Master of Science - Data Science and Engineering

1 Years On Campus Masters Program

Auburn University

Program Overview

The Master of Science in Data Science and Engineering at Auburn University is an interdisciplinary graduate programme designed to prepare students for careers in data science, data engineering and advanced analytics. The curriculum combines statistical methods, data mining, machine learning, database systems and computational technologies, helping students transform large and complex datasets into meaningful insights. Students can choose between two concentrations: Data Engineering, administered by the Department of Computer Science and Software Engineering, and Data Science, managed by the Department of Mathematics and Statistics.

Curriculum Structure

The programme requires a minimum of 30 graduate credit hours and is available through on-campus and online study options. Students complete six foundational courses, concentration-specific electives and a capstone project.

Core Curriculum: Students study Database Systems I (COMP 6120), Data Mining (COMP 6130), Machine Learning (COMP 6630), Intermediate Statistical Methods for Data Science (STAT 6000), Probability and Statistics for Data Science (STAT 6600), and Statistical Learning (STAT 6650). These courses establish the foundations of data management, computational analysis, statistical modelling and predictive techniques.

Advanced Learning and Specialisation: Students select electives according to their chosen concentration. Data Engineering students focus on advanced computing and technology applications, with options such as Cloud Computing, Artificial Intelligence, Deep Learning and Natural Language Processing. Data Science students select advanced statistics and mathematics courses to strengthen their expertise in statistical modelling, computational methods and data analysis.

Final Capstone Project: Students complete a three-credit capstone experience. Data Engineering students undertake the Capstone Engineering Project (COMP 7980), while Data Science students complete the Capstone Project (STAT 7940). These projects allow students to apply their technical and analytical knowledge to practical data science or engineering problems.

Focus Areas

Data Science and Advanced Analytics
Data Engineering
Statistical Modelling and Analysis
Machine Learning and Statistical Learning
Data Mining and Knowledge Discovery
Database Systems and Management
Artificial Intelligence
Deep Learning
Cloud Computing
Natural Language Processing
Computational Statistics
Big Data Technologies

Learning Outcomes

Graduates develop the ability to collect, manage, analyse and interpret large datasets using advanced statistical and computational techniques. They learn to build machine learning models, apply data mining methods, manage database systems and use modern technologies to solve complex data-related problems. The programme also strengthens programming, quantitative reasoning, analytical thinking and practical problem-solving skills through specialised coursework and capstone projects.

Professional Alignment (Accreditation)

The programme provides interdisciplinary graduate-level preparation through Auburn University's Departments of Computer Science and Software Engineering and Mathematics and Statistics. Its curriculum combines theoretical foundations with applied technical coursework and a required capstone project. No separate professional or industry accreditation is specified on the official programme page.

Reputation (Employability Rankings)

Auburn University emphasises the programme's balance of theoretical knowledge and practical application, with coursework in machine learning, data mining, statistical learning and advanced data technologies. Students can complete the degree online or on campus, providing flexibility for different study and professional needs. The university does not publish a specific programme-level QS or Guardian ranking or a verified employment-rate statistic for this degree.

Experiential Learning (Research, Projects, Internships etc.)

At Auburn University, the MS in Data Science and Engineering program is designed to provide students with hands-on experience that is crucial for success in the rapidly evolving field of data science. Students engage in practical learning through various projects, internships, and access to state-of-the-art facilities and tools. This experiential learning approach ensures that you not only understand theoretical concepts but also apply them in real-world scenarios, preparing you for a successful career.

Here are some key aspects of experiential learning in the program:

- Collaborative Projects: Engage in group projects that simulate real-world data challenges, allowing you to work alongside peers and faculty to develop innovative solutions.
- Internship Opportunities: Gain valuable industry experience through internships with leading companies, providing you with insights into the practical applications of data science.
- Advanced Software Tools: Utilize industry-standard software such as Python, R, SQL, and Tableau, equipping you with the technical skills employers are looking for.
- Research Laboratories: Access dedicated research labs equipped with cutting-edge technology, where you can conduct experiments and work on projects that contribute to the field of data science.
- Data Science Institute: Collaborate with the Auburn University Data Science Institute, which focuses on interdisciplinary research and offers networking opportunities with industry professionals.
- Field Trips and Workshops: Participate in field trips to tech companies and workshops led by industry experts, enhancing your learning experience and expanding your professional network.
- Library Resources: Benefit from extensive library resources, including access to databases and journals that are essential for your research and studies.

These opportunities not only enhance your learning experience but also make you a competitive candidate in the job market. If you're ready to take the next step in your education and career, consider applying to the MS in Data Science and Engineering program at Auburn University.

Progression & Future Opportunities

Auburn University's MS in Data Science and Engineering equips graduates with the skills and knowledge to excel in a rapidly evolving field. Alumni have successfully secured positions such as Data Scientist, Machine Learning Engineer, and Data Analyst, showcasing the program's strong alignment with industry needs. The demand for data professionals continues to grow, making this an excellent time to pursue your degree:

- Career Services: Auburn offers dedicated career services, including resume workshops, interview preparation, and networking events with industry leaders, ensuring you are well-prepared for the job market.
- Employment Statistics: Graduates from the program have reported a high employment rate, with many securing jobs within six months of graduation. The average starting salary for data science roles in the region is around $85,000, reflecting the strong demand for skilled professionals.
- University–Industry Partnerships: Auburn has established partnerships with leading companies such as IBM and Accenture, providing students with opportunities for internships, collaborative projects, and real-world experience that enhances their learning.
- Accreditation Value: The program is accredited by the Southern Association of Colleges and Schools Commission on Colleges (SACSCOC), ensuring that your degree holds long-term value in the job market.
- Graduation Outcomes: The program boasts a strong track record of graduates who have gone on to work in prestigious organizations, contributing to innovative projects and advancing their careers in data science.

Further Academic Progression: After completing the MS in Data Science and Engineering, you may choose to pursue a Ph.D. in Data Science, Computer Science, or a related field. This advanced study can open doors to academic positions, research opportunities, and higher-level roles in industry, allowing you to further deepen your expertise and impact in the field.