Data Science PhD Program

A unique blueprint for aspiring data scientists

Given the rapid evolution of emerging A.I. concepts like machine learning and language processing, Stevens worked with industry to develop four curricular threads that ensure mastery of the most important principles in this discipline. These areas represent the greatest needs for tomorrow’s data scientist.

• Machine learning and artificial intelligence. Explores statistical learning, A.I., machine learning and financial analytics.

• Mathematical and statistical modeling. Covers multivariate analytics, financial time series and dynamic programming techniques.

• Computational systems. Explores advanced algorithm design, distributed systems and cloud technologies.

• Data management at scale. A deeper dive into data technologies, mobile systems and data management.

At Stevens, the interdisciplinary Ph.D. in Data Science prepares inquisitive students to become pioneers in this space through a rigorous curriculum emphasizing mathematical and statistical modeling, machine learning, computational systems and data management. The program is administered through both the Schaefer School of Engineering and Science and the School of Business at Stevens, ensuring a diverse curriculum that responds to demand for data scientists with extensive knowledge of the theories, techniques and applications associated with data and artificial intelligence. Graduates become research leaders in academia or industry, where they lead the organization’s forays through the data revolution and into the age of A.I. and machine learning.

Who should apply

The Ph.D. in Data Science is a full-time program offered on the Stevens campus in Hoboken, NJ. Applicants must have technical backgrounds — either a master’s degree in a field like computer science or business analytics, or relevant work experience. The program has a strong practical research component, so students will need the intellectual curiosity to do important research alongside Stevens faculty who are breaking new ground in theory and application of data science.

Admission requirements

Admission to the Ph.D. in Data Science is a highly competitive. Classes and research projects explore high-tech concepts in great depth, so only technically oriented students with the highest academic credentials will be admitted.

A list of Stevens admissions criteria is available at Graduate Admissions. Some specialized requirements for entry into this program include: 

• An excellent GMAT or GRE score.

• Prerequisite courses in calculus, statistics, probability, algebra and database management.

• Fluency in at least one programming language, like C++ or Java.

• For international students: An excellent TOEFL or IELTS score.

A master's program in a technical discipline is a requirement for the program, although outstanding candidates with bachelor's degrees will be considered, as well. At Stevens, degree programs like those in Business Intelligence & Analytics, Computer Science, Financial Analytics, Financial Engineering, Biomedical Engineering and Chemical Biology are excellent preparation for the Data Science doctoral program. 

Relevant work experience will be factored into an admissions decision, but is not a requirement for entry to this program.

Curriculum includes:

• mathematical and statistical modeling

• machine learning and artificial intelligence

• data management at scale

• computational systems

• concentrations in either financial services or life sciences

> More info on curriculum