The Post-Baccalaureate Certificate in Data Science at Tufts University is an on-campus program designed for students and professionals who want foundational preparation in data science. Students build knowledge in mathematics, programming, probability, statistics, data structures, and applied machine learning while preparing for continued graduate study or data-focused professional opportunities.
The program requires 20–24 credits completed through six Tufts courses. It is available through full-time or part-time study on the Medford/Somerville campus and is typically completed in 12 to 28 months.
Students may pursue a standalone certificate pathway or a combined Post-Baccalaureate/M.S. pathway for those who intend to continue into the M.S. in Data Science.
The Post-Baccalaureate Certificate in Data Science is designed for applicants with at least a bachelor’s degree who want to build foundational preparation in data science. No prior educational experience in data science is required.
This program may be a strong fit for applicants who want to:
Applicants who already have sufficient quantitative and data science preparation for graduate-level coursework should explore the Certificate in Data Science.
The standalone certificate pathway is designed for students seeking foundational preparation in data science through a focused post-baccalaureate credential.
The combined Post-Baccalaureate/M.S. pathway is designed for students who intend to continue into the M.S. in Data Science. For students admitted to both programs, eligible graduate-level courses completed during the post-baccalaureate portion may count toward master’s degree requirements.
Through this coursework, students build knowledge in:
The Post-Baccalaureate Certificate in Data Science is jointly offered by the Department of Computer Science and the Department of Electrical and Computer Engineering at Tufts University School of Engineering.
Together, these departments support teaching and research in data science, machine learning, software and data systems, statistical signal processing, information theory, visualization, scalable computing, and data-driven modeling. Students develop foundational data science knowledge in an interdisciplinary engineering environment that connects computation, quantitative methods, and practical applications.
The program is intended for applicants who want foundational preparation in data science and do not have prior educational experience in the field. Students begin with essential mathematics and programming coursework before completing graduate-level study in probability and applied machine learning.
Students complete a structured curriculum in linear algebra, data structures, programming for data science, discrete mathematics, machine learning, and probabilistic systems analysis. This sequence builds preparation across the mathematical and computational foundations of data science.
Students may pursue a standalone certificate or select a combined Post-Baccalaureate/M.S. pathway intended for continued study in the M.S. in Data Science. This structure allows students to choose an academic plan aligned with their educational goals.
Students complete the program on Tufts’ Medford/Somerville campus, near the technology, research, health, and innovation communities of Greater Boston.
The Post-Baccalaureate Certificate in Data Science can support students and professionals who want foundational preparation in analyzing data and applying computational methods. Students may use this credential to strengthen preparation relevant to areas such as:
The U.S. Bureau of Labor Statistics reports that data scientists had a median annual wage of $112,590 in May 2024. Employment for data scientists is projected to grow 34% from 2024 to 2034.
No. Prior educational experience in data science is not required. The program is designed to build foundational preparation in mathematics, programming, probability, statistics, and applied machine learning.
The Post-Baccalaureate Certificate in Data Science is designed for applicants who need foundational preparation in mathematics, programming, and data science. The Certificate in Data Science is designed for applicants prepared to begin graduate-level data science coursework.
The School of Engineering offers partial tuition scholarships for a select group of Engineering master’s and certificate programs. When you apply for admission, you’ll automatically be considered, there’s no separate scholarship application or additional information required. Applicants are encouraged to apply early for priority scholarship consideration.
Applicants can apply online through Tufts Graduate Admissions Portal. Required materials typically include transcripts, a resume or CV, letters of recommendation, and a statement of purpose. International applicants may also need to submit English proficiency documentation. Visit the admissions page for current deadlines and application requirements.
Research/Areas of Interest: Artificial intelligence, machine learning, reinforcement learning.
Research/Areas of Interest: Machine Learning, Statistical Signal Processing, Information Theory, Optimal Transport
Research/Areas of Interest: Photon-counting imaging, wavefront sensing, low-light passive imaging.
Research/Areas of Interest: data science, software systems engineering, performance analysis, system, network, and data management
Research/Areas of Interest: computational molecular biology, data science, graph algorithms, network science, discrete mathematics
Research/Areas of Interest: Machine learning : probabilistic models, Bayesian inference, variational methods, time-series analysis, semi-supervised learning Clinical informatics : electronic health record analysis
Research/Areas of Interest: Machine Learning, Data Science, Deep Learning, Generative Models, Time Series, Graph Learning
Research/Areas of Interest: Artificial Intelligence, Developmental Robotics, Computational Perception, Robotic Manipulation, Machine Learning, Human-Robot and Human-Computer Interaction
Research/Areas of Interest: data science, algorithms for analysis of biological networks, gene and pathway regulation in human development, algorithms for precision medicine, computational approaches to pharmacogenomics and drug discovery or repositioning