Data Sciences, B.S. (Information Sciences and Technology)

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)

Only available through the College of Information Sciences and Technology

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)

Only available through the Eberly College of Science

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 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:

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)

Course List

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): 47 credits

Only Available through the College of Information Sciences and Technology

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

Course List

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): 38 credits

Only Available through the Eberly College of Science

Course List

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.

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 Information Sciences and Technology

Undergraduate Academic Advising Center
E103 Westgate Building
University Park, PA 16802
814-865-8947
advising@ist.psu.edu

College of Engineering

CSE Advising
W209 Westgate Building
University Park, PA 16802
cseadvising@engr.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 2023-24 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).

Applied Data Sciences Option: Data Sciences, B.S. at University Park Campus

• View the Suggested Academic Plan for the Computational Data Sciences Option
• View the Suggested Academic Plan for the Statistical Modeling Data Sciences Option

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.

*

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

¹

1 credit of IST 495 is required. A grade of "SA" must be earned in this course. This course can be completed at any time before graduation.

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.

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 satisfy a portion of that General Education requirement. If the student’s program prescribes GWS these courses will replace both ENGL 15/ENGL 30H and CAS 100A/CAS 100B/CAS 100C. Each course is 3 credits.

Advising Notes:

DS, IST, SRA, and MATH courses have enforced prerequisites.

Application Focus Course Listings

Select a minimum of 12 credits from your chosen focus area; at least 6 credits must be at the 300- or 400-levels. Students may also propose a custom application focus, with guidance and approval by an academic adviser or the program coordinator of the Applied DS Option.

Information and Cybersecurity Sciences

Course List

Code	Title	Credits
CYBER 100	Computer Systems Literacy	3
CYBER 100S	Computer Systems Literacy	3
DS 402	Emerging Trends in the Data Sciences	3
IST 140	Introduction to Application Development	3
SRA 111	Introduction to Security and Risk Analysis	3
IST 210	Organization of Data	3
IST 220	Networking and Telecommunications	3
STAT 200	Elementary Statistics	4
SRA 211	Threat of Terrorism and Crime	3
SRA 221	Overview of Information Security	3
SRA 231	Decision Theory and Analysis	3
IST 240	Introduction to Computer Languages	3
IST 242	Intermediate & Object-Oriented Application Development	3
IST 261	Application Development Design Studio I	3
CYBER 262	Cyber-Defense Studio	3
SRA 365	Statistics for Security and Risk Analysis	3
SRA 450	Cyber-Crime and Cyber-Warfare	3
SRA 468	Spatial Analysis of Risks	3
SRA 480	Crisis Informatics	3
IST 451	Network Security	3
IST 454	Computer and Cyber Forensics	3
IST 456	Information Security Management	3

Human-Centered Design and Development

Business Fundamentals

Course List

Code	Title	Credits
ECON 102	Introductory Microeconomic Analysis and Policy	3
ECON 104	Introductory Macroeconomic Analysis and Policy	3
ACCTG 211	Financial and Managerial Accounting for Decision Making	4
STAT 200	Elementary Statistics	4
SCM 200	Introduction to Statistics for Business	4
BA 301	Finance	3
BA 302	Supply Chains	3
BA 303	Marketing	3
BA 304	Management and Organization	3
BLAW 243	Legal Environment of Business	3
IB 303	International Business Operations	3
DS 402	Emerging Trends in the Data Sciences	3

Economics

Course List

Code	Title	Credits
ECON 102	Introductory Microeconomic Analysis and Policy	3
ECON 104	Introductory Macroeconomic Analysis and Policy	3
ECON 106	Statistical Foundations for Econometrics	3
STAT 200	Elementary Statistics	4
SCM 200	Introduction to Statistics for Business	4
ECON 302	Intermediate Microeconomic Analysis	3
ECON 304	Intermediate Macroeconomic Analysis	3
ECON 315	Labor Economics	3
ECON 323	Public Finance	3
ECON 333	International Economics	3
ECON 342	Industrial Organization	3
ECON 402	Decision Making and Strategy in Economics	3
ECON 404		3
ECON 406		3
ECON 407	Political Economy	3
ECON 408		3
ECON 410	Economics of Labor Markets	3
ECON 415	The Economics of Global Climate Change	3
ECON 421		3
ECON 424		3
ECON 425	Economics of Public Expenditures	3
ECON 428	Environmental Economics	3
ECON 442	Managerial Economics	3
ECON 445	Health Economics	3
ECON 447	Economics of Sports	3
ECON 471	Growth and Development	3
ECON 479	Economics of Matching	3
ECON 480	Mathematical Economics	3
DS 402	Emerging Trends in the Data Sciences	3

Psychology

Course List

Code	Title	Credits
PSYCH 100	Introductory Psychology	3
PSYCH 200	Elementary Statistics in Psychology	4
STAT 200	Elementary Statistics	4
PSYCH 212	Introduction to Developmental Psychology	3
PSYCH 221	Introduction to Social Psychology	3
PSYCH 243	Introduction to Well-being and Positive Psychology	3
PSYCH 253	Introduction to Psychology of Perception	3
PSYCH 256	Introduction to Cognitive Psychology	3
PSYCH 260	Neurological Bases of Human Behavior	3
PSYCH 261	Introduction to Psychology of Learning	3
PSYCH 270	Introduction to Abnormal Psychology	3
PSYCH 370	Psychology of the Differently-Abled	3
PSYCH 404	Principles of Measurement	3
PSYCH 410	Child Development	3
PSYCH 412	Adolescence	3
PSYCH 413	Cognitive Development	3
PSYCH 419	Psychology and a Sustainable World	3
PSYCH 423	Social Psychology of Interpersonal/Intergroup Relationships	3
PSYCH 424	Applied Social Psychology	3
PSYCH 425	Psychology of Human Emotion	3
PSYCH 441	Health Psychology	3
PSYCH 449	Basic Counseling Skills	3
PSYCH 452	Learning and Memory	3
PSYCH 455	Cognitive Neuroscience	3
PSYCH 457	Psychology of Language	3
PSYCH 456	Advanced Cognitive Psychology	3
PSYCH 458	Visual Cognition	3
PSYCH 473	Behavior Modification	3
PSYCH 484	Work Attitudes and Motivation	3
DS 402	Emerging Trends in the Data Sciences	3

Nutrition

Course List

Code	Title	Credits
BIOL 141	Introduction to Human Physiology	3
CHEM 110	Chemical Principles I	3
CHEM 112	Chemical Principles II	3
CHEM 202	Fundamentals of Organic Chemistry I	3
CHEM 210	Organic Chemistry I	3
BMB 211	Elementary Biochemistry	3
NUTR 100	Nutrition Applications for a Healthy Lifestyle	3
NUTR 175		3
NUTR 211R	Applying Biochemistry to Nutrition	1
NUTR 251	Introductory Principles of Nutrition	3
NUTR 358	Assessment of Nutritional Status	3
NUTR 360	Nutrition Education and Behavior Change Theory	3
NUTR 361	Community and Public Health Nutrition	3
NUTR 390	Nutritional Biochemistry and Physiology	4
NUTR 400	Introduction to Nutrition Counseling	2
NUTR 407	Nutrition for Exercise and Sports	3
NUTR 410	Eating and Weight Disorders	3
NUTR 421	Biocultural Perspectives on Public Health Nutrition	3
NUTR 425	Global Nutrition Problems: Health, Science, and Ethics	3
NUTR 445	Energy and Macronutrient Metabolism	3
DS 402	Emerging Trends in the Data Sciences	3

Food Science

Course List

Code	Title	Credits
FDSC 105	Food Facts and Fads	3
CHEM 110	Chemical Principles I	3
FDSC 200	Introductory Food Science	3
FDSC 201	Introductory Food Science Practicum	1
FDSC 206	Improving Food Quality	3
MICRB 201	Introductory Microbiology	3
MICRB 202	Introductory Microbiology Laboratory	2
BMB 211	Elementary Biochemistry	3
BMB 212	Elementary Biochemistry Laboratory	1
STAT 200	Elementary Statistics	4
STAT 240	Introduction to Biometry	3
STAT 250	Introduction to Biostatistics	3
FDSC 400	Food Chemistry and Analysis (I)	3
FDSC 403	Sensory Data Collection & Analysis	3
FDSC 404	Sensory Evaluation of Foods	3
FDSC 405	Food Engineering Principles	3
FDSC 406W	Physiology of Nutrition	3
FDSC 408	Food Microbiology	3
FDSC 409	Laboratory in Food Microbiology	2
FDSC 410	Food Chemistry and Analysis (II)	3
FDSC 413	Science and Technology of Plant Foods	3
FDSC 414	Science and Technology of Dairy Foods	3
FDSC 415	Science and Technology of Muscle Foods	3
FDSC 430	Unit Operations in Food Processing	3
FDSC 444	Arguing about Food	3
DS 402	Emerging Trends in the Data Sciences	3

Astronomy

Course List

Code	Title	Credits
ASTRO 21	Introduction to Research in Astronomy	2
ASTRO 120	The Big Bang Universe	3
ASTRO 130	Black Holes in the Universe	3
ASTRO 140	Life in the Universe	3
ASTRO 291	Astronomical Methods and the Solar System	3
ASTRO 292	Astronomy of the Distant Universe	3
ASTRO 401	Fundamentals of Planetary Science and Astronomy	4
ASTRO 402W	Astronomical Telescopes, Techniques, and Data Analysis	3
ASTRO 496	Independent Studies	1-3
BIOL/GEOSC 474	Astrobiology	3
PHYS 211	General Physics: Mechanics	4
PHYS 212	General Physics: Electricity and Magnetism	4
PHYS 250	Introductory Physics I	4
PHYS 251	Introductory Physics II	4
DS 402	Emerging Trends in the Data Sciences	3

Custom Application Focus

There is an option for a student to create a custom 4-course application focus sequence. It must be a coherent sequence of courses that provides context for the student in terms of content relevant to the Data Sciences program. This sequence gives the student an opportunity to receive cross-training in another domain so that the student can effectively formulate and solve data science problems in the context of the chosen domain. The sequence should contain at least six credits of 300- or 400-level coursework. It must be selected in consultation with an academic adviser or the program coordinator for the Applied Option of the Data Sciences program.

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 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

Professional Resources

• Association for Computing Machinery
• Association for Information Science and Technology

Contact

University Park

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

College of Engineering

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
W209 Westgate Building
University Park, PA 16802
814-865-9505
trk149@psu.edu
bam136@psu.edu

https://www.eecs.psu.edu

Eberly College of Science

DEPARTMENT OF STATISTICS
326 Thomas Building
University Park, PA 16802
814-865-1348
stat-advising@psu.edu

https://science.psu.edu/stat/contact-us