At which campus can I study this program?
Program Description
The intercollege 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 (DATSC_BS)
Only available through the College of Information Sciences and Technology
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 involving Big Data.
Computational Data Sciences (DTSCE_BS)
Only available through the College of Engineering
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 (DTSCS_BS)
Only available through the Eberly College of Science
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.
What is Data Sciences?
Data Sciences is a field that explores the methods, systems, and processes used to extract knowledge from data and turn these insights into discoveries, decisions, and actions. The emergence of massive amounts of data – also known as “big data” – found in our world through healthcare records, human sensors, digital media, and a number of other sources has increased the need for individuals who can obtain useful knowledge from big data and apply it to address major societal challenges across a variety of fields. Students pursuing this degree will develop 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.
MORE INFORMATION ABOUT DATA SCIENCES
You Might Like This Program If...
- You are curious about analyzing information to discover new insights.
- You want to apply data analytics to make strategic decisions.
- You want to understand how data can be used to visualize phenomena and predict different outcomes.
- You are interested in statistics, mathematics, and the social sciences, and want to combine these disciplines to understand what data is really telling us.
MORE INFORMATION ABOUT WHY STUDENTS CHOOSE TO STUDY DATA SCIENCES
Entrance to Major
To be eligible for entrance into the Data Sciences major, a degree candidate must satisfy requirements for entrance to the major.
Specific entrance requirements include:
- The degree candidate must be taking, or have taken, a program appropriate for entry to the major as shown in the bulletin.
- The degree candidate must complete the following entrance-to-major requirements: CMPSC 121* or CMPSC 131*, CMPSC 122* or CMPSC 132*, MATH 140*, MATH 141*, STAT 200* or DS 200*. These courses must be completed by the end of the semester during which the entrance to major process is carried out.
- *
Course requires a grade of C or better.
Degree Requirements
For the Bachelor of Science degree in Data Sciences, a minimum of 125 credits is required (at least 18 credits must be taken at the 400 level):
| Requirement | Credits |
|---|---|
| General Education | 45 |
| Electives | 5-14 |
| Requirements for the Major | 72-81 |
6 of the 45 credits for General Education are included in the Requirements for the Major. This includes: 6 credits of GQ courses.
Requirements for the Major
To graduate, a student enrolled in the major must earn a grade of C or better in each course designated by the major as a C-required course, as specified by Senate Policy 82-44.
Common Requirements for the Major (All Options)
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| Prescribed Courses: Require a grade of C or better | ||
| DS 220 | Data Management for Data Sciences | 3 |
| DS 300 | Privacy and Security for Data Sciences | 3 |
| DS 340W | Applied Data Sciences | 3 |
| DS 440 | Data Sciences Capstone Course | 3 |
| MATH 140 | Calculus With Analytic Geometry I  | 4 |
| MATH 141 | Calculus with Analytic Geometry II | 4 |
| MATH 220 | Matrices | 2 |
| STAT 184 | Introduction to R | 2 |
| STAT 380 | Data Science Through Statistical Reasoning and Computation | 3 |
| Additional Courses | ||
| 1 credit of First-Year Seminar | 1 | |
| Additional Courses: Require a grade of C or better | ||
| CMPSC 121 | Introduction to Programming Techniques | 3 |
| or CMPSC 131 | Programming and Computation I: Fundamentals | |
| CMPSC 122 | Intermediate Programming | 3 |
| or CMPSC 132 | Programming and Computation II: Data Structures | |
| STAT/MATH 318 | Elementary Probability | 3 |
| or STAT/MATH 414 | Introduction to Probability Theory | |
| Requirements for the Option | ||
| Select an option | 35-44 | |
Requirements for the Option
Applied Data Sciences (DATSC_BS): 38 credits
Only Available through the College of Information Sciences and Technology
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| Prescribed Courses: Require a grade of C or better | ||
| DS 200 | Introduction to Data Sciences | 4 |
| DS 310 | Machine Learning for Data Analytics | 3 |
| DS 320 | Data Integration | 3 |
| DS 330 | Visual Analytics for Data Sciences | 3 |
| DS 410 | Programming Models for Big Data | 3 |
| IST 230 | Language, Logic, and Discrete Mathematics | 3 |
| IST 495 | Internship | 1 |
| Additional Courses | ||
| Select 6 credits from any combination: | 6 | |
| Emerging Trends in the Data Sciences | ||
| Artificial Intelligence | ||
| Information Retrieval and Organization | ||
| Information Technology in an International Context | ||
| Globalization Trends and World Issues | ||
| Database Modeling and Applications | ||
| Research Design for Social Data Analytics | ||
| Supporting Courses and Related Areas 1 | ||
| Select 12 credits from the lists of Application Focus courses in Appendix B; 6 credits must be at the 400 level. | 12 | |
- 1
Students may apply up to 3 credits of ROTC as option Application Focus list credits and 3 credits of ROTC as GHW credits.
LIST OF APPLIED DATA SCIENCES COURSES
Computational Data Sciences (DTSCE_BS): 44 credits
Only Available through the College of Engineering
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| CMPSC 448 | Machine Learning and Algorithmic AI | 3 |
| Prescribed Courses: Require a grade of C or better | ||
| CMPSC 221 | Object Oriented Programming with Web-Based Applications | 3 |
| CMPSC 360 | Discrete Mathematics for Computer Science | 3 |
| CMPSC 442 | Artificial Intelligence | 3 |
| CMPSC 455 | Introduction to Numerical Analysis I | 3 |
| CMPSC 465 | Data Structures and Algorithms | 3 |
| DS 410 | Programming Models for Big Data | 3 |
| MATH 230 | Calculus and Vector Analysis | 4 |
| STAT 415 | Introduction to Mathematical Statistics | 3 |
| Additional Courses | ||
| Additional Courses: Require a grade of C or better | ||
| DS 200 | Introduction to Data Sciences | 4 |
| or STAT 200 | Elementary Statistics | |
| Supporting Courses and Related Areas 1 | ||
| Select 6 credits from Applied Option List A in Appendix D | 6 | |
| Select 6 credits from Applied Option List B in Appendix D | 6 | |
- 1
Students may apply up to 3 credits of ROTC as option list credits and 3 credits of ROTC as GHW credits.
LIST OF COMPUTATIONAL DATA SCIENCES COURSES
Statistical Modeling Data Sciences (DTSCS_BS): 35 credits
Only Available through the Eberly College of Science
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| Prescribed Courses: Require a grade of C or better | ||
| MATH 230 | Calculus and Vector Analysis | 4 |
| STAT 415 | Introduction to Mathematical Statistics | 3 |
| STAT 440 | Computational Statistics | 3 |
| STAT 462 | Applied Regression Analysis | 3 |
| Additional Courses | ||
| Additional Courses: Require a grade of C or better | ||
| DS 200 | Introduction to Data Sciences | 4 |
| or STAT 200 | Elementary Statistics | |
| DS 310 | Machine Learning for Data Analytics | 3 |
| or CMPSC 448 | Machine Learning and Algorithmic AI | |
| MATH 311W | Concepts of Discrete Mathematics | 3 |
| or CMPSC 360 | Discrete Mathematics for Computer Science | |
| Supporting Courses and Related Areas 1 | ||
| Select 6 credits from Quantitative Modeling Option List A courses, see Appendix D | 6 | |
| Select 6 credits from Quantitative Modeling Option List B courses, see Appendix D | 6 | |
- 1
Students may apply up to 3 credits of ROTC as option list credits and 3 credits of ROTC as GHW credits.
LIST OF STATISTICAL MODELING DATA SCIENCES COURSES
General Education
Connecting career and curiosity, the General Education curriculum provides the opportunity for students to acquire transferable skills necessary to be successful in the future and to thrive while living in interconnected contexts. General Education aids students in developing intellectual curiosity, a strengthened ability to think, and a deeper sense of aesthetic appreciation. These are requirements for all baccalaureate students and are often partially incorporated into the requirements of a program. For additional information, see the General Education Requirements section of the Bulletin and consult your academic adviser.
The keystone symbol appears next to the title of any course that is designated as a General Education course. Program requirements may also satisfy General Education requirements and vary for each program.
Foundations (grade of C or better is required.)
- Quantification (GQ): 6 credits
- Writing and Speaking (GWS): 9 credits
Knowledge Domains
- Arts (GA): 6 credits
- Health and Wellness (GHW): 3 credits
- Humanities (GH): 6 credits
- Social and Behavioral Sciences (GS): 6 credits
- Natural Sciences (GN): 9 credits
Integrative Studies (may also complete a Knowledge Domain requirement)
- Inter-Domain or Approved Linked Courses: 6 credits
University Degree Requirements
First Year Engagement
All students enrolled in a college or the Division of Undergraduate Studies at University Park, and the World Campus are required to take 1 to 3 credits of the First-Year Seminar, as specified by their college First-Year Engagement Plan.
Other Penn State colleges and campuses may require the First-Year Seminar; colleges and campuses that do not require a First-Year Seminar provide students with a first-year engagement experience.
First-year baccalaureate students entering Penn State should consult their academic adviser for these requirements.
Cultures Requirement
6 credits are required and may satisfy other requirements
- United States Cultures: 3 credits
- International Cultures: 3 credits
Writing Across the Curriculum
3 credits required from the college of graduation and likely prescribed as part of major requirements.
Total Minimum Credits
A minimum of 120 degree credits must be earned for a baccalaureate degree. The requirements for some programs may exceed 120 credits. Students should consult with their college or department adviser for information on specific credit requirements.
Quality of Work
Candidates must complete the degree requirements for their major and earn at least a 2.00 grade-point average for all courses completed within their degree program.
Limitations on Source and Time for Credit Acquisition
The college dean or campus chancellor and program faculty may require up to 24 credits of course work in the major to be taken at the location or in the college or program where the degree is earned. Credit used toward degree programs may need to be earned from a particular source or within time constraints (see Senate Policy 83-80). For more information, check the Suggested Academic Plan for your intended program.
Academic Advising
The objectives of the university’s academic advising program are to help advisees identify and achieve their academic goals, to promote their intellectual discovery, and to encourage students to take advantage of both in-and out-of class educational opportunities in order that they become self-directed learners and decision makers.
Both advisers and advisees share responsibility for making the advising relationship succeed. By encouraging their advisees to become engaged in their education, to meet their educational goals, and to develop the habit of learning, advisers assume a significant educational role. The advisee's unit of enrollment will provide each advisee with a primary academic adviser, the information needed to plan the chosen program of study, and referrals to other specialized resources.
READ SENATE POLICY 32-00: ADVISING POLICY
University Park
College of Engineering
Alisha Simon
Academic Adviser
W360 Westgate Building
University Park, PA 16802
814-867-4436
anw114@psu.edu
College of Information Sciences and Technology
Undergraduate Academic Advising Center
E103 Westgate Building
University Park, PA 16802
814-865-8947
advising@ist.psu.edu
Eberly College of Science
Undergraduate Statistics Office
Academic Advising
323 Thomas Building
University Park, PA 16802
814-865-1348
stat-advising@psu.edu
Suggested Academic Plan
The suggested academic plan(s) listed on this page are the plan(s) that are in effect during the 2022-23 academic year. To access previous years' suggested academic plans, please visit the archive to view the appropriate Undergraduate Bulletin edition (Note: the archive only contains suggested academic plans beginning with the 2018-19 edition of the Undergraduate Bulletin).
Computational Data Sciences Option: Data Sciences, B.S. at University Park Campus
The course series listed below provides only one of the many possible ways to move through this curriculum. The University may make changes in policies, procedures, educational offerings, and requirements at any time. This plan should be used in conjunction with your degree audit (accessible in LionPATH as either an Academic Requirements or What If report). Please consult with a Penn State academic adviser on a regular basis to develop and refine an academic plan that is appropriate for you.
If you are starting at a campus other than the one this plan is ending at, please refer to: http://advising.engr.psu.edu/degree-requirements/academic-plans-by-major.aspx
| First Year | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| CMPSC 121 or 131 (GQ)*#† | 3 | CMPSC 122 or 132*# | 3 |
| MATH 140 (GQ)*‡#† | 4 | MATH 141 (GQ)*‡# | 4 |
| DS 200 or STAT 200*# | 4 | DS 220* | 3 |
| General Education Course | 3 | ENGL 15 (GWS)‡ | 3 |
| First-Year Seminar | 1 | General Education Course | 3 |
| 15 | 16 | ||
| Second Year | |||
| Fall | Credits | Spring | Credits |
| CMPSC 221* | 3 | CMPSC 360* | 3 |
| STAT 184* | 2 | STAT 380* | 3 |
| MATH 230* | 4 | STAT 414* | 3 |
| MATH 220* | 2 | General Education Course | 3 |
| CAS 100A or 100B (GWS)‡† | 3 | General Education Course | 3 |
| General Education Course | 3 | ||
| 17 | 15 | ||
| Third Year | |||
| Fall | Credits | Spring | Credits |
| CMPSC 442 or DS 442* | 3 | CMPSC 410 or DS 410* | 3 |
| CMPSC 465* | 3 | CMPSC 448 | 3 |
| DS 300* | 3 | CMPSC 455* | 3 |
| STAT 415* | 3 | General Education Course | 3 |
| General Education Course | 3 | General Education Course | 3 |
| 15 | 15 | ||
| Fourth Year | |||
| Fall | Credits | Spring | Credits |
| DS 340W* | 3 | DS 440* | 3 |
| List A Course | 3 | List A Course | 3 |
| List B Course | 3 | List B Course | 3 |
| ENGL 202C (GWS)‡† | 3 | General Education Course | 3 |
| Department List (General Elective) | 3 | Department List (General Elective) | 3 |
| General Education Course (GHW) | 1.5 | General Education Course (GHW) | 1.5 |
| 16.5 | 16.5 | ||
| Total Credits 126 | |||
- *
Course requires a grade of C or better for the major
- ‡
Course requires a grade of C or better for General Education
- #
Course is an Entrance to Major requirement
- †
Course satisfies General Education and degree requirement
University Requirements and General Education Notes:
US and IL are abbreviations used to designate courses that satisfy University Requirements (United States and International Cultures).
W, M, X, and Y are the suffixes at the end of a course number used to designate courses that satisfy University Writing Across the Curriculum requirement.
GWS, GQ, GHW, GN, GA, GH, and GS are abbreviations used to identify General Education program courses. General Education includes Foundations (GWS and GQ) and Knowledge Domains (GHW, GN, GA, GH, GS, and Integrative Studies). Foundations courses (GWS and GQ) require a grade of ‘C’ or better.
Integrative Studies courses are required for the General Education program. N is the suffix at the end of a course number used to designate an Inter-Domain course and Z is the suffix at the end of a course number used to designate a Linked course.
All incoming Schreyer Honors College first-year students at University Park will take ENGL 137H/CAS 137H in the fall semester and ENGL 138T/CAS 138T in the spring semester. These courses carry the GWS designation and replace both ENGL 30H and CAS 100. Each course is 3 credits.
College Notes:
- Health and Physical Activity Elective: Students who complete the ROTC program may substitute 3 ROTC credits for the GHW requirement.
- Natural Sciences Elective: Nine credits of Natural Science (GN) are required. Any GN courses except the following may be used: ASTRO 1, 7N, 10, 11, 120, 140; all BI SC courses; All courses below CHEM 110 (except 3 credits of CHEM 106 may be used); PHYS 250, 251, and any course below PHYS 211; GEOSC 20
- OPTION A: CMPEN 454, CMPSC 450, or CMPSC 456
- OPTION B: CMPSC 431W, EE 456, IST 441, STAT 416, or STAT 440
- Department List Course (General Elective): See page 13, 14, & 15 of this document: http://assets.engr.psu.edu/EECS/docs/Computational%20Data%20Sciences%202...
Career Paths
Data Sciences blends the technical expertise needed to analyze, interpret, and manage big data with the interpersonal skills needed to communicate insights to a variety of audiences. The program prepares students to meet the growing need for professionals who have the analytical and problem-solving skills to address a wide range of societal challenges. Many companies participate in career fairs in Engineering, IST and Science with an express interest in hiring data science interns or graduates. A growing number of M.S. and Ph.D. programs await those who wish to pursue more advanced studies.
Careers
Because our courses blend technical knowledge with skills in communication and business, a Data Sciences degree allows students to compete for leading-edge analytics positions across many different industry sectors. Possible careers include: Data Analyst, Data and Analytics Manager, Data Architect, Data Engineering, Data Visualizer, Statistician.
MORE INFORMATION FOR THE APPLIED DATA SCIENCES OPTION
MORE INFORMATION FOR THE COMPUTATIONAL DATA SCIENCES OPTION
MORE INFORMATION FOR THE STATISTICAL MODELING DATA SCIENCES OPTION
Professional Resources
Contact
University Park
College of Engineering
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
W209 Westgate Building
University Park, PA 16802
814-865-9505
arc88@psu.edu
College of Information Sciences and Technology
COLLEGE OF INFORMATION SCIENCES AND TECHNOLOGY
411 Eric J. Barron Innovation Hub Building
State College, PA 16801
814-865-3528
Eberly College of Science
DEPARTMENT OF STATISTICS
326 Thomas Building
University Park, PA 16802
814-865-1348
stat-advising@psu.edu