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

Program Code: DATSC_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

  • Knowledge/Application: Understand 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 solve problems in our world.
    • Understand the synergy of statistical concepts/methods and computational/machine learning methods in discovering the structure of predictive models.
    • Understand and apply the technical fundamentals for data modeling to manage massive data (both structured and unstructured data).
    • Understand and apply the technical fundamentals of machine learning for generating predictive models and applying them to the analysis of large-scale data sets.
  • Problem-Solving & Evaluation: Understand, apply, adapt, and evaluate hypothesis-driven and exploratory data analysis strategies, using relevant domain knowledge and abstraction methods.
    • Identify, construct, and incorporate relevant abstraction and domain knowledge (of an application discipline) into problem formulation and the design of predictive modeling.
    • Construct, evaluate, and choose data-enabled predictive models using state-of-the-art machine learning, statistical modeling, and model evaluation methods to reduce the risk of overfitting.
    • Data-enabled design of models that leverage scalable computing infrastructures to meet the desired needs of exploratory data analysis for massive and complex data.
    • Design analytic models by integrating data of multiple modalities and from different sources to achieve synergy for the purpose of improved prediction and facilitating discovery.
    • Design and implement integrated data-enabled models that provide insights and/or enable solutions for high-impact problems in the real world.
  • Communication (Individual and Team): Communicate and work effectively (both individually and in teams) with multiple stakeholders using state-of-the-art visual analytic tools.
    • Formulate insights from data analytic results and communicate these insights effectively (both individually and in teams) with a range of stake holders using suitable visualization methods and tools.
    • Participate effectively on teams in order to accomplish the common goals of a data analytic project.
  • Professional Responsibilities: Understand the professional responsibilities in terms of the ethical, legal, security, and privacy issues regarding data-driven exploration and solution development.
    • Understand the importance and the best practice for protecting sensitive data; understand the issues regarding biases, fairness, and reproducibility throughout the life cycle of a data science project, their implications, and possible ways to address these and other issues related to data ethics.
  • Lifelong Learning: Commit to a passion for discovery that advances the knowledge of humanity toward a better world.

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

Alisha Simon
Academic Adviser
W360 Westgate Building
University Park, PA 16802
814-867-4436
anw114@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).

Applied 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.

First Year
FallCreditsSpringCredits 
MATH 140 (GQ)*‡#4MATH 141*#4 
CMPSC 131*#3CMPSC 132*#3 
ENGL 15 (GWS)3DS 200*#4 
General Education Course3General Education Course3 
PSU 171General Education Course3 
 14 17 
Second Year
FallCreditsSpringCredits 
DS 220*3IST 230, CMPSC 360, or MATH 311W*3 
MATH 220*2STAT/MATH 318 or 418*3 
CAS 100 (GWS)3ENGL 202 (GWS)3 
STAT 1842General Education Course3 
General Education Course3General Education Course3 
General Education Course3  
 16 15 
Third Year
FallCreditsSpringCreditsSummerCredits
DS 300*3DS 330*3IST 495*11
DS 305*3DS 410*3 
DS 310*3STAT 380*3 
DS 320*3Application Focus Selection3 
Application Focus Selection3General Education Course3 
 15 15 1
Fourth Year
FallCreditsSpringCredits 
DS 340W*3DS 440 or 440W*3 
DS 442, IST 442, SODA 308, IST 445, DS 420, IST 441, DS 402, or IST 4943DS 442, IST 442, SODA 308, IST 445, DS 420, IST 441, DS 402, or IST 4943 
DS 435*3Application Focus Selection (300- or 400-level)3 
Application Focus Selection (300- or 400-level)3General Education Course3 
General Education Course3Elective 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

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 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.

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
CYBER 100Computer Systems Literacy3
CYBER 100SComputer Systems Literacy3
IST 140Introduction to Application Development3
SRA 111Introduction to Security and Risk Analysis Keystone/General Education Course3
IST 210Organization of Data3
IST 220Networking and Telecommunications3
STAT 200Elementary Statistics Keystone/General Education Course4
SRA 211Threat of Terrorism and Crime3
SRA 221Overview of Information Security3
SRA 231Decision Theory and Analysis3
IST 240Introduction to Computer Languages3
IST 242Intermediate & Object-Oriented Application Development3
IST 261Application Development Design Studio I3
CYBER 262Cyber-Defense Studio3
SRA 365Statistics for Security and Risk Analysis3
SRA 450Cyber-Crime and Cyber-Warfare3
SRA 468Spatial Analysis of Risks3
SRA 480Crisis Informatics3
IST 451Network Security3
IST 454Computer and Cyber Forensics3
IST 456Information Security Management3
Human-Centered Design and Development
HCDD 113Foundations of Human-Centered Design and Development3
HCDD 113SFoundations of Human-Centered Design and Development FYS3
IST 140Introduction to Application Development3
IST 240Introduction to Computer Languages3
IST 242Intermediate & Object-Oriented Application Development3
IST 210Organization of Data3
IST 220Networking and Telecommunications3
HCDD 264Design Practice in Human-Centered Design and Development3
IST 261Application Development Design Studio I3
IST 311Object-Oriented Design and Software Applications3
HCDD 340Human-Centered Design for Mobile Computing3
HCDD 364WMethods for Studying Users3
IST 402Emerging Issues and Technologies3
Business Fundamentals
ECON 102Introductory Microeconomic Analysis and Policy Keystone/General Education Course3
ECON 104Introductory Macroeconomic Analysis and Policy Keystone/General Education Course3
ACCTG 211Financial and Managerial Accounting for Decision Making4
STAT 200Elementary Statistics Keystone/General Education Course4
SCM 200Introduction to Statistics for Business Keystone/General Education Course4
BA 301Finance3
BA 302Supply Chains3
BA 303Marketing3
BA 304Management and Organization3
BLAW 243Legal Environment of Business3
IB 303International Business Operations3
Economics
ECON 102Introductory Microeconomic Analysis and Policy Keystone/General Education Course3
ECON 104Introductory Macroeconomic Analysis and Policy Keystone/General Education Course3
ECON 106Statistical Foundations for Econometrics3
STAT 200Elementary Statistics Keystone/General Education Course4
SCM 200Introduction to Statistics for Business Keystone/General Education Course4
ECON 302Intermediate Microeconomic Analysis Keystone/General Education Course3
ECON 304Intermediate Macroeconomic Analysis Keystone/General Education Course3
ECON 315Labor Economics Keystone/General Education Course3
ECON 323Public Finance Keystone/General Education Course3
ECON 333International Economics Keystone/General Education Course3
ECON 342Industrial Organization Keystone/General Education Course3
ECON 402Decision Making and Strategy in Economics3
ECON 404Current Economic Issues3
ECON 406The Economics of Social Conflict3
ECON 407Political Economy3
ECON 408Intellectual Property3
ECON 410Economics of Labor Markets3
ECON 415The Economics of Global Climate Change3
ECON 421Analysis of Economic Data3
ECON 424Income Distribution3
ECON 425Economics of Public Expenditures3
ECON 428Environmental Economics3
ECON 442Managerial Economics3
ECON 445Health Economics3
ECON 447Economics of Sports3
ECON 471Growth and Development3
ECON 479Economics of Matching3
ECON 480Mathematical Economics3
Psychology
PSYCH 100Introductory Psychology Keystone/General Education Course3
PSYCH 200Elementary Statistics in Psychology Keystone/General Education Course4
STAT 200Elementary Statistics Keystone/General Education Course4
PSYCH 212Introduction to Developmental Psychology Keystone/General Education Course3
PSYCH 221Introduction to Social Psychology Keystone/General Education Course3
PSYCH 243Introduction to Well-being and Positive Psychology Keystone/General Education Course3
PSYCH 253Introduction to Psychology of Perception Keystone/General Education Course3
PSYCH 256Introduction to Cognitive Psychology Keystone/General Education Course3
PSYCH 260Neurological Bases of Human Behavior3
PSYCH 261Introduction to Psychology of Learning Keystone/General Education Course3
PSYCH 270Introduction to Abnormal Psychology3
PSYCH 370Psychology of the Differently-Abled3
PSYCH 404Principles of Measurement3
PSYCH 410Child Development3
PSYCH 412Adolescence3
PSYCH 413Cognitive Development3
PSYCH 419Psychology and a Sustainable World3
PSYCH 423Social Psychology of Interpersonal/Intergroup Relationships3
PSYCH 424Applied Social Psychology3
PSYCH 425Psychology of Human Emotion3
PSYCH 441Health Psychology3
PSYCH 449Basic Counseling Skills3
PSYCH 452Learning and Memory3
PSYCH 455Cognitive Neuroscience3
PSYCH 457Psychology of Language3
PSYCH 456Advanced Cognitive Psychology3
PSYCH 458Visual Cognition3
PSYCH 473Behavior Modification3
PSYCH 484Work Attitudes and Motivation3
Nutrition
BIOL 141Introduction to Human Physiology Keystone/General Education Course3
CHEM 110Chemical Principles I Keystone/General Education Course3
CHEM 112Chemical Principles II Keystone/General Education Course3
CHEM 202Fundamentals of Organic Chemistry I3
CHEM 210Organic Chemistry I3
BMB 211Elementary Biochemistry3
NUTR 100Nutrition Applications for a Healthy Lifestyle Keystone/General Education Course3
NUTR 175Healthy Food for All: Factors that Influence What we Eat in the US Keystone/General Education Course3
NUTR 211RApplying Biochemistry to Nutrition1
NUTR 251Introductory Principles of Nutrition Keystone/General Education Course3
NUTR 358Assessment of Nutritional Status3
NUTR 360Nutrition Education and Behavior Change Theory3
NUTR 361Community and Public Health Nutrition3
NUTR 390Nutritional Biochemistry and Physiology4
NUTR 400Introduction to Nutrition Counseling2
NUTR 407Nutrition for Exercise and Sports3
NUTR 410Eating and Weight Disorders3
NUTR 421Biocultural Perspectives on Public Health Nutrition3
NUTR 425Global Nutrition Problems: Health, Science, and Ethics3
NUTR 445Energy and Macronutrient Metabolism3
Food Science
FDSC 105Food Facts and Fads Keystone/General Education Course3
CHEM 110Chemical Principles I Keystone/General Education Course3
FDSC 200Introductory Food Science3
FDSC 201Introductory Food Science Practicum1
FDSC 206Improving Food Quality3
MICRB 201Introductory Microbiology3
MICRB 202Introductory Microbiology Laboratory2
BMB 211Elementary Biochemistry3
BMB 212Elementary Biochemistry Laboratory1
STAT 200Elementary Statistics Keystone/General Education Course4
STAT 240Introduction to Biometry Keystone/General Education Course3
STAT 250Introduction to Biostatistics Keystone/General Education Course3
FDSC 400Food Chemistry and Analysis (I)3
FDSC 403Sensory Data Collection & Analysis3
FDSC 404Sensory Evaluation of Foods3
FDSC 405Food Engineering Principles3
FDSC 406WPhysiology of Nutrition3
FDSC 408Food Microbiology3
FDSC 409Laboratory in Food Microbiology2
FDSC 410Food Chemistry and Analysis (II)3
FDSC 413Science and Technology of Plant Foods3
FDSC 414Science and Technology of Dairy Foods3
FDSC 415Science and Technology of Muscle Foods3
FDSC 430Unit Operations in Food Processing3
FDSC 444Arguing about Food3
Astronomy
ASTRO 21Introduction to Research in Astronomy2
ASTRO 120The Big Bang Universe Keystone/General Education Course3
ASTRO 130Black Holes in the Universe Keystone/General Education Course3
ASTRO 140Life in the Universe Keystone/General Education Course3
ASTRO 291Astronomical Methods and the Solar System Keystone/General Education Course3
ASTRO 292Astronomy of the Distant Universe Keystone/General Education Course3
ASTRO 401Fundamentals of Planetary Science and Astronomy4
ASTRO 402WAstronomical Telescopes, Techniques, and Data Analysis3
ASTRO 496Independent Studies1-3
BIOL/GEOSC 474Astrobiology3
PHYS 211General Physics: Mechanics Keystone/General Education Course4
PHYS 212General Physics: Electricity and Magnetism Keystone/General Education Course4
PHYS 250Introductory Physics I Keystone/General Education Course4
PHYS 251Introductory Physics II Keystone/General Education Course4
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 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 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
arc88@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

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