Data Sciences

The data sciences degree is part of an intercollege initiative among the College of Information Sciences and Technology (IST), College of Engineering, and Eberly College of Science to meet the need of professionals who can make sense of big data. The program provides students with the technical fundamentals of data sciences, with a focus on developing the knowledge and skills needed to manage and analyze large-scale, unstructured data to address an expanding range of problems in industry, government, and academia. As a result, data sciences graduates will possess the core skills and problem-solving approaches to compete for leading-edge analytics positions across many different industry sectors.

Computational Data Sciences Option

The computational option for data science, offered only through the Department of Computer Science and Engineering, focuses on the computational foundations of data science, including the design, implementation and analysis of software that manages the volume, heterogeneity and dynamic characteristics of large data sets and that leverages the computational power of multicore hardware. Students in this option will take upper-level courses in computer science and related fields to develop the skills necessary to construct efficient solutions to computational problems involving large data sets.

The mission of our undergraduate program is to prepare our students for a wide range of careers as computational data scientists and related positions in the field of computing. Our curriculum covers fundamental programming techniques and skills, broad knowledge of data science foundations, mathematical foundations of computing, and advanced topics in computing with large data sets. This curriculum provides students with the skills needed to design, develop, evaluate and analyze software solutions to computational problems involving large data and prepares them to be leaders throughout their careers. This program is intended to produce data science professionals with a deep understanding of how to compute with large data and not merely technicians who can use off-the-shelf tools. Success requires a strong aptitude in mathematics.

A description of all the data sciences courses can be found in LionPATH.

Below is a typical 4-year course load for data sciences students:

First Semester (15 credits)

  • MATH 140 Calculus with Analytic Geometry I*
  • CMPSC 121 Introductory Programming*
  • STAT 200 Elementary Statistics*
  • GN, GS, GH or GA course
  • First-year Seminar

Second Semester (16 credits)

  • Math 141 Calculus with Analytic Geometry II*
  • IST 210 Organization of Data*
  • CMPSC 122 Intermediate Programming*
  • ENGL 015 Rhetoric and Composition
  • GN, GS, GH or GA course

Third Semester (15 credits)

  • MATH 220 Matrices (linear equations, matrix algebra)
  • MATH 230 Calculus and Vector Analysis*
  • CAS 100 (GWS) Effective Speech
  • DS 220 Data Management for Data Sciences*
  • GA, GH, GS or GN course

Fourth Semester (15 credits)

  • STAT 414 Introduction to Probability Theory
  • STAT 380 Data Science through Statistical Reasoning and Computation
  • ENGL 202C Technical Writing
  • GA, GH, GS or GN course
  • GA, GH, GS or GN course

Fifth Semester (16.5 credits)

  • DS 300 Data Privacy & Security*
  • STAT 415 Mathematical Statistics
  • CMPSC 360 Discrete Mathematics*
  • Course from Option A List below
  • GA, GH, GS or GN course
  • GHA course

Sixth Semester (16.5 credits)

  • DS 340W Applied Data Sciences
  • CMPSC 465 Data Structures and Algorithms*
  • CMPSC 448 Machine Learning*
  • GA, GH, GS or GN course
  • GHA Course

Seventh Semester (16 credits)

  • CMPSC 442 Artificial Intelligence
  • CMPSC 461 Programming Language Concepts
  • Course from Option A List below
  • Departmental List Course
  • GA, GH, GS or GN course

Eighth Semester (15 credits)

  • DS 440 Capstone
  • DS 410 Data Analytics at Scale
  • Course from Option B List below
  • Department List Course
  • GA, GH, GS or GN course

List A (select 6 credits)

  • CMPEN 454 Fundamentals of Computer Vision
  • CMPSC 450 Concurrent Scientific Programming
  • CMPSC 451 Numerical Computations
  • CMPSC 455 Introduction to Numerical Analysis
  • CMPSC 456 Introduction to Numerical Analysis II

List B (select 6 credits)

  • CMPSC 431W Database Management Systems
  • EE 456 Introduction to Neural Networks
  • IST 441 Information Retrieval and Organization
  • STAT 416 Stochastic Modeling
  • STAT 440 Computational Statistics

*A grade of C or better in these courses is required for graduation (MATH 140, MATH 141, CMPSC 121 Inroductory Programming, CMPSC 122 Intermediate Programming, STAT 200, IST 210 require a C or better for entrance to the major). If a course requires a C or better and the course is a prerequisite for another course, a C is required to meet the prerequisite.

cover of 2018 data sciences undergraduate handbook cover of 2018 data sciences undergraduate handbook


The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research in fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

School of Electrical Engineering and Computer Science

The Pennsylvania State University

207 Electrical Engineering West

University Park, PA 16802


Department of Computer Science and Engineering


Department of Electrical Engineering