Undergraduate Program
BA and BS Major Requirements
Below are the requirements for both the BA and BS data science degrees. Students should email gids-undergrad@rochester.edu with any questions.
Students need to have completed or be currently registered to complete the pre-requisite courses before they can declare a data science major. Prerequisite course requirements may be satisfied by AP credit or by testing, according to the standards used by the department that houses the particular course. CSC 171 is satisfied by demonstrating knowledge of Java programming. The below courses cannot be taken Satisfactory/Fail (S/F).
All of the following:
- MTH 150: Discrete Mathematics OR MTH 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)
Plus one of the following sequences:
- MTH 161: Calculus I and MTH 162: Calculus II
- MTH 141: Calculus I, MTH 142: Calculus II, and MTH 143: Calculus III
- MTH 171: Calculus I (Honors) and MTH 172: Calculus II (Honors)
All of the following:
- CSC/DSC 240: Data Mining (fall/spring)
- CSC 242: Introduction to Artificial Intelligence (fall/spring)
- CSC/DSC 261: Database Systems (fall/spring)
- DSC 383W: Data Science Capstone(typically spring of senior year but may be taken as early as spring of junior year)
Plus one of the following:
- MTH 165: Linear Algebra with Differential Equations (fall/spring)
- MTH 173: Calculus III (Honors) (fall)
Plus one of the following:
- DSC/CSC/STT 262: Computational Introduction to Statistics (fall)
- STT 213: Elements of Probability and Mathematical Statistics (fall)
- STT 212: Applied Statistics for the Biological and Physical Sciences I (fall/spring)
Plus one of the following:
- DSC/CSC 265: Intermediate Statistical and Computational Methods (spring)
- Both STT 216: Applied Statistics II and STT 226W: Introduction to Linear Models
Plus one of the following:
- CSC 282: Design and Analysis of Efficient Algorithms (fall)
- DSC 201: Tools for Data Science (fall/spring)
- DSC 275: Time Series Analysis & Forecasting in Data Science (fall)
Student can choose one of the following concentration (application areas):
- Biology
- Biomedical Signals and Imaging
- Brain and cognitive sciences
- Computer science, statistics, and mathematics
- Earth and environmental science
- Physics
- Economics and business
- Political science
Prerequisites for particular concentration (application area) courses beyond data science major prerequisites may be required. Please check the online course description/course schedule (CDCS).
Biology
One or both of the following:
- BIO 110/112: Principles of Biology I
- BIO 111/113: Principles of Biology II
Plus one or two of the following (for a total of three courses):
- BIO 190: Genetics and the Human Genome
- BIO 198: Principles of Genetics
- BIO 205/205W: Evolution
- BIO 206/206W: Eukaryotic Genomes
- BIO 253/253W: Computational Biology
- BIO 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)
Top ↑
Brain and Cognitive Sciences
Any three of the following courses:
- BCS 151: Perception and Action (fall)
- BCS 152: Language and Psycholinguistics (fall)
- BCS 153: Cognition (spring)
- BCS 221: Auditory Perception (spring)
- BCS 229: Computer Models of Human Perception and Cognition (fall)
- BCS 244: Neuroethology (spring)
- BCS 245: Sensory and Motor Neuroscience (spring)
- BCS 248: Neuroeconomics (not offered after Fall 2017)
- BCS 265: Language and the Brain (spring)
- BCS 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 BS:
- 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 (not offered 2018-19)
- 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 (not offered 2018-19)
- DSC 201: Tools for Data Science (fall)
- DSC 210: Digital Imaging: Transforming Real Into Virtual
- DSC 267: Image, Text, and Technology
- DSC 275: Time Series Analysis & Forecasting in Data Science (fall)
- MTH 201: Introduction to Probability (fall/spring)
- MTH 202: Stochastic Processes (spring)
- MTH 203: Introduction to Mathematical Statistics
- MTH 208: Operations Research I (fall)
- MTH 215: Fractal and Chaotic Dynamics (offered every other fall, next offered in Fall 2019)
- MTH 218: Introduction to Mathematical Models in Life Science (spring)
- MTH 230: Number Theory with Applications (fall)
- MTH 233: Introduction to Cryptography (spring)
- STT 221W: Sampling Techniques
Earth and Environmental Science
One or two of the following:
- EES 101: Introduction to Geological Sciences
- EES 103: Introduction to Environmental Science
- EES 105: Introduction to Climate Change
Plus one or two of the following (for a total of three courses):
- EES 211/211W: Geohazards and Their Mitigation: Living on an Active Planet
- EES 212: A Climate Change Perspective to Chemical Oceanography
- EES 218: Atmospheric Geochemistry(Fall)
- EES 233: Marine Ecosystem and Carbon Cycle Modeling (Spring)
- EES 234: Fundamentals of Atmospheric Modeling (Spring)
- EES 235: Physical Oceanography (Fall)
- EES 236: Physics of Climate (Fall)
- EES 251: Introduction to Remote Sensing and Geographic Information Systems
Physics
Any three of the following courses:
- MTH 281: Applied Boundary Value Problems (fall)
- PHY 237: Quantum Mechanics of Physical Systems
- PHY 227: Thermodynamics and Statistical Mechanics
- PHY 235W: Classical Mechanics I
- PHY 373: Physics and Finance (offered every other year, next offering Spring 2020)
Economics and Business
Any three of the following courses:
- ECO 207: Intermediate Microeconomics (fall/spring/summer)
- ECO 209: Intermediate Macroeconomics (fall/spring/summer)
- ECO 214: Economic Theory of Organizations (fall/spring) OR ECO 217/217W: Economics of Organizations (fall)
- ECO 231W: Econometrics (fall/spring)
- ECO 288/288W/PSC 288: Game Theory (fall/spring)
- ACC 201: Financial Accounting (fall/spring)
- CIS 240 Data Management & Descriptive Analytics for Business (fall)
- CIS 242 Predictive Analytics (spring)
- GBA 220 Business Information Systems and Analytics (fall/apring)
- MTH 210: Introduction to Financial Mathematics (fall/spring)
- MKT 203: Principles of Marketing (fall/spring)
Political Science
Any three of the following courses:
- PSC 107 Introduction to Positive Theory (spring)
- PSC 200: Applied Data Analysis (fall/spring/summer)
- PSC 205: Data Analysis II (valid beginning spring 2018)
- PSC 248 Discrimination (fall)
- PSC/IR 270 Mechanisms of International Relations (Not offered next year.)
- PSC 278/IR 278: Foundations of Modern International Politics (spring)
- PSC 281: Formal Models in Political Science (offered every other year, will not be offered in 2018-19)
- PSC 287 Theories of Political Economy (Not offered next year)
- PSC 288/ECO 288/288W: Game Theory (fall/spring)
Only BS students are required to take supplementary courses.
BS students must take both:
- MTH 201: Introduction to Probability (fall/spring)
- MTH 203: Introduction to Mathematical Statistics (spring)
BS students must take one of the following:
- CSC 244: Knowledge Representation and Reasoning in AI (fall)
- CSC 246: Machine Learning (spring)
- CSC 247: Natural Language Processing (fall, not offered 2017-18)
- CSC 248: Statistical Speech and Language Processing (fall, not offered 2018-19)
- 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 (not offered 2017-18 or 2018-19)
- DSC 201: Tools for Data Science (fall)
- DSC 275: Time Series Analysis & Forecasting in Data Science (fall)
Every data science major must complete two upper-level writing experiences. One of the experiences is satisfied by DSC 383W: Data Science Capstone. The other experience can be any of:
- WRT 273: Communicating Your Professional Identity (2 credits), which is typically taken during the 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.
Below is an example of a possible four-year schedule for a data science student pursuing a BS. You can also see this PDF of other possible schedules for BA and BS data science degrees.
First Year Student | Sophomore | ||
Fall | Spring | Fall | Spring |
PREREQUISITE: MTH 161 | PREREQUISITE: MTH 162 | PREREQUISITE: CSC 172 | CORE: CSC 242 |
PREREQUISITE: MTH 150 | PREREQUISITE: CSC 171 | CORE: MTH 165 | CORE: DSC 265 |
Application Area Prerequisite | WRT 105 | CORE: DSC 262 | Cluster or Free Elective |
Cluster or Free Elective | APPLICATION AREA course | Cluster or Free Elective | Cluster or Free Elective |
Junior | Senior | ||
Fall | Spring | Fall | Spring |
CORE: CSC 240 | CORE: CSC 261 | CORE: CSC 282 | CORE: DSC 383W |
BS SUPPLEMENTAL: MTH 201 | BS SUPPLEMENTAL: MTH 203 | BS SUPPLEMENTAL: upper level CSC course | APPLICATION AREA course |
WRT 273 | Cluster or Free Elective | Cluster or Free Elective | Cluster or Free Elective |
APPLICATION AREA course | Cluster or Free Elective | Cluster or Free Elective | Cluster or Free Elective |