Program Overview
Stevens Institute of Technology's Data Science Doctoral Program is an interdisciplinary program that equips students with the skills and knowledge to excel in data science, particularly in emerging areas like artificial intelligence, machine learning, and language processing. The program emphasizes a strong foundation in data analytics and modeling, machine learning, computational systems, and data management at scale. Graduates are prepared for research careers in academia and important positions in industries such as business, financial services, and life sciences.
Program Outline
It is designed to equip students with the skills and knowledge necessary to excel in the field of data science, particularly in emerging areas like artificial intelligence, machine learning, and language processing. Students will apply these theories and techniques in practical research alongside Stevens faculty who are at the forefront of the data science field. Graduates are prepared for research careers in academia and important positions in industries such as business, financial services, and life sciences.
Outline:
The program curriculum is structured around four pillars:
- Mathematical and statistical modeling: This pillar covers multivariate analytics, financial time series, and dynamic programming techniques.
- Machine learning and artificial intelligence: This pillar focuses on statistical learning and financial analytics applications of machine learning and AI.
- Computational systems: This pillar explores advanced algorithm design, distributed systems, and cloud technologies. Students can choose a concentration that aligns with their career interests:
- Financial services: This concentration prepares students for roles in financial innovation, high-frequency trading, portfolio optimization, automated investment systems, financial data mining and visualization, and trade surveillance and financial fraud detection.
- Life sciences: This concentration prepares students for advanced research in computational modeling in biology and biomedical science, bioinformatics, computational and medicinal chemistry, and biomedical data reduction. Students can also select from a list of approved general electives with their advisor's approval.
Assessment:
The program requires students to complete rigorous research requirements, culminating in a dissertation. The dissertation is expected to contribute to the creation of knowledge and the development of theory and practice in a selected area. The dissertation and related research are the most significant components of the doctoral degree and prepare students for original work and publication in peer-reviewed journals.
Teaching:
The program boasts a faculty of renowned researchers in artificial intelligence, deep learning, computational intelligence, and other related fields. Students benefit from close collaboration with these faculty members, whose research has led to significant breakthroughs.
Careers:
Graduates of the Data Science Doctoral Program are well-prepared for research careers in academia and important positions in industries such as business, financial services, and life sciences.
Other:
The program offers cross-disciplinary curriculum and research opportunities, application-oriented research, a highly collaborative environment, opportunities for industry collaborations, and access to leading research universities and national laboratories in the New York City area.
