Students must successfully complete the following five courses by the end of their third semester, or show evidence that they have taken equivalent coursework elsewhere.
- DSGA-1001 – Introduction to Data Science. This course introduces students to the fundamental principles of data science that underlie data science algorithms, processes, methods, and data-analytic thinking. It introduces students to algorithms and tools based on these principles and to frameworks to support problem-focused data-analytic thinking. It is offered in the fall semester.
- DSGA-1002 – Probability and Statistics for Data Science. This course introduces basic probabilistic and statistical methods needed in the practice of data science. It is offered in the fall semester.
- DSGA-1003 – Machine Learning and Computational Statistics. This courses covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. It covers the mathematical methods and theoretical aspects, but primarily focuses on algorithmic and practical issues. It is offered in the spring semester.
- DSGA-1004 – Big Data. This course studies the state-of-the-art in big data management: algorithms, techniques, and tools. This course is offered in the spring.
- DSGA-1005 – Inference and Representation. This course covers graphical models, causal inference, and advanced topics in statistical machine learning. It is offered in the fall semester.
Students must successfully complete 57 credit hours of elective courses. Faculty at the Center for Data Science are experts in a broad range of data science topics, and the Center’s course offerings reflect that diversity. For example, students will be able to take courses in Deep Learning, Optimization, and Natural Language Processing.
Some of the pre-approved courses are:
- Deep Learning (DSGA-1008). The course covers a wide variety of topics in deep learning, feature learning and neural computation. It covers the mathematical methods and theoretical aspects as well as algorithmic and practical issues. Deep Learning is at the core of many recent advances in AI, particularly in audio, image, video, and language analysis and understanding.
- Optimization-based Data Analysis (MATHGA-2840). This course covers data-analysis methods that exploit low-dimensional structure, captured by sparse or low-rank models, to extract information from data using optimization.
- Mathematics of Data Science (MATHGA-2830). A course designed for PhD students with an interest in doing research in theoretical aspects of algorithms that aim to extract information from data.
- Natural Language Understanding with Distributed Representations (DSGA-3001). This course examines some of the modern computational approaches, mainly using deep learning, to understanding, processing and using natural languages.
- Research Rotation Courses (DSGA-2001-10, multiple sections). A research rotation is a semester-long guided research experience in which the student will have an opportunity to design and carry out original research in a collaborative setting. The idea is to help students identify research interests. Students undertaking research rotations should sign up for a section of the course DSGA-1010 Research Rotation in Data Science, a three-credit course. PhD students normally take this elective 6 times.
- Preparation for Teaching Data Science (DSGA 2001-11). In this class, students learn effective teaching skills for teaching data science topics to university students. They will help prepare and deliver an assigned course.
- Practical Training for Data Science (DSGA-1009). Practical Training offers course credit for academically relevant internship experience. This is an integral part of the PhD Program curriculum and facilitates students academic and professional development. The course allows students to apply their academic and research knowledge to real-world problems.
Students can take courses that are not on the pre-approved course list with permission from the Director of Graduate Studies (DGS).