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Undergraduate Program

BA and BS Major Requirements

Listed below are the requirements for the data science Bachelor of Arts (BA) and Bachelor of Science (BS) degrees.

Questions? Send an email to gids-undergrad@rochester.edu.

Prerequisite Courses

Students must complete or be registered to complete the following pre-requisite courses with before declaring a data science major:

  • MATH 150: Discrete Mathematics OR MATH 150A: Discrete Math Module
  • CSC 171: Intro to Computer Science (formerly The Science of Programming)   
  • CSC 172: Data Structures and Algorithms (formerly The Science of Data Structures)

Students must also complete ONE of the following sequences:

  • MATH 161: Calculus I and MATH 162: Calculus II
  • MATH 141: Calculus I, MATH 142: Calculus II, and MATH 143: Calculus III
  • MATH 171: Calculus I (Honors) and MATH 172: Calculus II (Honors)

Prerequisite courses cannot be taken Satisfactory/Fail (S/F).

Prerequisite course requirements may be satisfied by AP credit or by testing, according to the standards of the department housing the particular course. For example, CSC 171 is satisfied by demonstrating knowledge of Java programming.

Core Courses

ALL OF THE FOLLOWING:

  • CSC 242: Introduction to Artificial Intelligence (fall/spring)
  • DSCC/CSC 240: Data Mining (fall/spring)
  • DSCC/CSC 261: Introduction to Databases (formerly Database Systems) (fall/spring)
  • DSCC 383W: Data Science Capstone (typically taken spring of senior year but may be taken as early as spring of junior year)

Plus ONE of the following:

  • MATH 165: Linear Algebra with Differential Equations (fall/spring)
  • MATH 173: Calculus III (Honors) (fall)

Plus ONE of the following:

  • DSCC/CSC/STAT 262: Computational Introduction to Statistics (fall)
  • STAT 213: Elements of Probability and Mathematical Statistics (fall)
  • STAT 212: Applied Statistics for the Biological and Physical Sciences I (fall/spring)

Plus ONE of the following:

  • DSCC/CSC 265: Intermediate Statistical and Computational Methods (spring)
  • Both STAT 216: Applied Statistics II and STAT 226W: Introduction to Linear Models

Plus ONE of the following:

  • CSC 282: Design and Analysis of Efficient Algorithms (fall)
  • DSCC 201: Tools for Data Science (fall/spring)
  • DSCC 275: Time Series Analysis & Forecasting in Data Science (fall)
Concentration (Application Area) Courses

Students can choose from one of the following concentrations (application areas):

Each concentration requires students to take three courses.

Individual concentration (application area) courses may require prerequisites beyond the data science major prerequisites. Please check the online course description/course schedule (CDCS) prior to registering for courses.

Biology

ONE or BOTH of the following:

  • BIOL 110/112: Principles of Biology I
  • BIOL 111/113: Principles of Biology II

Plus ONE or TWO of the following (for a total of three courses):

  • BIOL 190: Genetics and the Human Genome
  • BIOL 198: Principles of Genetics
  • BIOL 205/205W: Evolution
  • BIOL 206/206W: Eukaryotic Genomes
  • BIOL 253/253W: Computational Biology
  • BIOL 265/265W: Molecular Evolution

Biomedical Signals and Imaging

BOTH of the following:

  • BME 101: Introduction to Biomedical Engineering (fall)
  • BME 210: Biomedical Circuits (spring)

Plus ONE of the following (for a total of three courses):

  • BME 230: Biomedical Signals and Systems (fall)
  • BME 253: Ultrasound Imaging (fall)
  • BME 274: Biomedical Sensors (spring)
  • CSC 249: Machine Vision (spring)

Brain and Cognitive Sciences

Any THREE of the following courses:

  • BCSC 151: Perception and Action (fall)
  • BCSC 152: Language and Psycholinguistics (fall)
  • BCSC 153: Cognition (spring)
  • BCSC 221: Auditory Perception (spring)
  • BCSC 229: Computer Models of Human Perception and Cognition (fall)
  • BCSC 244: Neuroethology (spring)
  • BCSC 245: Sensory and Motor Neuroscience (spring)
  • BCSC 265: Language and the Brain (spring)
  • BCSC 247, Topics in Computational Neuroscience

Computer Science, Statistics, and Mathematics

Any THREE of the following courses, not including courses taken to fulfill the supplementary course requirement for the data science BS degree:

  • CSC 229: Computer Models of Human Perception and Cognition (fall)
  • CSC 246: Machine Learning (spring)
  • CSC 247: Natural Language Processing (fall)
  • CSC 248: Statistical Speech and Language Processing
  • CSC 249: Machine Vision (spring)
  • CSC 254: Programming Language and Design Implementation (fall)
  • CSC 252: Computer Organization (spring)
  • CSC 253: Dynamic Language and Software Development (fall)
  • CSC 256: Operating Systems (fall)
  • CSC 258: Parallel and Distributed Systems
  • CSC 280: Computer Models and Limitations (spring)
  • CSC 282: Design and Analysis of Efficient Algorithms (fall)
  • CSC 298: Deep Learning and Graphical Models
  • DSCC 201: Tools for Data Science (fall)
  • DSCC 210: Digital Imaging: Transforming Real Into Virtual
  • DSCC 267: Image, Text, and Technology
  • DSCC 275: Time Series Analysis & Forecasting in Data Science (fall)
  • MATH 201: Introduction to Probability (fall/spring)
  • MATH 202: Stochastic Processes (spring)
  • MATH 203: Introduction to Mathematical Statistics
  • MATH 208: Operations Research I (fall)
  • MATH 215: Fractal and Chaotic Dynamics (offered every other fall, next offered in Fall 2021)
  • MATH 218: Introduction to Mathematical Models in Life Science (spring)
  • MATH 230: Number Theory with Applications (fall)
  • MATH 233: Introduction to Cryptography (spring)
  • STAT 221W: Sampling Techniques

Earth and Environmental Science

ONE or TWO of the following:

  • EESC 101: Introduction to Geological Sciences
  • EESC 103: Introduction to Environmental Science
  • EESC 105: Introduction to Climate Change

Plus ONE or TWO of the following (for a total of three courses):

  • EESC 211/211W: Geohazards and Their Mitigation: Living on an Active Planet
  • EESC 212: A Climate Change Perspective to Chemical Oceanography
  • EESC 218: Atmospheric Geochemistry (fall)
  • EESC 233: Marine Ecosystem and Carbon Cycle Modeling (spring)
  • EESC 234: Fundamentals of Atmospheric Modeling (spring)
  • EESC 235: Physical Oceanography (fall)
  • EESC 236: Physics of Climate (fall)
  • EESC 251: Introduction to Remote Sensing and Geographic Information Systems

Linguistics

Required:

  • LING 110: Introduction to Linguistic Analysis

And ONE of the following:

  • LING 210: Introduction to Language Sound Systems
  • LING 220: Introduction to Grammatical Systems
  • LING 224: Introduction to Computational Linguistics
  • LING 225: Introduction to Semantic Analysis

Plus ONE of the following:

  • LING 247/CSC 247: Natural Language Processing
  • LING 248/CSC 248: Statistical Speech and Language Processing
  • LING 250: Data Science for Linguistics
  • LING 268: Computational Semantics
  • LING 281 Statistical and Neural Computational Linguistics

Physics

Any THREE of the following courses:

  • MATH 281: Applied Boundary Value Problems (fall)
  • PHYS 237: Quantum Mechanics of Physical Systems
  • PHYS 227: Thermodynamics and Statistical Mechanics
  • PHYS 235W: Classical Mechanics I
  • PHYS 373: Physics and Finance (offered every other year, next offering Spring 2022)

Economics and Business

Any THREE of the following courses:

  • ECON 207: Intermediate Microeconomics (fall/spring/summer)
  • ECON 209: Intermediate Macroeconomics (fall/spring/summer)
  • ECON 214: Economic Theory of Organizations (fall/spring) OR ECON 217/217W: Economics of Organizations (fall)
  • ECON 231W: Econometrics (fall/spring)
  • ECON 288/288W/PSCI 288: Game Theory (fall/spring)
  • ACC 201: Financial Accounting (fall/spring)
  • CIS 220 Business Information Systems and Analytics (fall/spring) (formerly GBA 220)
  • CIS 240 Data Management & Descriptive Analytics for Business (fall)
  • CIS 242 Predictive Analytics (spring)
  • MATH 210: Introduction to Financial Mathematics (fall/spring)
  • MKT 203: Principles of Marketing (fall/spring)

Political Science

Any THREE of the following courses:

  • PSCI 107 Introduction to Positive Theory (spring)
  • PSCI 200: Applied Data Analysis (fall/spring/summer)
  • PSCI 205: Data Analysis II
  • PSCI 248 Discrimination (fall)
  • PSCI/INTR 270 Mechanisms of International Relations
  • PSCI 278/INTR 278: Foundations of Modern International Politics (spring)
  • PSCI 281: Formal Models in Political Science (offered every other year, will not be offered in 2021-22)
  • PSCI 287 Theories of Political Economy
  • PSCI 288/ECON 288/288W: Game Theory (fall/spring)

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Supplementary Courses (Only Required for BS)

Only Bachelor of Science (BS) students are required to take supplementary courses.

BS students must take BOTH:

  • MATH 201: Introduction to Probability (fall/spring)
  • MATH 203: Introduction to Mathematical Statistics (spring)

Plus ONE of the following:

  • CSC 244: Knowledge Representation and Reasoning in AI (fall)
  • CSC 246: Machine Learning (spring)
  • CSC 247: Natural Language Processing (fall)
  • CSC 248: Statistical Speech and Language Processing (fall)
  • CSC 249: Machine Vision (spring)
  • CSC 252: Computer Organization (spring)
  • CSC 282: Design and Analysis of Efficient Algorithms (fall)
  • CSC 298: Deep Learning and Graphical Models
  • DSCC 201: Tools for Data Science (fall)
  • DSCC 275: Time Series Analysis & Forecasting in Data Science (fall)
Clusters and Course Overlaps
Data science is a natural science major. To fulfill University of Rochester degree requirements, students in data science are required to complete a humanities cluster and a social science cluster. Only ONE course may serve as both an application area course and as part of a cluster. To learn more about the University cluster requirement, please visit the College Center for Advising Services. Explore cluster options via the Cluster Search Engine.
A major or a minor in the humanities or social sciences may be used in place of a cluster to fulfill University requirements. No more than three courses in a major or two courses in a minor can overlap with the data science major. Consult the University's overlap policy and your academic advisor to prepare for planned overlaps.
Upper-Level Writing

Every data science major is required to complete TWO upper-level writing experiences. One experience can be satisfied by taking DSCC 383W: Data Science Capstone. The other experience can be any of the following:

  • WRTG 273: Communicating Your Professional Identity (2 credits), which is typically taken during sophomore or junior year.
  • "W" courses in other departments.
  • Creation of a research paper or published technical report as part of an independent study, with advisor approval.
Sample Schedule

Included below is an example of a possible four-year schedule for a data science student pursuing a Bachelor of Science (BS). Sample BA and alternative BS sample schedules are available via PDF.

FRESHMANSOPHOMOREJUNIORSENIOR
Fall
  • PREREQUISITE: MATH 161
  • PREREQUISITE: MATH 150 
  • Application Area Prerequisite
  • Cluster or Free Elective


  • PREREQUISITE: CSC 172
  • CORE: MATH 165
  • CORE: DSCC 262
  • Cluster or Free Elective



  • CORE: DSCC 240
  • BS SUPPLEMENTAL: MATH 201
  • WRTG 273
  • Application Area Course



  • CORE: DSCC 475
  • BS SUPPLEMENTAL: upper level CSC course
  • Cluster or Free Elective
  • Cluster or Free Elective
Spring
  • PREREQUISITE: MATH 162
  • PREREQUISITE: CSC 171
  • WRTG 105
  • Application Area Course


  • CORE: CSC 242
  • CORE: DSCC 265
  • Cluster or Free Elective
  • Cluster or Free Elective



  • CORE: DSCC 261
  • BS SUPPLEMENTAL: MATH 203
  • Cluster or Free Elective
  • Cluster or Free Elective
  • CORE: DSCC 383W
  • Application Area Course
  • Cluster or Free Elective
  • Cluster or Free Elective