Applied Statistical Modeling Certificate | The University of Texas at Austin

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: developing (Level 2)(based on Certificate)

Statistical analysis plans — develop and execute routinely for moderately complex studies, adapting methods to meet user needs with limited oversight.
Data quality and preprocessing pipelines — design and apply weighting, imputation, and adjustment procedures independently for standard research datasets.
Validity and efficiency of statistical procedures — evaluate and document for ongoing projects, flagging methodological concerns to senior staff.
Regression, ANOVA, and multivariate techniques — implement and interpret across familiar applied contexts including government, healthcare, or industry settings.
Graphs, charts, and written reports — produce to communicate statistical results clearly to technical and semi-technical audiences in a professional environment.
Sampling frame design and sample size determination — execute for survey or experimental studies using established methodological references.
Statistical programming scripts — write and maintain in R, Python, or SAS to automate recurring analytical workflows within a departmental setting.
Relationships and confounding factors in research data — identify and interpret, providing documented explanations of trends affecting study conclusions.
Client or stakeholder meetings — present statistical findings using charts and bullets, responding to moderately complex questions with confidence.
Business intelligence and data mining tools — apply to extract and synthesize patterns from large organizational datasets in support of ongoing projects.

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.