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. Statistics and Data Science

Statistics and Data Science

Yale University

Doctoral Degree

Become a contributor for free to openly demonstrate student outcomes, industry alignment & eligibility criteria.

Fields of study include the main areas of statistical theory (with emphasis on foundations, Bayes theory, decision theory, nonparametric statistics), probability theory (stochastic processes, asymptotics, weak convergence), information theory, bioinformatics and genetics, classification, data mining and machine learning, neural nets, network science, optimization, statistical computing, and graphical models and methods.

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

  • New Haven, Connecticut

    Woodbridge Hall, New Haven, Connecticut, 6520

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 15-2051.00

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-populated·from O*NET + BLS
Occupations matched to this program, with median wage, top wage, growth, and openings
SOCOccupationMethodWageGrowthOpenings
Match confidence: high15-2051.00Data Scientiststitle_inference$112,590 median$194,410 top+33.51%8,250
What You'll Learn

Key competencies developed through this program

Auto-populated·from NSX Competency Framework

Mastery: advanced (Level 4)(based on Doctoral 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.

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

Auto-populated·from Scorecard + DOL
Completion Rate
100%
Placement Rate
29%