New York University Graduate School of Arts and Science 6 courses 72 total credits
Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers who are fluent in the emerging field of data science, and to develop a native environment for their education and training. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has been recognized by the NSF through an NRT training grant. The CDS PhD program model rigorously trains data scientists of the future who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) clearly communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) effectively translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work. Our programmatic mission is to nurture this new generation of data scientists, by designing and building a data science environment where methodological innovations are developed and translated successfully to domain applications, both scientific and social. Our vision is that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle: Advances in data science methodology transform the process of discovery in the sciences, and enable effective data-driven governance in the public sector. At the same time, the demands of real-world translational projects will catalyze the creation of new data science methodologies. An essential ingredient of such methodologies is that they embed ethics and responsibility by design.
Major Requirements
| Code | Course Title | Credits |
|---|---|---|
| DS-GA 2003 | Introduction to Data Science | 3 |
| DS-GA 1002 | Probability and Statistics for Data Science I | 3 |
| DS-GA 1003 | Machine Learning | 3 |
| DS-GA 1004 | Big Data | 3 |
| DS-GA 1005 | Inference and Representation | 3 |
| DS-GA 2001 | Research Rotation | 18 |
| Total Major Requirements | 72 credits | |
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