Aerial view of campus with Williamsport, the Susquehanna River and Bald Eagle Mountain as a backdrop

Data Science (DSCI)

Associate Professor: M. Kurtz (Co-coordinator)
Assistant Professor: M. Smith (Co-coordinator)

  • Courses Required for the Major: 16 (B.S. Degree)
  • Capstone Requirement: DSCI 401
  • Math prerequisite for major (not counted in major): math placement level 4 or completion of
    MATH 127
  • Minor: Data Science

A major in Data Science includes substantial coursework in data science, computer science, and mathematics. This major will prepare students for a career as a data analyst, as well as for graduate study in data science. A minor in data analytics is suitable for students of all majors who wish to better understand, manage, analyze and use data in their careers.

Major Requirements

A major in Data Science requires the following courses in computer science, data science, and mathematics: DSCI 101, 201, 401; CPTR 125, 246, 247, 339; MATH 128, 129, 130, 214, 234, 238, 434, and 443; and one course chosen from ECON 340 or PHYS 330.

Capstone Requirement

All data science majors are required to complete DSCI 401, in which they will complete an applied research project that will apply the tools in data science, mathematics and computer science they have learned.

Minor Requirements

A minor in Data Science consists of DSCI 101 and 201; CPTR 124 (preferred) or 125; MATH 123 or 214; MATH 130; and one course chosen from CPTR 339, ECON 340, or PHYS 330.

101
INTRODUCTION TO DATA SCIENCE
Suitable for students of any major, this course is an interactive introduction to the world of data science and the skills needed to harness the power of data and modeling to understand the complex world all around us. Students explore data topics such as exploration, cleaning, manipulation, visualization, effective communication, simple modeling, and ethical issues in data science. Through practical applications, students participate in the lifecycle of a data science project, gain experience with common data science software, and explore career pathways in data science. Prerequisites: math placement level 2 or higher, MATH 100, or consent of instructor.

201
DATA, MODELING, AND ALGORITHMS
Through practical applications to a wide range of domains drawing from social science, physical science, and the humanities, students explore common practices and nuances of data management and common machine learning algorithms. Students learn to identify a well-defined research question, collect and curate data, select the optimal techniques to analyze the data, implement the chosen algorithms or models, assess model performance, communicate relevant, interesting, and actionable insights, and think critically about ethical issues involved in data science. Prerequisites: DSCI 101 and either CPTR 124 or CPTR 125.

401
DATA SCIENCE CAPSTONE
This is a project-based capstone course leading students through the process of project conceptualization, data collection/cleaning/exploration, analysis, and communication. Students present their work both in writing and orally/visually to peers at the end of the semester. Prerequisites: DSCI 201; either MATH 123 or MATH 214; MATH 130; CPTR 339; and either ECON 340, or PHYS 330.

CPTR 124
PROGRAMMING FOR DATA SCIENCE
This course introduces students to the structure and use of a dynamically typed computer programming language and exposes students to the basics of computational thinking. It covers topics in data representation, implementation of mathematically correct control and data flow through a program, the algorithmic representation of a problem, and covers concepts of procedural programming. The course introduces students to mathematical and logical operators, and the implementation of propositional and predicate logic using code. In addition, an introduction to data visualization and effective use of basic statistical functions are covered. This introductory course is intended for non-majors in Computer Science. Prerequisites: Math placement level 2 or higher, or consent of instructor.