MS in Analytics
Directed by Joel Sokol, College of Engineering (ISyE); Jeffrey Hu, Scheller College of Business; Polo Chau, College of Computing (CSE)
The one-year Master of Science in Analytics to meet the growing demand for savvy professionals who can transform data into relevant insights for making better business decisions. This interdisciplinary degree program leverages the combined strengths of Georgia Tech in statistics, operations research, computing, and business by melding the world-class expertise of the College of Engineering’s Stewart School of Industrial & Systems Engineering, the College of Computing’s School of Computational Science and Engineering, and the Scheller College of Business.
MS in Quantitative and Computational Finance
Directed by Sudheer Chava, Scheller College of Business
The QCF provides a strong foundation of quantitative skills for complex mathematical modeling, and computational skills to implement these models using multiple statistical techniques and programming languages. It also gives a solid, practical understanding of Finance theory and institutional details.
FLAMEL Traineeship Program
FLAMEL is a doctoral student training program designed to develop innovations in computing, mathematics, material science, and manufacturing in order to accelerate the creation of new high performance materials for applications. The goal of this program is to develop and employ advances in areas such as machine learning algorithms and modeling and simulation for materials applications in an emerging field known as materials informatics. Funded the NSF IGERT program, it provides 24 two-year traineeships to doctoral students over a five-year period (2014-2019).
Data Science for Social Good
The Atlanta Data Science for Social Good (DSSG) program is an intensive, ten-week paid internship experience that places students on multidisciplinary teams working under the supervision of a professor on a problem that comes from a partner in the City of Atlanta or a local non-profit company. Teams solve a real problem in a real context and to give partners an opportunity to tap the skills and ingenuity of a student team. Teams comprise technical expertise and public policy expertise, for a truly interdisciplinary approach. Problems explored come from a variety of domains, including transportation, energy, smart urban development, sustainability, and food systems.
Several short courses coordinated by the IRI will complement these academic degrees to enhance industry-academic relations and to facilitate cross-college collaboration within Georgia Tech. We will work to partner students in data science-related disciplines with students working in data-intensive science and engineering disciplines to strengthen the value of research and education in both programs. In addition, we will look for opportunities to train data scientists in industry through summer courses and short traineeships.
Student-Run Data Analytics Service Center
For more information please contact Srinivas Aluru (CSE), Le Song (CSE), or Hongyuan Zha (CSE)
PhD in Machine Learning
The machine learning (ML) Ph.D. program is a collaborative venture between Georgia Tech's colleges of Computing, Engineering, and Sciences.
Directed by Irfan Essa, College of Computing (IC)