BS in Informatics Degree Requirements

Information on requirements for a BS in Informatics.

students at tables with laptops

The Informatics degree at the Manning College of Information and Computer Sciences provides students with a solid foundation in information and data sciences, with an emphasis on interdisciplinary applications. The program focuses on building a strong theoretical base in computational thinking and analysis, practical tools for applying these concepts across diverse fields, and data-driven communication skills for effective information dissemination. 

Students choose a concentration, allowing them to specialize in one of two areas of faculty expertise and distinction within the College: 1) Data Science and 2) Health and Life Sciences. The Data Science concentration offers more in-depth education in data analysis, management, and the statistical foundations of data science. The Health and Life Sciences concentration enables students to develop expertise in more domain-specific data analytics, focusing on clinical healthcare, personal healthcare, biology, and/or bioinformatics. 

Informatics Degree Requirements

The degree requirements are organized into nine Core Requirement courses, three Concentration Requirement courses, and six Elective Requirement courses. The Core Requirements deliver foundational training in mathematical, statistical, and computational principles, along with practical tools for applying these theories. The Concentration Requirements provide specialized education in two data-centric areas: Data Science and Health & Life Sciences. Lastly, the Elective Requirements offer students the opportunity to explore and gain in-depth knowledge in specific data-intensive disciplines of their interests.

Informatics Degree Tracking Forms

To be used by Informatics students pursuing the BS to track progress. Please note that tracking forms are informal descriptions of the programs that are for guidance only. See Academic Requirements Report (ARR) on SPIRE.

Science Credits Requirement

Students pursuing a Bachelor of Science (BS) in CICS are also required to complete at least 60 credits of science coursework, as outlined below. Credits earned to satisfy general education and major requirements count toward this requirement. Consequently, Informatics students should carefully plan their coursework to ensure they meet both the Informatics Degree Requirements and the CICS Science Credit Requirements.

Approved Science Coursework

  • College of Information and Computer Sciences: All courses
  • College of Natural Sciences (CNS): All courses
  • 5-Colleges: All CICS and CNS comparable 5-college courses
  • Select courses from other colleges: Please see the listing at CNS for specifically approved courses in other colleges (CICS approvals parallel those of CNS at this time).

Core Requirements

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

The Core Requirements are comprised of two introductory courses on “big ideas” and mathematical foundations, along with nine core courses across four broad areas: 1) human factors and societal aspects (two courses), 2) courses on statistics and data analysis (three courses), problem-solving and programming (two courses), Junior Year Writing and Integrative Experience (IE) requirements (two courses). 

  • 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
  • STATISTC 240 (or COMPSCI 240, PSYCH 240, OIM 240, RES-ECON 212, SOCIOL 212, or STATISTC 315 (formerly STATISTC 515)
  • 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 CS-P AP exam

+Credit given for a score of 4 or 5 on the CS-A AP exam

Concentration Requirements

Students choose a concentration, allowing them to specialize in one of two areas of faculty expertise and distinction within the College: 1) Data Science and 2) Health and Life Sciences. 

Specific Requirements for Data Science (DS) Concentration

The Data Science concentration offers more in-depth education in data analysis, management, and the statistical foundations of data science. 

  • 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, or OIM 350

Specific Requirements for Health and Life Sciences (HLS) Concentration

The Health and Life Sciences concentration enables students to develop expertise in more domain-specific data analytics, focusing on clinical, personal healthcare, and biological datasets.

  • Pick three of the following courses:
    • INFO 324: Intro to Clinical Health Informatics
    • INFO 390C: Introduction to Computational Biology and Bioinformatics OR BIOL 379H: Genomics and Bioinformatics OR BIOL 476 Evolutionary Genomics & Bioinformatics (Req BIOL 152)
      • INFO 390C is a new addition as of Fall 2024. Pending approval by the Faculty Senate. May require an Academic Requirements Report (ARR) fix while the curriculum change is in progress.
    • PUBHLTH 490Z: Statistical Modeling for Health Data Science OR PUBHLTH 460: Telling Stories with Data
    • COMPSCI 328: Mobile Health Sensing and Analytics
  • Complete one of the following Ethics courses:
    • PHIL 160: Intro to Ethics
    • PHIL 164: Medical Ethics
    • COMPSCI 508: Ethical Considerations in CS (req CICS 305)
    • PUBHLTH 497: Research Ethics
    • HISTORY 264 History of Healthcare & Medicine in the U.S.

Elective Requirements

The Elective Requirements provide students with an educational opportunity to understand and apply the learned information and data science principles and tools to a particular domain of data-intensive. Students choose six courses from the approved list below and/or propose their own (see below for details). 

Approved Electives

Click here for Spring 2025 Informatics Electives

These courses are pre-approved electives that count toward the Elective Requirements. 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. Courses marked with HLS in parentheses are particularly appropriate for HLS students, but all Informatics students who meet the eligibility requirements may count them as electives.

  • BIOL 379H: Genomics and Bioinformatics (HLS)
  • BIOL 383H: Gene and Genome Analysis (HLS)
  • BIOL 476: Evolutionary Genomics & Bioinformatics (formerly BIOL 597GE) (HLS)
  • BIOL 478: Human Genome Analysis (formerly BIOL 497G) (HLS)
  • BIOL 479: Genomics and Data Science (formerly BIOL 497D) (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)
  • CICS 590P/SPP 590P: Technology Policy and Innovation to Serve the Common Good
    • New addition as of Fall 2024. Pending approval by the Faculty Senate. May require an Academic Requirements Report (ARR) fix while the curriculum change is in progress.
  • 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 the waiting list)
  • ENGLISH 491DS: Seminar – Data Science for the Humanities
  • GEOGRAPH 493W/NRC 588 (formerly NRC 597GW) Seminar - Web GIS 
  • GEOGRAPH 484: Geographic Computation (formerly GEOGRAPH 497A)
  • INFO 324: Introduction to Clinical Health Informatics (HLS)
  • INFO 348: Data Analytics with Python
  • INFO 390C: Introduction to Computational Biology and Bioinformatics (HLS)
    • New addition as of Fall 2024. Pending approval by the Faculty Senate. May require an Academic Requirements Report (ARR) fix while the curriculum change is in progress.
  • INFO 490C: Introduction to Social and Cultural Analytics
  • INFO 490PI: Personal Health Informatics (HLS)
  • 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
  • NRC 585: Intro to Geographic Information Systems
  • OIM 350: Business Intelligence and Analytics
  • OIM 454: Advanced Business Analytics
  • PUBHLTH 345: Public Health Data Science: Data Exploration and Visualization
    • New addition as of Fall 2024. Pending approval by the Faculty Senate. May require an Academic Requirements Report (ARR) fix while the curriculum change is in progress.
  • 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)
  • SOCIOL 313: Survey Design and Analysis (HLS)
  • SOCIOL 351: Social Network Analysis
  • STATISTC 315: Statistics I (formerly STATISTC 515)
  • STATISTC 501: Methods of Applied Stats
  • STATISTC 516: Statistics II or STATISTC 490S: Mathematical Foundations of Statistics and Data Science
    • STATISTC 490S is a new addition as of Fall 2024. Pending approval by the Faculty Senate. May require an Academic Requirements Report (ARR) fix while the curriculum change is in progress.
  • STATISTC 525: Regression and Analysis of Variants
  • STATISTC 526: Design of Experiments
  • STATISTC 535: Statistical Computing
    • New addition as of Fall 2024. Pending approval by the Faculty Senate. May require an Academic Requirements Report (ARR) fix while the curriculum change is in progress.

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.

Proposing Your Own Electives

Proposed electives should be at the 300-level or higher. Students may petition to take 600-level or higher courses as a one-time exception. The content of the proposed course must not significantly overlap with degree-required courses or approved electives. Electives do not need to be from the course offerings within CICS. Students are expected to meet any posted prerequisite requirements for any proposed class.

The course must involve computational principles and technologies relevant to information and data science, either to: 1) further develop theoretical foundations in these fields, or 2) apply learned principles to understand domain-specific vocabulary, cultures, and problems. In other words, students may either deepen their theoretical knowledge of information science or apply its foundations in other disciplines.

  • Computational principles represent the fundamental concepts and techniques used in computer and information sciences in order to solve problems and perform tasks through computation. These principles include but are not limited to 1) algorithms, 2) data structure, 3) theories of computer science, mathematics, and statistics, 4) computational and numerical models, and 5) machine learning and artificial intelligence. 

To propose an elective, it is highly recommended that students first consult with their academic advisor. Afterward, students should email informatics [at] cics [dot] umass [dot] edu with a brief essay (less than 500 words) describing: 1) how the course addresses computational principles related to information and data science, and 2) how the proposed elective aligns with their current curriculum and educational goals within the Informatics degree program. The syllabus of the proposed course must also be attached to the email.