Data Sciences, B.S. (Engineering)

Program Code: DTSCE_BS

Program Description

Data Sciences is an interdisciplinary field concerned with the integration of methods, processes, systems, and tools from Computer Science, Informatics, and Statistics, to discover, validate, and apply knowledge and actionable insights from data, 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 computer science, informatics, and statistics tools for data management, machine learning, information integration, and predictive modeling, and effectively communicate their findings to a broad range of stakeholders. The students will gain the critical analytical skills needed to assess the feasibility, benefits, limitations, risks, and ethical implications of applying data sciences methods in different settings. Through experiences such as the capstone project, students should be prepared 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 public policy. The students in the major will specialize in one of the following options: applied, computational, or statistical modeling data sciences, as described below.

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:

  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: 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 0-9
Requirements for the Major 75-84

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)

Prescribed Courses
Prescribed Courses: Require a grade of C or better
DS 220Data Management for Data Sciences3
DS 340WApplied Data Sciences3
DS 435Ethical Issues in Data Science Practice3
MATH 140Calculus With Analytic Geometry I Keystone/General Education Course4
MATH 141Calculus with Analytic Geometry II Keystone/General Education Course4
MATH 220Matrices Keystone/General Education Course2
STAT 184Introduction to R2
STAT 380Data Science Through Statistical Reasoning and Computation3
Additional Courses
Additional Courses: Require a grade of C or better
1 credit of First-Year Seminar1
CMPSC 121Introduction to Programming Techniques Keystone/General Education Course3
or CMPSC 131 Programming and Computation I: Fundamentals
CMPSC 122Intermediate Programming3
or CMPSC 132 Programming and Computation II: Data Structures
DS 440Data Sciences Capstone Course3
or DS 440W Data Science Capstone
STAT/MATH 318Elementary Probability3
or STAT/MATH 418 Introduction to Probability and Stochastic Processes for Engineering
Requirements for the Option
Select an option38-47

Requirements for the Option

Applied Data Sciences (DATSC_BS): 41 credits
Only Available through the College of Information Sciences and Technology
Prescribed Courses
Prescribed Courses: Require a grade of C or better
DS 200Introduction to Data Sciences4
DS 300Privacy and Security for Data Sciences3
DS 310Machine Learning for Data Analytics3
DS 320Data Integration3
DS 330Visual Analytics for Data Sciences3
DS/CMPSC 410Programming Models for Big Data3
IST 495Internship1
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
Supporting Courses and Related Areas 1
Select 12 credits from the lists of Application Focus courses in Appendix B; 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
Prescribed Courses
Prescribed Courses: Require a grade of C or better
CMPSC 221Object Oriented Programming with Web-Based Applications3
CMPSC 360Discrete Mathematics for Computer Science3
CMPSC 442Artificial Intelligence3
CMPSC 448Machine Learning and Algorithmic AI3
CMPSC 461Programming Language Concepts3
CMPSC 465Data Structures and Algorithms3
DS/CMPSC 410Programming Models for Big Data3
MATH 230Calculus and Vector Analysis4
STAT/MATH 414Introduction to Probability Theory3
STAT/MATH 415Introduction to Mathematical Statistics3
Additional Courses
Additional Courses: Require a grade of C or better
DS 200Introduction to Data Sciences4
or STAT 200 Elementary Statistics Keystone/General Education Course
Supporting Courses and Related Areas 1
Select 6 credits from Computational Option List A in Appendix C6
Select 6 credits from Computational Option List B in Appendix C6
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): 38 credits
Only Available through the Eberly College of Science
Prescribed Courses
Prescribed Courses: Require a grade of C or better
MATH 230Calculus and Vector Analysis4
STAT/MATH 414Introduction to Probability Theory3
STAT/MATH 415Introduction to Mathematical Statistics3
STAT 440Computational Statistics3
STAT 462Applied Regression Analysis3
Additional Courses
Additional Courses: Require a grade of C or better
DS 200Introduction to Data Sciences4
or STAT 200 Elementary Statistics Keystone/General Education Course
DS 310Machine Learning for Data Analytics3
or CMPSC 448 Machine Learning and Algorithmic AI
MATH 311WConcepts of Discrete Mathematics3
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 D6
Select 6 credits from Statistical Modeling Option List B courses, see Appendix D6
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 Keystone/General Education Course 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.

Program Learning Objectives

  1. Knowledge: 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.
    1. Integrate statistical concepts/methods and computational/machine learning methods to discover the structure of data and build predictive models.
    2. Apply the principles of data management to organize and use different types of data, both structured and unstructured.
  2. Problem-Solving and Evaluation: Identify, formulate and solve data science problems that arise in various applications.
    1. Identify and incorporate relevant abstraction and domain knowledge to formulate data science problems in different application contexts.
    2. Design or adapt appropriate statistical, machine learning, and other data science methods for solving specific problems.
    3. Compare, contrast, and evaluate competing data science methods appropriate to the context of the problem.
    4. Employ modern computing infrastructure to scale up data science methods for massive and complex data.
    5. Integrate data from multiple sources while considering the best practices, challenges, and pitfalls of using heterogeneous data to solve problems.
  3. Communication: 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.
  4. Teamwork: Participate effectively on teams in order to accomplish the goals of a project containing data science components.
  5. Data Ethics: Critically evaluate and conscientiously respond to the ethical and societal implications of data science practice.
    1. Analyze the potential human impacts of data-driven technologies, especially for marginalized communities.
    2. Develop strategies to solve data science problems that reflect shared social and ethical values, such as privacy, security, fairness, and accountability.
    3. Interpret and apply the ethical responsibilities of computing professionals.
    4. Ensure reproducibility of data science analyses.  
  6. Lifelong Learning: Recognize the importance of continued learning beyond graduation.
    1. Demonstrate readiness to join an evolving professional community by participating in professional development, such as reading trade journals and engaging with appropriate professional organizations.
    2. Demonstrate readiness for independent learning by performing literature reviews and staying abreast of current trends within the field of data science.
  7. Option Objectives:
    1. 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.
    2. Computational Data Sciences Option: Design, development, and analysis of software (computational solutions) for data science problems.
    3. 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.

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
FallCreditsSpringCredits
CMPSC 121 or 131 (GQ)*#†3CMPSC 122 or 132*#3
MATH 140 (GQ)*‡#†4MATH 141 (GQ)*‡#4
DS 200 or STAT 200*#4DS 220*3
General Education Course3ENGL 15 (GWS)3
First-Year Seminar1General Education Course3
 15 16
Second Year
FallCreditsSpringCredits
CMPSC 221*3CMPSC 360*3
STAT 184*2STAT 380*3
MATH 230*4STAT 414*3
MATH 220*2General Education Course3
CAS 100A or 100B (GWS)‡†3General Education Course3
General Education Course3 
 17 15
Third Year
FallCreditsSpringCredits
CMPSC 442 or DS 442*3CMPSC 410 or DS 410*3
CMPSC 465*3CMPSC 4483
DS 435*3CMPSC 461*3
STAT 415*3General Education Course3
General Education Course3General Education Course3
 15 15
Fourth Year
FallCreditsSpringCredits
DS 340W*3DS 440W*3
List A Course3List A Course3
List B Course3List B Course3
ENGL 202C (GWS)‡†3General Education Course3
Department List (General Elective)3General Education Course (GHW)1.5
General Education Course (GHW)1.5 
 16.5 13.5
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 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:

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

https://www.eecs.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

http://stat.psu.edu/about-us/contact-us