Data Science Doctoral Program

Who should apply?

We welcome applicants with a master’s degree in a technical discipline (such as computer science, business intelligence and analytics, financial analytics, financial engineering or biomedical engineering and chemical biology). However, exceptional applicants with a bachelor’s degree and relevant work experience will also be considered.

Students may begin this Ph.D. program in the fall semester only. Therefore, applications must be submitted by February 1 for admission the following fall. Applicants are generally notified of their admission status around February 15.

Program Admission Requirements

• An excellent GMAT or GRE score (required for both part-time and full-time applicants)

• 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

For information about fellowships and assistantships, contact Graduate Admissions. Contact >

Data Science Doctoral Program Curriculum Overview

Coursework in the Data Science program is supplemented by rigorous research requirements that challenge students to discover creative solutions to problems in data analysis and computer science. As you work to create original, substantial research for your dissertation, you’ll be supported by faculty advisors from both the business and engineering schools at Stevens, a tremendous advantage in helping you prepare for a research career. A distinguishing feature of the curriculum is its flexibility. Core courses support the four pillars of the program — mathematical and statistical modeling, machine learning and A.I., computational systems, and data management — while students select a concentration that aligns with their career interests.

Concentrations

The program offers two customizable concentrations that draw upon Stevens’ leadership in financial services and life sciences. You'll select at least three courses from either concentration, or work with your advisor to create a concentration in another discipline.

Financial services

This concentration prepares students to lead forays into areas such as financial innovation, high-frequency trading, large-scale portfolio optimization, automated investment systems, financial data mining and visualization, and trade surveillance and financial fraud detection. These topics will be covered with emphasis on practical solutions to the challenges facing investment banks, hedge funds, mutual funds, exchanges and regulators.

Life sciences

This concentration prepares you to pursue advanced research topics, such as computational modeling in biology and biomedical science, bioinformatics, computational and medicinal chemistry, and biomedical data reduction. Statistical modeling, data management and machine learning techniques will help you identify trends in healthcare data and direct research in the pharmaceutical industry, in government or at hospitals.

General electives

With your advisor's approval, you may select from a list of approved general electives to round out your course requirements. Courses in areas like applied machine learning, distributed systems and cloud computing, cognitive computing and web mining are available.

Doctoral Dissertation and Advisory Committee

Following completion of the written exams and all coursework, you are required to write and defend a dissertation in a selected area of concentration. It is expected your dissertation will contribute to the creation of knowledge and the development of theory and practice in a selected area. The dissertation, and related research, is the most significant component of your doctoral degree, and will prepare you for the challenges of doing original work and getting published in competitive peer-reviewed journals.

LEARN MORE ABOUT GENERAL REQUIREMENTS >

If you have existing graduate credits or experience in this area of study, contact [email protected] to discuss opportunities to include it in the curriculum.