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The Master of Science in Data Science and Engineering (MS-DSE) at Auburn University is a STEM-designated, interdisciplinary program that equips students with advanced technical and analytical skills to tackle complex, data-intensive challenges in engineering, science, business, and industry. Delivered jointly by the Samuel Ginn College of Engineering and the College of Sciences and Mathematics, this program integrates rigorous coursework in computer science, statistics, and engineering applications, preparing graduates to lead in the era of big data and AI.
Designed for individuals with a quantitative or technical background, the MS-DSE emphasizes both theoretical depth and hands-on experience in extracting actionable insights from structured and unstructured data.
Program Format & Duration
Offered in both Thesis and Non-Thesis (Coursework-only) options
On-campus and online formats available
Duration: Typically 2 years (full-time) or 3+ years (part-time)
STEM-designated (international students eligible for 3-year OPT)
Fall, Spring, and Summer admission cycles
GRE may be required depending on prior qualifications
Core Curriculum Components
The MS-DSE program blends statistical theory, programming, and domain-specific modeling to prepare students for careers that demand both engineering rigor and data-driven innovation.
Required Core Courses
Statistical and Mathematical Foundations
Statistical Learning
Probability and Stochastic Modeling
Optimization for Data Science
Computer Science and Data Engineering
Data Structures and Algorithms
Database Systems and Big Data Technologies
Machine Learning and Artificial Intelligence
Engineering & Domain Applications
Data-Driven Decision Making in Engineering
High-Performance Computing
Modeling and Simulation
Capstone or Thesis
Thesis Track: Original research and formal thesis defense
Non-Thesis Track: Comprehensive project report or case-based capstone
Electives
Students can tailor their learning through electives in areas such as:
Natural Language Processing
Advanced Python and R
Cybersecurity Analytics
Predictive Maintenance & Industrial IoT
Deep Learning and Neural Networks
Auburn’s MS-DSE program integrates hands-on, applied learning throughout the curriculum to ensure that students are workforce-ready upon graduation.
Capstone Project or Thesis
Students collaborate on real-world challenges from industry or government sponsors
Emphasis on data acquisition, preprocessing, exploratory analysis, model building, and deployment
Example topics:
Predictive analytics for energy systems
Smart manufacturing and quality control
AI in healthcare diagnostics
Research Opportunities
Students may work alongside faculty on federally funded projects from NSF, DoD, or DOE
Access to labs such as:
Auburn Cyber Research Center
Center for Industrial Innovation and Engineering Analytics
National Center for Additive Manufacturing Excellence
Internships and Industry Engagement
Auburn’s close ties to regional employers (e.g., NASA, Alabama Power, Boeing) provide students with practical exposure
Optional internship support through the university's Engineering Career Development office
Auburn’s MS-DSE program integrates hands-on, applied learning throughout the curriculum to ensure that students are workforce-ready upon graduation.
Capstone Project or Thesis
Students collaborate on real-world challenges from industry or government sponsors
Emphasis on data acquisition, preprocessing, exploratory analysis, model building, and deployment
Example topics:
Research Opportunities
Students may work alongside faculty on federally funded projects from NSF, DoD, or DOE
Access to labs such as:
Internships and Industry Engagement
Progression and Future Opportunities
Career Outcomes
Roles pursued by graduates include:
Top Employers
Graduates have gone on to careers with:
Further Study
Some students pursue:
Salary Overview-
Top Employers-
Geographic Placement-
Industry Sectors-
Career Growth and Progression-
What This Means for You-
Bachelor’s Degree: Hold a U.S. bachelor’s degree or international equivalent from a regionally accredited institution. Undergraduate Preparation: Complete at least one 3000-level (or higher) statistics course and demonstrate proficiency in a modern programming language.Transcripts: Upload unofficial transcripts from all institutions at application; official transcripts and certified English versions required upon admission.GRE Scores: Optional for the Data Science option—GRE submission is encouraged but not requiredEnglish Proficiency (International Applicants): Submit TOEFL iBT ≥ 79 (with ≥16 each section) or IELTS ≥ 6.5 unless exempt by prior English-medium degree.Application Fee: Pay the graduate application fee ($60 domestic, $70 international). Additional Requirements: Follow program-specific instructions—Data Science option evaluated by Mathematics & Statistics; Data Engineering option by Computer Science; bridge courses available for applicants lacking CS or statistics background (e.g., COMP 1210/2210/3270, STAT 3600/3610, MATH 1610/1620/2660).Degree Requirements: Complete a minimum of 30 graduate credit hours, including core and elective courses, plus a capstone project.Three‑Year Bachelor’s Degrees: Auburn does not specify general acceptance of three‑year bachelor’s degrees; applicants should verify with individual departments whether their degree meets the U.S.-equivalent requirement WES Evaluation: International applicants must provide official transcripts and certified English translations, and credential evaluations like WES are required only if the department specifically requests them—otherwise, they are optional Data AnalystData ScientistBusiness AnalystStatisticianStatistical ProgrammerSAS ProgrammerMachine Learning EngineerDeep Learning EngineerAI ResearcherData EngineerBI DeveloperData ArchitectAnalytics ConsultantAdvisory ConsultantQuantitative AnalystRisk AnalystOperations AnalystMarketing AnalystResearch ScientistInsights AnalystForecasting AnalystHealthcare Data AnalystFinancial Data AnalystProduct Analyst
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Auburn University
