# 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.

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).

One of the following:

- MTH 150: Discrete Mathematics
- MTH 150A: Discrete Math Module

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)

Plus both of the following:

- CSC 171: The Science of Programming
- CSC 172: The Science of Data Structures

One of the following:

- MTH 165: Linear Algebra with Differential Equations
- MTH 173: Calculus III (Honors)

Plus one of the following:

- DSC/CSC 262: Computational Introduction to Statistics
- STT 213: Elements of Probability and Mathematical Statistics
- STT 212: Applied Statistics for the Biological and Physical Sciences I

Plus one of the following:

- DSC/CSC 265: Intermediate Statistical and Computational Methods
**Both**STT 216: Applied Statistics II**and**STT 226W: Introduction to Linear Models

Plus **all** of the following:

- CSC 240: Data Mining
- CSC 242: Introduction to Artificial Intelligence
- CSC 261: Database Systems
- CSC 282: Design and Analysis of Efficient Algorithms
- DSC 383W: Data Science Capstone (fall semester of senior year)

**Only BS students are required to take supplementary courses.**

BS students must take both:

- MTH 201: Introduction to Probability
- MTH 203: Introduction to Mathematical Statistics

BS students must take one of the following:

- CSC 244: Logical Foundations of A.I.
- CSC 246: Machine Learning
- CSC 247: Natural Language Processing
- CSC 248: Statistical Speech and Language Processing
- CSC 249: Machine Vision
- CSC 252: Computer Organization
- CSC 298: Deep Learning and Graphical Models

Student can choose one of the following application areas:

- Biology
- Brain and cognitive sciences
- Computer science, statistics, and mathematics
- Earth and environmental science
- Physics
- Economics and business
- Political science

Prerequisites for particular 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

#### Brain and Cognitive Sciences

Any three of the following courses:

- BCS 151: Perception and Action
- BCS 152: Language and Psycholinguistics
- BCS 153: Cognition
- BCS 221: Auditory Perception
- BCS 229: Computer Models of Human Perception and Cognition
- OPT 248/BCS 223: Vision and the Eye
- BCS 244: Neuroethology
- BCS 245: Sensory and Motor Neuroscience
- BCS 248: Neuroeconomics
- BCS 265: Language and the Brain

#### 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
- CSC 246: Machine Learning
- CSC 247: Natural Language Processing
- CSC 248: Statistical Speech and Language Processing
- CSC 249: Machine Vision
- CSC 254: Programming Language and Design Implementation
- CSC 252: Computer Organization
- CSC 253: Dynamic Language and Software Development
- CSC 256: Operating Systems
- CSC 258: Parallel and Distributed Systems
- CSC 280: Computer Models and Limitations
- CSC 298: Deep Learning and Graphical Models
- DSC 210: Digital Imaging: Transforming Real Into Virtual
- DSC 267: Image, Text, and Technology
- ECE 206: GPU Parallel C/C++ Programming
- MTH 201: Introduction to Probability
- MTH 202: Stochastic Processes
- MTH 203: Introduction to Mathematical Statistics
- MTH 208: Operations Research I
- MTH 215: Fractal and Chaotic Dynamics
- MTH 218: Introduction to Mathematical Models in Life Science
- MTH 230: Number Theory with Applications
- MTH 233: Introduction to Cryptography
- 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 251: Introduction to Remote Sensing and Geographic Information Systems

#### Physics

Any three of the following courses:

- MTH 281: Applied Boundary Value Problems
- PHY 237: Quantum Mechanics of Physical Systems
- PHY 227: Thermodynamics and Statistical Mechanics
- PHY 235W: Classical Mechanics I
- PHY 373: Physics and Finance

#### Economics and Business

Any three of the following courses:

- ECO 207: Intermediate Microeconomics
- ECO 209: Intermediate Macroeconomics
- ECO 214: Economic Theory of Organizations
**OR**ECO 217/217W: Economics of Organizations - ECO 231W: Econometrics
- ECO 288/288W/PSC 288: Game Theory
- ACC 201: Financial Accounting
- MTH 210: Introduction to Financial Mathematics
- MKT 203/203W: Principles of Marketing

#### Political Science

Any three of the following courses:

- PSC 200: Applied Data Analysis
- PSC 203: Survey Research Methods
- PSC 235: Organizational Behavior
- PSC 278/IR 278: Foundations of Modern International Politics
- PSC 281: Formal Models in Political Science
- PSC 288/ECO 288/288W: Game Theory

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.