Introduction to Data Science
The emerging discipline of data science has become essential to making decisions, understanding observations, and solving problems in today’s world. Whether we want to apply artificial intelligence techniques to a problem, build a computational model of real-world phenomena, statistically test a hypothesis, or analyze structured, textual or image data, data science and data analysis techniques are becoming an essential part of every scientist, researcher, engineer, and policy maker’s tool chest.
Penn’s Master of Science in Engineering (MSE) in Data Science prepares students for a wide range of data-centric careers, whether in technology and engineering, consulting, science, policy-making, or understanding patterns in literature, art or communications.
The Data Science Program can typically be completed in one-and-a-half to two years. It blends leading-edge courses in core topics such as machine learning, big data analytics, and statistics, with a variety of electives and an opportunity to apply these techniques in a domain specialization – a depth area – of choice.
The depth area offers both preparatory coursework and a thesis or practicum in a data science application area. Potential areas of specialization include network science (the Warren Center for Network and Data Science), digital humanities (the Price Lab for Digital Humanities), biomedicine (the Institute for Biomedical Informatics), and public policy (the Penn Wharton Budget Model and the Annenberg Center for Public Policy) — as well as more traditional opportunities in Computer and Information Science and Electrical and Systems Engineering. For students interested in applying data analysis and modeling to other areas within engineering and the physical sciences, Penn offers a specialized and synergistic program in Scientific Computing.
Data Science at Penn
Penn provides the perfect environment for data science enthusiasts, with its strong cross-disciplinary traditions. Biomedical informatics, communications and public policy, robotics, machine learning and artificial intelligence, and data privacy are of broad interest across campus. There are also several extracurricular student groups, including the Penn Data Science Group, which runs speaker panels, events with companies, data projects and kaggle teams . The Philadelphia area is host to multiple biomedical data and cloud startups.