The major combines computer science, statistics, and a student's choice of advanced course work in any one of a number of different application areas of data science, including business, biology, earth and environmental science, political science and others. BS students must take three additional courses listed below in the supplementary course section. Each graduate will receive a degree conferred by the University's School of Arts & Sciences.
The major consists of:
 Prerequisite courses, which must be completed before declaring the major.
 Required core courses in Mathematics, Computer Science, and Statistics.
 Supplementary courses, which are required for BS students.
 Three upperlevel courses in an application area.
In order to plan and declare your major, please contact Michelle Vogl for an appointment <michelle.vogl@rochester.edu>.
BA Sample Schedule
BS Sample Schedule
“Mathematics and statistics are useful and practical skills, and when you combine them with computer science, they become even more so.”
Lauren Kemperman '17
data science major
PREREQUISITE COURSES
Prerequisite course requirements may be satisfied by AP credit or by testing, according to the standards used by the department that is home to the particular course. CSC 171 is satisfied by demonstration of knowledge of Java programming. The below courses cannot be taken S/F.
MTH 150 Discrete Mathematics
or MTH 150A Discrete Math Module
MTH 161 Calculus I and MTH 162 Calculus II
or MTH 141, MTH 142, and MTH 143
or MTH 171Q and MTH 172Q
CSC 171 The Science of Programming
CSC 172 The Science of Data Structures
CORE COURSES
MTH 165 Linear Algebra with Differential Equations
or MTH 163 Ordinary Differential Equations I AND MTH 235 Linear Algebra
or MTH 173 (only for those in the honors calculus series)
DSC 262/CSC 262 Computational Introduction to Statistics
or STT 213 Elements of Probability and Mathematical Statistics
or STT 212 Applied Statistics for the Biological and Physical Sciences I
DSC 265/CSC 265 Intermediate Statistical and Computational Methods
or both STT 216 Applied Statistics II and STT 226W Introduction to Linear Models
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 your senior year)
SUPPLEMENTARY COURSES
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:
 CSC 244 Logical Foundations of A.I.
 CSC 246 Machine Learning
 CSC 247 Natural Language Processing
 CSC 248 Statistical Speech & Language Processing
 CSC 249 Machine Vision
 CSC 252 Computer Organization
 CSC 298 Deep Learning and Graphical Models (NEW!)
APPLICATION AREA COURSES
Prerequisite for particular application area courses (beyond those included in the prerequisites and core for the data science major) may be required, please check the online course description / course schedule (CDCS).
Biology
One or both of the following:
 BIO 110/BIO 112 Principles of Biology I
 BIO 111/BIO 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 & Cognitive Sciences
Any three of the following courses:
 BCS 151 Perception & Action
 BCS 152 Language & Psycholinguistics
 BCS 153 Cognition
 BCS 221 Auditory Perception
 BCS 229 Computer Models of Human Perception & Cognition (NEW!)
 OPT 248/BCS 223 Vision and the Eye
 BCS 244 Neuroethology
 BCS 245 Sensory & Motor Neuroscience
 BCS 248 Neuroeconomics
 BCS 265 Language & 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 & Cognition (NEW!)
 CSC 246 Machine Learning
 CSC 247 Natural Language Processing
 CSC 248 Statistical Speech & Language Processing
 CSC 249 Machine Vision
 CSC 254 Programming Language and Design Implementation
 CSC 252 Computer Organization
 CSC 253 Dynamic Language & Software Development
 CSC 256 Operating Systems
 CSC 258 Parallel & Distributed Systems
 CSC 280 Computer Models & Limitations
 CSC 298 Deep Learning and Graphical Models (NEW!)
 DSC 210 Digital Imaging: Transforming Real Into Virtual (NEW!)
 DSC 267 Image, Text, & Technology (NEW!)
 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 & 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
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
Linguistics
Any three of the following courses:
 LIN 228 Lexical Semantics
 LIN 247 / CSC 247 Natural Language Processing
 LIN 250 Data Science for Linguistics
 LIN 260 Syntactic Theory
 LIN 265 Formal Semantics
 LIN 268 Computational Semantics
Physics
Any three of the following courses:
 MTH 281 Applied Boundary Value Problems
 PHY 237 Quantum Mechanics of Physical Systems
 PHY 227 Thermodynamics & Statistical Mechanics
 PHY 235W Classical Mechanics I
 PHY 373 Physics and Finance
Political Science
Any three of the following courses:
 PSC 200 Applied Data Analysis
 PSC 227 Designing American Democracy (Spring '17 only)
 PSC 278 / IR 278 Foundations of Modern International Politics
 PSC 281 Formal Models in Political Science
 PSC 288 / ECO 288/288W Game Theory
 PSC Independent Study
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