The interdisciplinary Data Sciences degree provides students with breadth through a set of core classes, as well as depth in one of three options. Following a set of common courses in computation, mathematics, databases, and statistics at the pre-major stage, majors will choose among options focused on application (College of IST), computation (College of Engineering) and statistical modeling (College of Science).
The options will allow students to choose an area of specialization that prepares them for specific employment opportunities. Students focusing on computational approaches will take upper level classes in computer science, developing new algorithms and software for harnessing the power of big data. Those focusing on the statistical modeling option will take upper level statistics courses, developing and refining new analytic techniques made possible by big data. Finally, students focusing on the application of these technologies will take a wide range of advanced data sciences classes offered in IST to produce high-level applications and connect big data analytics with decision makers across a variety of specialized domains.
Students in all three options will come together for two shared capstone experiences in their junior and senior years. Combined, the three options will produce highly trained professionals who understand data science’s multiple dimensions for a growing segment of the U.S. economy.
To be eligible for entrance into the Data Sciences major, a degree candidate must be enrolled in the College of Information Sciences and Technology, the College of Engineering, the Eberly College of Science, or the Division of Undergraduate Studies and satisfy requirements for entrance to the major.
Specific entrance requirements include:
- The degree candidate must be taking, or have taken, a set of courses appropriate for entry to the major as shown in the bulletin, including 40-59 cumulated credits of course work.
- The degree candidate must complete the following entrance-to-major courses: Calculus I and II (MATH 140 and MATH 141); Introductory and Intermediate Program (using Python; CMPSC 121 and CMPSC 122); Elementary Statistics (STAT 200); and Organization of Data (IST 210). These courses must be completed by the end of the semester during which the entrance to major process is carried out.
The major also prescribes an additional 20 credits of coursework: Data Management for Data Sciences (DS 220); Privacy and Security for Data Sciences (DS 300); Applied Data Sciences (DS 340W); Data Sciences Capstone Course (DS 440); Technical Writing (ENGL 202C); Matrices (Math 220); and Data Science through Statistical Reasoning and Computation (STAT 380).
Finally, the major requires an additional 6 credits that combine writing with public speaking (e.g., ENGL 015 + CAS 100), and a choice of either Elementary Probability (STAT 318) or Introduction to Probability Theory (STAT 414).