B.S. in Computer Science (Data Science Track)

Computer science offers a General track and a Data science track. CSCI 120, 121, 265, 244, 234 and 334 are required for either track. The general track includes additional requirements within computer science as well as MATH 106/166 or MATH 170 as a co-requisite. The data science track includes additional requirements within mathematics as well as courses in applications and ethics.

General Track:

General Track Required Courses

• CSCI 120 Introduction to Computer Science
• CSCI 121 Intermediate Software Development, prerequisite: C- or better in CSCI 120
• CSCI 140 Discrete Structures for Computer Science or MATH 212 Discrete Mathematical Structures and Proof. Students who complete MATH 212 cannot later earn credit for CSCI 140.
• CSCI 265 Database Systems, prerequisite: C- or better in CSCI 120
• CSCI 220.2 Introduction to DevOps, prerequisite: C- or better in CSCI 120
• CSCI 244 Advanced Software Development, prerequisite: C- or better in CSCI 121
• CSCI 234 Introduction to Software Engineering, prerequisite: C- or better in CSCI 244
• CSCI 243.2 Preparing for a Computing Career, prerequisite: CSCI 120 and junior standing
• CSCI 334 System Design and Implementation (WI), prerequisite: CSCI 234

General Track Electives

Students must complete three units of additional elective courses numbered 210-299 or 310-399, with at least two courses numbered 310-399. Students may count at most one unit of internship toward these three units. Students may count one of MATH 230 Mathematical Methods in Operations Research or MATH 258 Numerical Analysis as a 200-level elective in the major.

General Track Corequisites

MATH 170 Calculus 1 (or MATH 106 Analytic Geometry and Calculus I with Review, Part 1 and MATH 166 Analytic Geometry and Calculus I with Review, Part 2)

Data Science Track:

Data Science Track Required Courses

• CSCI 120 Introduction to Computer Science
• CSCI 121 Intermediate Software Development, prerequisite: C- or better in CSCI 120
• CSCI 265 Database Systems, prerequisite: C- or better in CSCI 120
• CSCI 244 Advanced Software Development, prerequisite: C- or better in CSCI 121
• CSCI 234 Introduction to Software Engineering, prerequisite: C- or better in CSCI 244
• CSCI 243.2 Preparing for a Computing Career, prerequisite: CSCI 120 and junior standing
• CSCI 334 System Design and Implementation (WI), prerequisite: CSCI 234
• Math 231 Mathematical Statistics 1
• Math 337 Mathematical Statistics 2

Data Science Corequisites

• MATH 170 Calculus 1 (or MATH 106 Analytic Geometry and Calculus I with Review, Part 1 and MATH 166 Analytic Geometry and Calculus I with Review, Part 2)
• MATH 171 Calculus 2 (or MATH 106 Analytic Geometry and Calculus I with Review, Part 1 and MATH 166 Analytic Geometry and Calculus I with Review, Part 2)

One course in ethics:

• NURS 360 Ethical Dilemmas in Healthcare
• PHIL 222 Ethics
• PHIL 224 Applied Ethics
• PHIL 251 Philosophy of Technology
• PHIL 257 Bio-Ethics and Social Justice
• PHIL 259 Medical Ethics
• PHIL 281 Topics in Ethics
• PHIL 355 Meta-Ethics
• Other ethics courses, to be approved by the student’s advisor, may apply

We recommend that students in this program complete the module on responsible conduct in research in CITI training (which is available free to faculty and staff), and report completion of that training to the advisor. Students could contact the IRB Committee for access to this software.

One course in applications:

• HLTP 230 Epidemiology
• MGMT 311 Marketing Research (prereq: MGMT 251; plus Math 107, Math 231 or ECON156)
• BIOL 363 Genomics (prereqs: BIOL 210 and permission of the instructor)
• ECON 256 Applied Econometrics (prereqs: a course in statistics and ECON 152, which is recommended as the M4 for students in data science)
• Honors, Independent Studies, and other applications courses may apply; consult with the advisor

One additional 300-level course in Mathematics or from the Applications list or one of the following courses from the Master of Science in Predictive Analytics (MS-PA)program:

• MGMT 555, Business Research Methods
• MGMT 556, Decision Analysis

With permission of the program director of the MSDA program, students may enroll in additional courses in the MSDA

Students who complete this program would have “Data Science” listed on their transcript, under “concentration.” They are not eligible for the Informatics minor. Students may, however, count Math 170 and 171, but not Math 231 and 337, towards the math minor, as these latter two courses are required within the major.

The Minor in Computer Science

The minor in computer science consists of CSCI 120, CSCI 121, and three other CSCI course units numbered above 110. One of the following courses may, with departmental consent, be counted toward the computer science minor: MATH 230, MATH 258, MATH 231; PHIL 211. With departmental consent, one course with significant computing content from another program may be counted as one of the three elective course units towards the computer science minor.

The Minor in Informatics

Informatics is the application of computing skills, statistical methods, and domain knowledge to obtain and analyze data in order to make decisions about organizations and society.

The minor in informatics consists of five courses: CSCI 120​; CSCI 265​; one course in statistical reasoning (MATH 107​, HLTP 189​, ECON 156​, or MATH 231​); one course in ethics (NURS 360​, IDIS 215, or a PHIL course with “Ethics” in the title​); and one course in applications (HLTP 230​, MGMT 311​, BIOL 363,​ ECON 256​). Other courses in statistical reasoning, ethics, or applications may be accepted with approval of the program director.

The Interdepartmental Major in Computer Science

The six courses that compose Set I of the interdepartmental major in computer science include CSCI 120, CSCI 121, and four other CSCI courses numbered above 110, at least one of which is expected to be numbered 310-380 or 390-399. The additional courses in computer science and the six courses of Set II are selected by the student with the approval of the advisor.

Upon completion of the computer science major, a successful student will be able to:

1. Implement a solution for a problem using appropriate programming techniques.
   • Skills and Literacies, Knowledge
2. Create a design for a software system using appropriate design principles and patterns.
   • Skills and Literacies, Critical Questioning, Create and Engage
3. Utilize appropriate process and collaborative tools to contribute to a software project.
   • Perspectives, Critical Questioning, Create and Engage
4. Evaluate and learn new technologies independently.
   • Perspectives, Critical Questioning