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Program Description
Data Sciences is a field of study concerned with developing, applying, and validating methods, processes, systems, and tools for drawing useful knowledge, justifiable conclusions, and actionable insights from large, complex and diverse data through exploration, prediction, and inference. Data Sciences integrate aspects of Computer Science, Informatics, and Statistics to yield powerful data science methods, systems, tools, and best practices that find applications across a broad range of application domains. The curriculum for the major is designed to equip students with the knowledge and the skills needed to elicit, formulate, and solve data sciences problems using modern data science methods, tools, and best practices for data management, data exploration, data integration, predictive modeling (using machine learning), and effectively communicate their findings to, and collaborate with a broad range of stakeholders. The students will gain the critical analytical skills needed to assess the feasibility, benefits, effectiveness, limitations, risks, and ethical implications of applying data sciences methods in different settings. Experiences such as the capstone project prepare students to function effectively as members of interdisciplinary data science teams to harness the potential of data to enable discovery, optimize products and processes, and inform decisions. As distinct from majors that focus primarily on developing data science knowledge and skills to support inquiry in other domains, the primary focus of the Data Sciences major is on the development, evaluation, application, and validation of the data science tools themselves. All students in the major receive in-depth training in data sciences through a set of core courses. Additionally, data sciences students specialize in one of the following options: applied, computational, or statistical modeling data sciences, as described below.
Applied Data Sciences (DATSC_BS, DTSAB_BS)
Only available through the College of Information Sciences and Technology and Penn State Abington
The students in the Applied DS option will receive exposure to an application domain so they are equipped to formulate and solve data science problems drawn from the chosen domain, e.g., life and health sciences, business, behavioral and cognitive sciences, physical sciences, agricultural sciences, among others.
Computational Data Sciences (DTSCE_BS)
Only available through the College of Engineering
The students in the Computational DS option will receive additional training in Computer Science to be able to design, analyze, implement, and deploy advanced algorithms, hardware and software architectures, and systems for data management and analyses.
Statistical Modeling Data Sciences (DTSCS_BS, DTSAB_BS)
Only available through the Eberly College of Science and Penn State Abington
The students in the Statistical modeling DS option will receive additional training in Statistics to be able to formulate, develop, and apply the proper statistical models and methods for data analyses, e.g., experiment design, sampling, hypotheses testing, and limiting false discovery.
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 using AI and data science techniques.
- 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 123 credits is required:
| Requirement | Credits |
|---|---|
| General Education | 45 |
| Electives | 3-12 |
| 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 340W | Applied Data Sciences | 3 |
| DS 435 | Ethical Issues in Data Science Practice | 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 | ||
| Additional Courses: Require a grade of C or better | ||
| 1 credit of First-Year Seminar | 1 | |
| 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 | |
| DS 440 | Data Sciences Capstone Course | 3 |
| or DS 440W | Data Science Capstone | |
| Requirements for the Option | ||
| Select an option | 38-47 | |
Requirements for the Option
Applied Data Sciences (DATSC_BS, DTSAB_BS): 47 credits
Only Available through the College of Information Sciences and Technology and Penn State Abington
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| Prescribed Courses: Require a grade of C or better | ||
| DS 200 | Introduction to Data Sciences | 4 |
| DS 300 | Privacy and Security for Data Sciences | 3 |
| DS 305 | Algorithmic Methods and Tools | 3 |
| DS 310 | Machine Learning for Data Analytics | 3 |
| DS 320 | Data Integration | 3 |
| DS 330 | Visual Analytics for Data Sciences | 3 |
| DS/CMPSC 410 | Programming Models for Big Data | 3 |
| IST 495 | Internship | 1 |
| Additional Courses | ||
| Select 6 credits from any combination: | 6 | |
| Emerging Trends in the Data Sciences | ||
| Network Analytics | ||
| Artificial Intelligence | ||
| Research Project | ||
| Information Retrieval and Organization | ||
| Information Technology in an International Context | ||
| Research Design for Social Data Analytics | ||
| Additional Courses: Require a grade of C or better | ||
| Select 3 credits from the following: | 3 | |
| Discrete Mathematics for Computer Science | ||
| Language, Logic, and Discrete Mathematics | ||
| Concepts of Discrete Mathematics | ||
| Select 3 credits from the following: | 3 | |
| Elementary Probability | ||
| Introduction to Probability Theory | ||
| Introduction to Probability and Stochastic Processes for Engineering | ||
| Supporting Courses and Related Areas 1 | ||
| Select 12 credits from the lists of Application Focus courses; 6 credits must at at the 300- or 400-levels. | 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): 47 credits
Only Available through the College of Engineering
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| 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 448 | Machine Learning and Algorithmic AI | 3 |
| CMPSC 461 | Programming Language Concepts | 3 |
| CMPSC 465 | Data Structures and Algorithms | 3 |
| DS/CMPSC 410 | Programming Models for Big Data | 3 |
| MATH 230 | Calculus and Vector Analysis | 4 |
| STAT/MATH 414 | Introduction to Probability Theory | 3 |
| STAT/MATH 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 Computational Option List A in Appendix C | 6 | |
| Select 6 credits from Computational Option List B in Appendix C | 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, DTSAB_BS): 38 credits
Only Available through the Eberly College of Science and Penn State Abington
| Code | Title | Credits |
|---|---|---|
| Prescribed Courses | ||
| Prescribed Courses: Require a grade of C or better | ||
| MATH 230 | Calculus and Vector Analysis | 4 |
| STAT/MATH 414 | Introduction to Probability Theory | 3 |
| STAT/MATH 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 Statistical Modeling Option List A courses, see Appendix D | 6 | |
| Select 6 credits from Statistical 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 and Inter-Domain courses do not meet this requirement.)
- Quantification (GQ): 6 credits
- Writing and Speaking (GWS): 9 credits
Breadth in the Knowledge Domains (Inter-Domain courses do not meet this requirement.)
- Arts (GA): 3 credits
- Health and Wellness (GHW): 3 credits
- Humanities (GH): 3 credits
- Social and Behavioral Sciences (GS): 3 credits
- Natural Sciences (GN): 3 credits
Integrative Studies
- Inter-Domain Courses (Inter-Domain): 6 credits
Exploration
- GN, may be completed with Inter-Domain courses: 3 credits
- GA, GH, GN, GS, Inter-Domain courses. This may include 3 credits of World Language course work beyond the 12th credit level or the requirements for the student’s degree program, whichever is higher: 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.
Program Learning Objectives
- Knowledge/Application: Understand the technical fundamentals of data sciences with a focus on developing the knowledge and skills needed to manage and analyze data to solve problems in our world.
- Integrate statistical concepts/methods and computational/machine learning methods to discover the structure of data and build predictive models.
- Apply the principles of data management to organize and use different types of data, both structured and unstructured.
- Problem-Solving & Evaluation: Identify, formulate and solve data science problems that arise in various applications.
- Identify and incorporate relevant abstraction and domain knowledge to formulate data science problems in different application contexts.
- Design or adapt appropriate statistical, machine learning, and other data science methods for solving specific problems.
- Compare, contrast, and evaluate competing data science methods appropriate to the context of the problem.
- Employ modern computing infrastructure to scale up data science methods for massive and complex data.
- Integrate data from multiple sources while considering the best practices, challenges, and pitfalls of using heterogeneous data to solve problems.
- Communication (Individual and Team): Articulate the benefits, risks, formulation, solution, and results of data science projects to diverse stakeholders, including fellow data scientists, collaborators with subject matter expertise, and the general public, using written, verbal, and visual forms.
- Teamwork: Participate effectively on teams in order to accomplish the goals of a project containing data science components.
- Data Ethics: Critically evaluate and conscientiously respond to the ethical and societal implications of data science practice.
- Analyze the potential human impacts of data-driven technologies, especially for marginalized communities.
- Develop strategies to solve data science problems that reflect shared social and ethical values, such as privacy, security, fairness, and accountability.
- Interpret and apply the ethical responsibilities of computing professionals.
- Ensure reproducibility of data science analyses.
- Lifelong Learning: Recognize the importance of continued learning beyond graduation.
- Demonstrate readiness to join an evolving professional community by participating in professional development, such as reading trade journals and engaging with appropriate professional organizations.
- Demonstrate readiness for independent learning by performing literature reviews and staying abreast of current trends within the field of data science.
- Option Objectives:
- Applied Data Sciences Option: Gain in-depth knowledge in a chosen application focus area and demonstrate skills to formulate and solve data science problems in the context of applications in that area.
- Computational Data Sciences Option: Design, development, and analysis of software (computational solutions) for data science problems.
- Statistical Modeling Data Sciences Option: Demonstrate facility with common regression-based inferential modeling techniques including analysis of variance, generalized linear models, multiple regression, and logistic regression, as well as proficiency in basic statistical optimization and simulation techniques.
Suggested Academic Plan
The suggested academic plan(s) listed on this page are the plan(s) that are in effect during the 2024-25 academic year. To access previous years' suggested academic plans, please visit the archive to view the appropriate Undergraduate Bulletin edition.
Applied Data Sciences Option: Data Sciences, B.S. at Abington 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.
| First Year | |||||
|---|---|---|---|---|---|
| Fall | Credits | Spring | Credits | ||
| MATH 140 (GQ)*‡#† | 4 | MATH 141 (GQ)*‡#† | 4 | ||
| CMPSC 131*# | 3 | CMPSC 132*# | 3 | ||
| ENGL 15 (GWS)‡ | 3 | CAS 100A, 100B, or 100C (GWS)‡ | 3 | ||
| General Education Course (GS) (Recommended: SRA 111) | 3 | Elective (Recommended: IST 210) | 3 | ||
| STAT 184* | 2 | Application Focus Selection (Recommended: IST 220) | 3 | ||
| PSU 1* | 1 | ||||
| 16 | 16 | ||||
| Second Year | |||||
| Fall | Credits | Spring | Credits | ||
| DS 200*# | 4 | DS 220* | 3 | ||
| IST 230* | 3 | MATH/STAT 318* | 3 | ||
| MATH 220* | 2 | STAT 380* | 3 | ||
| Application Focus Selection (Recommended: SRA 221) | 3 | ENGL 202 (GWS)‡ | 3 | ||
| General Education Course (GN) | 3 | General Education Course (Inter-Domain) | 3 | ||
| 15 | 15 | ||||
| Third Year | |||||
| Fall | Credits | Spring | Credits | Summer | Credits |
| DS 300* | 3 | DS 310* | 3 | IST 495* | 1 |
| DS 305* | 3 | DS 320* | 3 | ||
| DS 330* | 3 | CMPSC 410* | 3 | ||
| Application Focus Selection (Recommended: IST 451) | 3 | Application Focus Selection (Recommended: IST 454) | 3 | ||
| General Education Course (GA) | 3 | General Education Course (GH) | 3 | ||
| 15 | 15 | 1 | |||
| Fourth Year | |||||
| Fall | Credits | Spring | Credits | ||
| DS 340W* | 3 | DS 440 (W)* | 3 | ||
| DS 435* | 3 | Additional Course Selection (Recommended: DS 402) | 3 | ||
| Additional Course Selection (Recommended: DS 494) | 3 | General Education Course (GHW) | 3 | ||
| General Education Course (GN) | 3 | General Education Course (Inter-Domain) | 3 | ||
| General Education Course (Exploration) | 3 | General Education Course (Exploration) | 3 | ||
| 15 | 15 | ||||
| Total Credits 123 | |||||
- *
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 Cultural Diversity 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.
General Education includes Foundations (GWS and GQ), Knowledge Domains (GHW, GN, GA, GH, GS) and Integrative Studies (Inter-domain) requirements. N or Q (Honors) is the suffix at the end of a course number used to help identify an Inter-domain course, but the inter-domain attribute is used to fill audit requirements. Foundations courses (GWS and GQ) require a grade of 'C' or better.
Statistical Modeling Data Sciences Option: Data Sciences, B.S. at Abington 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.
| First Year | |||
|---|---|---|---|
| Fall | Credits | Spring | Credits |
| MATH 140 (GQ)*‡#† | 4 | MATH 141 (GQ)*‡#† | 4 |
| CMPSC 131*# | 3 | CMPSC 132*# | 3 |
| ENGL 15 (GWS)‡ | 3 | CAS 100A, 100B, or 100C (GWS)‡ | 3 |
| General Education Course (GA) | 3 | MATH 220* | 2 |
| STAT 184* | 2 | General Education Course (GH) | 3 |
| PSU 1* | 1 | ||
| 16 | 15 | ||
| Second Year | |||
| Fall | Credits | Spring | Credits |
| DS 200*# | 4 | DS 220* | 3 |
| CMPSC 360* | 3 | MATH/STAT 414* | 3 |
| MATH 230* | 4 | STAT 380* | 3 |
| General Education Course (GN) | 3 | ENGL 202 (GWS)‡ | 3 |
| General Education Course (GHW) | 1.5 | General Education Course (Inter-Domain) | 3 |
| 15.5 | 15 | ||
| Third Year | |||
| Fall | Credits | Spring | Credits |
| List B Selection: (Recommended: DS 330) | 3 | DS 310* | 3 |
| List A Selection (Recommended: CMPSC 455) | 3 | List B Selection (Recommended: DS 320 or 410) | 3 |
| MATH/STAT 415* | 3 | STAT 440* | 3 |
| General Education Course (GN) | 3 | General Education Course (GS) | 3 |
| Elective | 3 | Elective | 3 |
| 15 | 15 | ||
| Fourth Year | |||
| Fall | Credits | Spring | Credits |
| STAT 462* | 3 | List A Selection (Recommended: STAT 463) | 3 |
| DS 340W* | 3 | DS 440 (W)* | 3 |
| DS 435* | 3 | Elective | 3 |
| General Education Course (Inter-Domain) | 3 | Elective | 3 |
| General Education Course (Exploration) | 3 | General Education Course (Exploration) | 3 |
| General Education Course (GHW) | 1.5 | ||
| 16.5 | 15 | ||
| Total Credits 123 | |||
- *
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 Cultural Diversity 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.
General Education includes Foundations (GWS and GQ), Knowledge Domains (GHW, GN, GA, GH, GS) and Integrative Studies (Inter-domain) requirements. N or Q (Honors) is the suffix at the end of a course number used to help identify an Inter-domain course, but the inter-domain attribute is used to fill audit requirements. Foundations courses (GWS and GQ) require a grade of 'C' or better.
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 and technical 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 Science and AI Engineers, Data Scientist, Data Analyst, Data Specialist, Data Visualization Specialist, IT Analyst, Machine Learning Engineer, Data Engineer, Business Systems Analyst/Consultant.
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