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University Bulletin
Undergraduate Degree Programs

Engineering

Data Sciences

University Park, College of Engineering (DATSC)
University Park, College of Information Sciences and Technology (DATSC)
University Park, Eberly College of Science (DATSC)

Mary Beth Rosson, Associate Dean, Information Sciences and Technology; Chita Das, Department Head, Computer Science and Engineering, College of Engineering

Not all options are available at all Colleges. Contact the College you are interested in entering to determine which options are offered.

The inter-college Data Sciences major will educate students on 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. The underlying knowledge for data sciences derives from machine learning, data mining, computer science, statistics, and visualization, and the emerging science of managing and analyzing data at scale. Students will gain breadth of knowledge through common core classes, as well as depth in one of three options. After taking common courses during the pre-major stage, students will choose among options focused on application (College of IST), computation (College of Engineering) and science (College of Science). Students in all three options will come together in their junior and senior years for two shared capstone experiences. In combination the three options position Penn State to offer highly trained professionals who understand data science’s multiple dimensions for a growing segment of the U.S. economy.

Applied Data Sciences - This option focuses on the principles, methods, and tools for assembly, validation, organization, analysis, visualization, and interpretation of large and heterogeneous data, to support data-driven discovery and decision making, with emphasis on addressing pressing scientific, organizational, and societal challenges. A combination of required and elective courses provides students with the training and skills needed to develop advanced tools and domain-specific analyses that yield actionable knowledge from data. This option also provides critical analytical skills needed to assess the benefits and limitations of data analytics across a broad range of applications.

Computational Data Sciences - This option focuses on the computational foundations of the data sciences, 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 Big Data.

Statistical Modeling Data Sciences - This option focuses on statistical models and methods that are needed to discover and validate patterns in Big Data. Students in this option will take upper-level statistics and mathematics courses, learning to apply the theoretical machinery of quantitative models to the solution of real-world problems involving Big Data.

Entrance Requirements
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:
1. The degree candidate must be taking, or have taken, a program appropriate for entry to the major as shown in the bulletin.
2. The degree candidate must complete the following entrance-to-major requirements: MATH 140 GQ (4) [1]; MATH 141 GQ (1) [1]; CMPSC 121 (3) [1]; CMPSC 122 (3); STAT 200 (GQ) (4)[1]; IST 210 (3)[1]. These courses must be completed by the end of the semester during which the entrance to major process is carried out.

For the B.S. degree in Data Sciences, a minimum of 125 credits is required (at least 18 credits must be taken at the 400 level).

GENERAL EDUCATION: 45 credits
(15 of these 45 credits are included in the REQUIREMENTS FOR THE MAJOR)
(See description of General Education in this bulletin.)

FIRST-YEAR SEMINAR:
(Included in ELECTIVES or GENERAL EDUCATION course selection)

UNITED STATES CULTURES AND INTERNATIONAL CULTURES:
(Included in GENERAL EDUCATION course selection, or REQUIREMENTS FOR THE MAJOR)

WRITING ACROSS THE CURRICULUM:
(Included in REQUIREMENTS FOR THE MAJOR)

ELECTIVES: 5-18 credits

REQUIREMENTS FOR THE MAJOR: 77-90 credits
(This includes 15 credits of General Education courses: 9 credits of GWS and 6 credits of GQ courses.)

COMMON REQUIREMENTS FOR THE MAJOR (ALL OPTIONS): 50 credits

PRESCRIBED COURSES (41 credits)
CMPSC 121 GQ(3)[1], CMPSC 122(3)[1], DS 220(3)[1], DS 300(3)[1], DS 340(3)[1], DS 440(3)[1], ENGL 202C GWS(3), IST 210(3)[1], MATH 140 GQ(4)[1], MATH 141 GQ(4)[1], MATH 220 GQ(2)[1], STAT 200 GQ(4)[1], STAT 380(3)[1]

ADDITIONAL COURSES (9 credits)
CAS 100 GWS(3), ENGL 015 GWS(3); ENGL 137/CAS 137 GWS(3), ENGL 138/CAS 138 GWS(3) (Sem: 1-6)
STAT 318/MATH 318(3)[1]; STAT 414/MATH 414(3)[1] (Sem: 3-4)

REQUIREMENTS FOR THE OPTION: 27-40

APPLIED DATA SCIENCES: 40 credits

PRESCRIBED COURSES (22 credits)
IST 110 GS(3)[1], IST 230(3)[1], DS 200(3)[1], DS 310(3)[1], DS 320(3)[1], DS 330(3)[1], DS 410(3)[1], IST 495(1)[1] (Sem: 5-6)

ADDITIONAL COURSES (6 credits)
SRA 231(3); IST 442 IL(3); SODA 308(3); IST 445(3) (Sem: 5-8)
IST 337(3); IST 441(3); DS 402(3); IST 462(3) (Sem: 5-8)

SUPPORTING COURSES AND RELATED AREAS (12 credits)
Select 6 credits from Applied Option List A  (Sem: 5-8)
Select 6 credits from Applied Option List B (Sem: 5-8)
(Students may apply up to 3 credits of ROTC as option list credits and 3 credits of ROTC as GHA credits)

COMPUTATIONAL DATA SCIENCES: 38 credits

PRESCRIBED COURSES (25 credits)
MATH 230(4)[1], CMPSC 360(3)[1], CMPSC 448(3), CMPSC 465(3)[1], STAT 415/MATH 415(3)[1], CMPSC 461(3), DS 410(3)[1], CMPSC 442(3)

ADDITIONAL COURSES (1 credit)
1 credit of First-Year Seminar (Sem: 1-2)

SUPPORTING COURSES AND RELATED AREAS (12 credits)
Select 6 credits from Option List A courses
Select 6 credits from Option List B courses
(Students may apply up to 3 credits of ROTC as option list credits and 3 credits of ROTC as GHA credits)

STATISTICAL MODELING DATA SCIENCES: 27 credits

PRESCRIBED COURSES (11 credits)
MATH 230(4), STAT 184(1), STAT 440(3), STAT 462(3)

ADDITIONAL COURSES (4 credits)
MATH 311W(3)[1]; CMPSC 360(3)[1] (Sem: 5-8)
1 credit of First-Year Seminar (Sem: 1-2)

SUPPORTING COURSES AND RELATED AREAS (12 credits)
Select 6 credits from Quantitative Modeling Option List A courses
Select 6 credits from Quantitative Modeling Option List B courses
(Students may apply up to 3 credits of ROTC as option list credits and 3 credits of ROTC as GHA credits)

List of Applied Data Sciences Option Courses

List of Computational Data Sciences Courses

List of Statistical Modeling Data Sciences Courses

[1] A student enrolled in this major must receive a grade of C or better, as specified in Senate Policy 82-44.

Last Revised by the Department: Fall Semester 2015

Blue Sheet Item #: 44-02-038

Review Date: 10/13/2015

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