Bachelor of Arts Information Science, Systems, and Technology

4 Years On Campus Bachelors Program

Cornell University

Program Overview

The Bachelor of Science in Information Science, Systems, and Technology (ISST) at Cornell University combines engineering, computing, data analysis, and an understanding of how people and organisations use technology. Designed for students interested in developing and managing complex digital systems, the program builds skills in programming, machine learning, statistical modelling, information ethics, and technology-driven problem-solving, preparing graduates to apply technical knowledge across industries such as technology, finance, consulting, and business.

Curriculum Structure

The curriculum combines core information science courses, engineering requirements, electives, and concentrations. The following outlines the typical academic progression; exact course timing can vary according to prerequisites, engineering requirements, and the student's chosen concentration.

Year 1 – Building the Foundations

Students establish a strong foundation in mathematics, computing, science, and engineering while beginning to explore information science. Courses such as Calculus for Engineers, Introduction to Computer Science, and introductory engineering courses develop analytical reasoning, programming fundamentals, and structured problem-solving skills.

Year 2 – Developing Technical and Analytical Skills

As students progress through the engineering curriculum and prepare to affiliate with the major, they develop a deeper understanding of computing, mathematical modelling, and information systems. Relevant courses include Introductory Design and Programming for the Web (INFO 1300), Information Ethics, Law, and Policy (INFO 1200) or Choices and Consequences in Computing (INFO 1260), and Engineering Probability and Statistics: Modeling and Data Science II (ORIE 3500), subject to prerequisites and the student's study plan.

Year 3 – Applying Information Science to Complex Problems

Students deepen their technical expertise through courses such as Practical Tools for Operations Research, Machine Learning and Data Science (ORIE 3120), Networks (INFO 2850), and approved courses in data science and machine learning. They also begin focusing on their selected concentration, developing the ability to analyse complex datasets, understand networked systems, or design interactive digital technologies.

Year 4 – Specialisation and Professional Preparation

In their final year, students complete remaining major requirements, concentration courses, and approved electives while refining their professional interests. Depending on their chosen pathway, they can explore areas such as Visual Data Analytics for the Web (INFO 3300), interactive technologies, user experience, or networks and markets, while using academic advising and career services to prepare for employment or postgraduate study.

Focus Areas

Information systems engineering, data science and machine learning, web programming, statistical modelling, network analysis, interactive technologies, user experience, information ethics and policy, technology management, digital systems design.

Learning Outcomes

Graduates develop the ability to solve data- and technology-centred problems by combining computing, statistical reasoning, and social and organisational analysis. They learn to design, implement, and evaluate digital systems, algorithms, models, visualisations, and interactive applications; communicate technical findings effectively; and assess the ethical, social, and professional implications of technology.

Professional Alignment (Accreditation)

The degree is an engineering-based undergraduate program offered through Cornell's College of Engineering. Its curriculum integrates engineering design, computation, statistics, information systems, and the social and organisational contexts in which digital technologies operate. The official program information does not establish a specific professional accreditation for this degree, so no separate accreditation claim is made here.

Reputation (Employability Outcomes)

Cornell's Information Science department reports the following outcomes for the Class of 2025, six months after graduation, across its Information Science degree pathways:

91% were employed full-time or enrolled in graduate school.
$100,000 was the reported mean starting salary.
33% were employed in the technology sector, followed by 32% in financial services and 15% in consulting.
97% were employed in the United States.

These are department-wide Information Science outcomes, not figures reported exclusively for ISST graduates. They nevertheless provide useful context on the career opportunities associated with Cornell's Information Science programs.

Experiential Learning (Research, Projects, Internships etc.)

At Cornell Bowers, students can strengthen their classroom learning through research, technical coursework, and opportunities to explore real-world information systems. The ISST curriculum develops practical capabilities in web programming, machine learning, data analysis, and network modelling. Students can also explore faculty-led research and connect with the university's wider engineering community to develop their technical and professional skills.

Key opportunities and resources include:

Web Development and Programming: INFO 1300, Introductory Design and Programming for the Web, develops skills in creating web-based systems and applying programming concepts.
Data Science and Machine Learning: ORIE 3120, Practical Tools for Operations Research, Machine Learning and Data Science, introduces practical analytical methods for solving data-intensive problems.
Research Opportunities: The Bowers Undergraduate Research Experience (BURE) connects eligible Cornell Bowers undergraduates with faculty mentors for paid research experiences. This can help students develop research skills and explore specialised topics.
Faculty-Guided Projects: Students can explore undergraduate research opportunities and discuss independent research with faculty members, subject to the relevant course and departmental requirements.
Engineering and Computing Resources: Cornell Engineering provides an interdisciplinary learning environment in which students can develop technical expertise across computing, mathematics, statistics, and engineering.
Internships and Employer Connections: Cornell's Handshake platform and engineering career resources help students find internships, campus recruitment opportunities, and employer contacts.
Career Preparation: Engineering career advising and Cornell Career Network services offer support with job searches, interview preparation, career exploration, and applications for graduate study.

Progression & Future Opportunities

The ISST degree prepares students to work at the intersection of computing, data, engineering, and organisational problem-solving. Graduates can pursue technical and analytical roles across technology companies, financial services, consulting, and other sectors that rely on digital systems and data-driven decision-making. Depending on their coursework, concentration, projects, and experience, potential career paths include:

Data analyst, data scientist, software developer, machine learning engineer, systems analyst, business intelligence analyst, UX designer, technology consultant, data engineer, information systems analyst, product analyst, IT project manager.

Career Development and Graduate Opportunities

Dedicated Career Support: Cornell Career Network offers career exploration, résumé assistance, graduate-school advising, internship and full-time job searches, and access to recruiting resources.
Internship and Employer Access: Cornell Engineering students can use Handshake, campus recruiting activities, employer websites, and networking opportunities to pursue relevant positions.
Employment Outcomes: Cornell Bowers reports that 91% of its Information Science graduates in the Class of 2025 were employed full-time or enrolled in graduate school six months after graduation. The reported mean starting salary was $100,000 across the department's Information Science degree pathways; this is not an ISST-only salary figure.
Industry Relevance: The degree's emphasis on programming, data science, machine learning, networks, and information systems aligns with skills used in technology, finance, and consulting. Cornell Bowers also maintains research resources for collaboration with industry and corporate partners; individual employment or partnership outcomes are not guaranteed.
Professional Recognition: Students gain an engineering-oriented education in information systems, with technical and interdisciplinary preparation that can support further professional development. No specific professional accreditation has been confirmed for this degree.
Further Career Development: Students can build their professional profiles through research, relevant internships, student organisations, technical coursework, and collaboration with faculty.

Further Academic Progression: After completing the bachelor's degree, graduates may consider postgraduate study in information science, computer science, data science, machine learning, human-computer interaction, information systems, or a related engineering discipline. Cornell offers postgraduate options such as the Master of Professional Studies in Information Science and the Ph.D. in Information Science. Admission depends on each program's entry requirements and the applicant's academic background.