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.
You will take at least one course, and no more than three courses, in each of the four core areas, working with your advisor to select the best portfolio for your career interests. Students who demonstrate competency in a particular subject area may waive the related core course.
Mathematical and Statistical Modeling
- BIA 652 Multivariate Analysis
- FE 542 Time Series with Applications in Finance
- MA 661 Stochastic Optimal Control and Dynamic Programming
Machine Learning and Artificial Intelligence
- BIA 656 Statistical Learning and Analytics
- CS 541 Artificial Intelligence
- CS 559 Machine Learning: Fundamentals and Applications
- FE 690 Advanced Financial Analytics
Data Management at Scale
- BIA 678 Big Data Technologies
- CS 522 Mobile Systems and Applications
- CS 609 Data Management and Exploration on the Web
- BIO 668 Computational Biology
- FE 595 Financial Systems Technology
- CS 549 Distributed Systems and Cloud Computing
- CS 600 Advanced Algorithm Design and Implementation
Ph.D. required courses
All doctoral candidates at Stevens take the following courses to prepare them for the rigorous research involved in the dissertation process.
- PRV 961 Doctoral Signature Credit Seminar
- MGT 719 Research Methods
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.
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.
- FE 546 Optimization Models and Methods in Finance
- FE 545 Design Patterns and Derivative Pricing
- FE 550 Data Visualization Applications
- FE 610 Stochastic Calculus for Financial Engineers
- FE 635 Financial Enterprise Risk Engineering
- FE 680 Derivatives
- FIN 638 Corporate Finance
- FIN 628 Derivatives
- FE 655 Systemic Risk and Financial Regulation
- FE 670 Algorithmic Trading Strategies
- FE 621 Computational Methods in Finance
- FIN 703 Microeconomic Theory
- FIN 704 Econometrics
- FIN 705 Asset Pricing Theory and Applications
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.
- CH 644 Computer Methods in Chemistry
- CH 760 Chemoinformatics
- CHE 660 Advanced Process Control
- CHE 661 Design of Control Systems
- CPE 610 Introduction to Bioinformatics Engineering
- CPE 686 Software Tools in Bioinformatics
- CS 544 Health Informatics
- CS 691 Introduction to System Biology
- CS 694 Advanced Computational Modeling in Biology and Biomaterials Science
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.
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.