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

4 Years On Campus Bachelors Program

Pennsylvania State University University Park

Program Overview

The Data Sciences, B.S. (Information Sciences and Technology) at Pennsylvania State University combines computer science, informatics, mathematics, and statistics to prepare students to manage, analyze, and interpret large and complex datasets and turn data into actionable insights. It is well suited to students interested in data-driven problem-solving, AI, analytics, and decision-making, with the opportunity to specialize in Applied Data Sciences, Computational Data Sciences, or Statistical Modeling Data Sciences.

Curriculum Structure:

Year 1: Students build the mathematical and programming foundations needed for data science through courses such as MATH 140 – Calculus With Analytic Geometry I, MATH 141 – Calculus with Analytic Geometry II, and CMPSC 121 – Introduction to Programming Techniques or CMPSC 131 – Programming and Computation I: Fundamentals. These subjects establish quantitative reasoning and programming skills that support later work with data and computational methods.

Year 2: Students develop core data-science capabilities through DS 220 – Data Management for Data Sciences, STAT 184 – Introduction to R, and CMPSC 122 – Intermediate Programming or CMPSC 132 – Programming and Computation II: Data Structures. The curriculum moves from programming fundamentals into data management, statistical computing, and structured approaches to handling data.

Year 3: Students move into applied and advanced data-science topics according to their selected option, including DS 305 – Algorithmic Methods and Tools, DS 310 – Machine Learning for Data Analytics, and DS 320 – Data Integration in the Applied Data Sciences option. Students in the Computational option instead develop deeper computer-science expertise through subjects such as CMPSC 442 – Artificial Intelligence and CMPSC 465 – Data Structures and Algorithms, while the Statistical Modeling option emphasizes areas such as STAT 440 – Computational Statistics and STAT 462 – Applied Regression Analysis.

Year 4: Students consolidate their technical and analytical knowledge through advanced option courses and the DS 440 – Data Sciences Capstone Course, applying data-science methods to substantial problems. Depending on their specialization, students can also study areas such as DS/CMPSC 410 – Programming Models for Big Data, DS/CMPSC 442 – Artificial Intelligence, DS 420 – Network Analytics, or STAT 415 – Introduction to Mathematical Statistics.

Focus Areas: Data management, data exploration, data integration, predictive modeling, machine learning, artificial intelligence, visual analytics, algorithmic methods, programming for big data, privacy and security, statistical modeling, and ethical data-science practice. Students can specialize through Applied Data Sciences, Computational Data Sciences, or Statistical Modeling Data Sciences.

Learning Outcomes: Students develop the ability to formulate and solve data-science problems, manage and analyze large-scale unstructured data, apply data-management and predictive-modeling methods, evaluate the feasibility and limitations of analytical approaches, assess ethical implications and risks, communicate findings effectively, and collaborate with interdisciplinary teams.

Professional Alignment (Accreditation): Not stated: The official Penn State program bulletin does not identify ABET or another professional accreditation for the Data Sciences, B.S. (Information Sciences and Technology). Penn State's ABET accreditation statement applies to its specified engineering programs, not this IST Data Sciences degree.

Reputation (Employability Rankings): Penn State ranks highly for employer reputation: the university reports a No. 92 global and No. 24 U.S. employer-reputation ranking in the QS World University Rankings 2027, while Penn State is No. 92 globally and in the top 6% worldwide overall. Penn State also reports a 5-star QS rating for employability, although these are university-level measures rather than rankings specifically for the Data Sciences B.S. program.

Experiential Learning (Research, Projects, Internships etc.)

Students in Penn State’s Data Sciences, B.S. (Information Sciences and Technology) gain substantial practical experience through interdisciplinary coursework, real-world data analysis, required internships, research opportunities, and shared capstone experiences. The program brings together the College of Information Sciences and Technology, College of Engineering, and Eberly College of Science, allowing students to develop practical skills in data management, machine learning, data integration, visualization, and predictive modeling before applying them to real data and industry problems.

Practical learning opportunities include:

Required Internship: Every student in the Applied Data Sciences option must complete at least one internship before graduation through IST 495, giving students direct workplace experience connected to their data-science studies.
240-Hour Internship Experience: Students can complete a 240-hour internship, either part-time or full-time and either virtually or in person, provided the work relates to the IST curriculum.
Faculty Research Projects: Students may satisfy the internship requirement through a faculty-developed 240-hour research project, working with a faculty member on a real-world problem or case-based learning project.
Data Sciences Capstone: DS 440 – Data Sciences Capstone Course is a semester-long group project involving analysis of real data. Students conceptualize and plan a project, implement their approach, integrate methods such as machine learning, data mining, data integration and visualization, and present their findings through a written report and oral presentation.
Machine Learning and AI: The Applied Data Sciences curriculum includes DS 310 – Machine Learning for Data Analytics, while students can also study DS/CMPSC 442 – Artificial Intelligence, providing hands-on exposure to modern computational approaches to data analysis.
Data Integration and Visualization: DS 320 – Data Integration and DS 330 – Visual Analytics for Data Sciences develop practical skills for combining data from different sources and communicating analytical findings visually.
Big Data Computing: DS/CMPSC 410 – Programming Models for Big Data gives students experience with programming approaches designed for large-scale data processing.
Nittany AI Alliance: Students can work with industry partners through the Nittany AI Alliance, which connects students with real-world problems and AI-based solutions through challenges, internships, events, and experiential learning.
Nittany Data Labs: Students can extend their classroom learning through student organizations such as Nittany Data Labs, alongside mentored research and other data-focused experiential opportunities.
Career and Employer Network: The College of IST’s Office of Career Solutions and Corporate Engagement provides internship resources and connections with hundreds of organizations, including IST Recruiting Partners that actively recruit students.
Interdisciplinary Learning: Students can choose the Applied Data Sciences, Computational Data Sciences, or Statistical Modeling Data Sciences option, allowing practical data-science preparation to be combined with an application domain, computer science, or statistics.

Progression & Future Opportunities

Graduates of the Data Sciences, B.S. (Information Sciences and Technology) at Pennsylvania State University are prepared to manage and analyze large-scale data and turn complex information into useful insights for industry, government, and academia. The program combines data management, programming, machine learning, analytics, visualization, and statistical reasoning, while its three-option structure allows students to build an applied, computational, or statistical data-science profile.

Typical Job Roles: Data Scientist, Data Analyst, Data Engineer, Business Intelligence Analyst.

Career Development & Industry Connections: Penn State’s College of Information Sciences and Technology provides dedicated career services, required internship experience, employer connections, and recruiting opportunities that can help students transition into data and technology careers:

Career Services: The Office of Career Solutions and Corporate Engagement provides career guidance, professional-development support, employer events, career fairs, and access to Nittany Lion Careers, Penn State’s recruiting platform.
Internships: Most IST undergraduate programs require at least one internship for credit before graduation. At University Park, students can complete a 240-hour internship, either part-time or full-time and either virtually or in person, providing structured practical experience.
Employment & Salary: Penn State IST reports a $83,000 median full-time starting salary and that 88% of graduates have full-time jobs or are pursuing further education or military/service within six months of graduation.
Internship Salary: The College of IST reports an average intern salary of $22.27 per hour, based on its published internship outcomes.
Industry Connections: More than 300 organizations post internship and career opportunities specifically targeting Penn State IST students, while the College’s Recruiting Partners program connects students with employers that actively invest in networking, mentoring, events, and career opportunities.
Industry Employers: Recent IST outcome data identifies employers including Penn State Applied Research Laboratory, Lockheed Martin, EY, PwC, KPMG, Merck, Highmark Health, Erie Insurance, and Deloitte, demonstrating opportunities across technology, consulting, healthcare, insurance, government, and research.
Employer Engagement: Students can connect directly with employers through the annual IST Employer Connection Days, including the IST Career Fair, AI Showcase, Cyber Networking Mixer, and the spring Tech Connect event with IST alumni and industry partners.
Graduation Outcomes: The Data Sciences degree develops graduates who can manage and analyze large-scale, unstructured data and apply data-science methods to problems across industry, government, and academia. Students also complete a Data Sciences Capstone Course (DS 440/DS 440W), providing a culminating application of their data-science knowledge.
Long-Term Accreditation Value: No program-specific professional accreditation is stated on the official Penn State Data Sciences, B.S. program page. Therefore, a specific accreditation benefit should not be claimed for this degree.

Further Academic Progression: After completing the B.S., students can pursue graduate-level study in data science, computer science, statistics, information sciences, artificial intelligence, or related fields, depending on their academic interests and prerequisites. Penn State’s interdisciplinary structure also provides a strong foundation for advanced study because students can graduate with an Applied Data Sciences, Computational Data Sciences, or Statistical Modeling Data Sciences specialization.