Data Science Doctoral Program
Gain in-demand skills in emerging areas like artificial intelligence, machine learning and language processing in a Ph.D. program designed with input from industry leaders.
An interdisciplinary degree program of the Schaefer School of Engineering and Science and the Stevens School of Business, the data science Ph.D. curriculum drives students to master the bedrock principles, methods and systems for extracting insights from rich data sets. Then, you’ll apply those theories, techniques and applications in practical research alongside Stevens faculty who are working at the forefront of the data science field. Our graduates go on to pursue research careers in academia and secure important positions in industries like business, financial services and life sciences.
The Department of Computer Science offers dynamic opportunities to explore leading-edge research within a close community of faculty mentors. You'll be able to study under a faculty mentor in the area that you find most exciting:
Theoretical underpinnings of data science, including machine learning and artificial intelligence
Applications of data science to financial services
Applications of data science to the life sciences
Computer Science Research
The computer science department at Stevens offers you a maximum amount of flexibility to pursue research opportunities in cutting-edge, competitive areas of exploration like secure systems, machine learning, cryptography and visual computing. You’ll work with recognized leaders in the field, gain exposure to top industry labs and learn sought-after principles that will help propel your career. Learn more about research in the Department of Computer Science.
The Stevens Advantage
Just 15 minutes away from the center of Manhattan, Stevens sits near the heart of one of the world’s top technology hubs. This proximity gives Stevens students exemplary recruitment opportunities with some of the biggest names in business and technology.
More Advantages to Our Program
Cross-disciplinary curriculum and research opportunities
Study under well-known researcher faculty
Application-oriented research
Highly collaborative environment
Opportunity for industry collaborations
Access to leading research universities and national laboratories in the New York City area
Areas of Focus
Mathematical and statistical modelling including multivariate analytics, financial time series and dynamic programming techniques
Machine learning and artificial intelligence applications for statistical learning and financial analytics
Computational systems, exploring advanced algorithm design, distributed systems and cloud technologies
Data management at scale, involving a deeper dive into data technologies, mobile systems and data management
Additional Information
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.
A Tech Forward Education
An expert in data mining and deep learning, Dr. Lappas is an authority in the business impact of fake reviews in social media.
Ted Lappas
Data Science Faculty
A former chief scientist at AT&T Labs, Dr. Belanger has earned more than 30 patents related to data science and business analytics.
David Belanger
Data Science Faculty
A highly cited business researcher investigating applications of machine learning and social network algorithms to solve finance problems.
Germán Creamer
Data Science Faculty
Dr. Florescu is an expert in creating stochastic models for practical application. He leads an international conference on high frequency in finance.
Ionut Florescu
Data Science Faculty
An expert in data mining and deep learning, Dr. Lappas is an authority in the business impact of fake reviews in social media.
Ted Lappas
Data Science Faculty
A former chief scientist at AT&T Labs, Dr. Belanger has earned more than 30 patents related to data science and business analytics.
David Belanger
Data Science Faculty
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Computer Science Doctoral Program
Prepare to make an enduring impact in fields like machine learning, artificial intelligence and cybersecurity with a Ph.D. in computer science from Stevens.
Data Science Doctoral Program
Gain in-demand skills in emerging areas like artificial intelligence, machine learning and language processing in a Ph.D. program designed with input from industry leaders.