BS in Informatics Degree Requirements

Information on requirements for a BS in informatics.

students at tables with laptops

Core Requirements

For students entering Fall 2023 or later; for students who entered prior to Fall 2023, review your course requirements here.

  • INFO 101: Introduction to Informatics*
  • INFO 150: A Mathematical Foundation of Informatics
  • CICS 110: Foundations of Programming
  • CICS 160: Object-Oriented Programming
  • CICS 210: Data Structures
  • INFO 203: Networked World
  • STATS 240 (or equivalent): Statistics
  • INFO 248: Introduction to Data Science
  • CICS 305: Social Issues in Computing (JYW General Education Requirement)
  • COMPSCI 325: Human Computer Interaction
  • Completion of the IE General Education requirement via one of the IE courses available, typically INFO 490PI: Personal Health Informatics

*Credit given for a score of 4 or 5 on the CSP AP exam

Choose a Concentration 

Data Science-Specific Requirements

  • INFO 348: Predictive Analytics in Python
  • COMPSCI 345: Practical Applications of Data Management
  • Data Science Concentration Elective (pick one of the following): STAT 501, STAT 315, OIM 350

Health and Life Sciences-Specific Requirements

  • Pick three from these four options:
    • INFO 324: Intro to Clinical Health Informatics
    • BIO 379H: Genomics and Bioinformatics OR BIOL 597GE Evolutionary Genomics & Bioinformatics (Req Bio 152)
    • PUBHLTH 490Z: Statistical Modeling for Health Data Science OR PUBHLTH 460: Telling Stories with Data
    • CS 328: Mobile Health Sensing and Analytics
  • Complete one of the following Ethics courses: PHIL 160: Intro to Ethics; PHIL 164: Medical Ethics, CS 508: Ethical Considerations in CS (req CICS 305); PUBHLTH 497: Research Ethics; HISTORY 264 History of HealthCare & Medicine in the U.S.

Electives

Choose six courses from the preapproved list below and/or propose your own. Posted prerequisites are waived for informatics majors only for those elective courses that are underlined, though most of these classes do require "Junior" status. If an elective course in the list below is not underlined, review the prerequisites as listed in SPIRE for that individual class.

Proposed electives should be 300-level or higher, be generally available for students to take, and contain a clear connection to Informatics.  Students may propose 600 or higher courses through a petition as a one-time exception. Proposed electives need not be from course offerings in CICS but students should expect to provide a syllabus and a rationale for the elective they want to propose. Students will be expected to fulfill any posted pre-requisite requirements for any class proposed.

  • BIOL 379H: Genomics and Bioinformatics (HLS)
  • BIOL 383H: Gene and Genome Analysis (HLS)
  • BIOL 478: Human Genome Analysis (formerly BIOL 497G) (HLS)
  • BIOL 479: Genomics and Data Science (formerly BIOL 497D) (HLS)
  • BIOL 597GE: Evolutionary Genomics & Bioinformatics (HLS)
  • BIOSTATS 535: Data Handling and Analysis Using SAS
  • BIOSTATS 683: Introduction to Causal Inference in a Big Data World (HLS)
  • BIOSTATS 690T: Applied Statistical Genetics (HLS)
  • BIOSTATS 690TO: Topics in Biostatistics and Data Science (HLS)
  • CLASSICS 390STA: Visualizing Archaeological Data
  • CLASSICS 396A-IS: Poggio Civitate Field School
  • COMM 408: Survey of Digital Behavioral Data (formerly COMM 497DB)
  • COMM 540: Internet Governance & Information Policy (formerly COMM 497GP)
  • COMPSCI 320: Software Engineering
  • COMPSCI 326: Web Programming
  • COMPSCI 328: Mobile Health Sensing and Analytics (HLS)
  • COMPSCI 365: Digital Forensics
  • COMPSCI 383: Artificial Intelligence 
  • COMPSCI 389: Introduction to Machine Learning
  • COMPSCI 420: Software Entrepreneurship
  • COMPSCI 426: Scalable Web Systems (formerly COMPSCI 490STA/497S)
  • COMPSCI 490U: Introduction to UX Research
  • COMPSCI 508: Ethical Considerations in CS
  • COMPSCI 571: Data Visualization and Exploration
  • ECE 579: Math Tools for Data Science and Machine Learning
  • ECON 309: Game Theory
  • ECON 337: Economics in the Age of Big Data
  • ECON 452: Econometrics
  • ENGLISH 379: Prof. Writing courses
  • ENGLISH 391C: Web Design (email instructor to join waiting list)
  • ENGLISH 491DS: Seminar – Data Science for the Humanities
  • GEOGRAPH 493W/NRC 597GW: Seminar - Web GIF
  • INFO 324: Introduction to Clinical Health Informatics (HLS)
  • INFO 348: Data Analytics with Python
  • INFO 390C: Introduction to Computational Biology and Bioinformatics
  • INFO 490C: Introduction to Social and Cultural Analytics
  • INFO 490PI: Personal Health Informatics
  • LEGAL 342: Machine Bias and Law
  • MARKET 413: Social Media and Marketing Analytics
  • MARKET 455: Internet Marketing
  • MATH 456: Mathematical Modeling
  • MATH 551: Introduction to Scientific Computing
  • MATH 605: Probability Theory I
  • MI-ENG 272: Fundamentals of Data Visualization (formerly MI-ENG 397DH)
  • NRC 585: Intro to Geographic Information Systems
  • OIM 350: Business Intelligence and Analytics
  • OIM 454: Advanced Business Analytics
  • PUBLHLTH 413: Introduction to Epidemiologic Management and Analysis (formerly PUBHLTH 490KR) (HLS)
  • PUBHLTH 460: Telling Stories with Data (HLS)
  • PUBHLTH 490Z: Statistical Modeling for Health Data Science (HLS) 
  • PUBHLTH497R: Research Ethics (formerly PUBHLTH 497) (HLS)
  • SOC 313: Survey Design and Analysis (HLS)
  • SOC 351: Social Network Analysis
  • STATISTC 315: Statistics I (formerly STATISTC 515)
  • STATISTC 501: Methods of Applied Stats
  • STATISTC 516: Statistics II
  • STAT 525: Regression and Analysis of Variants
  • STAT 526: Design of Experiments

Please note: no single course can be used to fulfill two different major requirements. For example, COMPSCI 345 cannot be used to fulfill the Data Science concentration requirement AND count as an elective requirement for Data Science concentration students.