, resolving inaccuracies and structural anomalies without supervisory input.
Statistical methodology selection — evaluate and justify the most appropriate techniques for novel user needs or research questions, drawing on breadth of theoretical and applied knowledge.
Non-routine methodological challenges — diagnose and resolve, including violations of model assumptions, missing data patterns, and small-sample inference problems in real project environments.
Comprehensive analytical reports — author for senior leadership, regulators, or peer-reviewed publication audiences, integrating statistical and contextual findings with precision and clarity.
Experimental and quasi-experimental designs — develop and test end-to-end, including power analysis and adaptive design modifications, in research or operational settings.
Advanced data mining and machine learning pipelines — build and critically evaluate, integrating statistical rigor with computational methods for high-dimensional datasets.
Peer and client review sessions — lead independently, presenting nuanced statistical results and nonstatistical implications to mixed audiences including executives, scientists, and policymakers.
Interdisciplinary research teams — serve as the statistical authority, advising collaborators on analytic strategy and interpreting quantitative evidence within broader scientific context.
Systems of data collection and measurement — analyze for bias, efficiency, and fitness-for-purpose, recommending design improvements to organizational data infrastructure.

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.