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.