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 gidsundergrad@rochester.edu.
Prerequisite Courses
Students must complete or be registered to complete the following prerequisite courses with before declaring a data science major:
 MATH 150: Discrete Mathematics OR MATH 150A: Discrete Math Module for MATH171
 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 IA and MATH 162: Calculus IIA
 MATH 141: Calculus I, MATH 142: Calculus II, and MATH 143: Calculus III
 MATH 171: Honors Calculus I and MATH 172: Honors Calculus II
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.
Core Courses
ALL OF THE FOLLOWING:
 DSCC/CSC 242: Introduction to Artificial Intelligence (fall/spring)
 DSCC/CSC 240: Data Mining (fall/spring)
 DSCC/CSC 261: Database Systems (fall/spring)
 DSCC 383W: Data Science Capstone (typically taken fall or spring of senior 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:
 STAT 190: Introduction to Statistical Methodology (replaces former courses DSCC/CSC/STAT 262: Computational Introduction to Statistics or STAT 213: Elements of Probability and Mathematical Statistics) (fall/spring)
 STAT 180: Introduction to Applied Statistical Methodology (replaces former course STAT 212: Applied Statistics for the Biological and Physical Sciences I) (fall/spring)
Plus ONE of the following:
 DSCC 265: Introduction to Statistical Machine Learning (formerly Intermediate Statistical and Computational Methods) (spring)
 Both STAT 216: Intermediate Statistical Methodology (formerly Applied Statistics II) (fall)and STAT 226W: Linear Models (formerly Introduction to Linear Models) (spring)
Plus ONE of the following:
 DSCC 201: Tools for Data Science (fall/spring)
 DSCC 275: Time Series Analysis & Forecasting in Data Science (fall)
 CSC 282: Design and Analysis of Efficient Algorithms (fall/spring)
Concentration (Application Area) Courses
Students can choose from one of the following concentrations (application areas):
 Biology
 Biomedical signals and imaging
 Brain and cognitive sciences
 Computer science, statistics, and mathematics
 Earth and environmental science
 Linguistics
 Physics
 Economics and business
 Political science
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 245: Deep Learning
 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 263: Data Management Systems (spring)
 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 202: Data Science at Scale (spring)
 DSCC 210: Digital Imaging: Transforming Real Into Virtual
 DSCC 275: Time Series Analysis and Forecasting in Data Science (fall)
 MATH 201: Introduction to Probability (fall/spring)
 MATH 202: Stochastic Processes(spring)
 MATH 203: Introduction to Mathematical Statistics (fall/spring)
 MATH 208: Operations Research I (fall)
 MATH 215: Fractal and Chaotic Dynamics(fall odd years)
 MATH 218: Introduction to Mathematical Models in Life Science(spring odd years)
 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 and 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 202122)
 PSCI 287 Theories of Political Economy
 PSCI 288/ECON 288/288W: Game Theory (fall/spring)
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 (fall/spring)
Plus ONE of the following:
 CSC 244: Knowledge Representation and Reasoning in AI (fall)
 CSC 245: Deep Learning
 CSC 246: Machine Learning (fall/spring)
 CSC 247: Natural Language Processing (spring)
 CSC 248: Statistical Speech and Language Processing (fall)
 CSC 249: Machine Vision (spring)
 CSC 252: Computer Organization (fall/spring)
 CSC 282: Design and Analysis of Efficient Algorithms (fall/spring)
 DSCC 201: Tools for Data Science (fall/spring)
 DSCC 275: Time Series Analysis and 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 data science major course may overlap with 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.
Math Minor
A data science BS major can easily earn a math minor by taking one additional math course, MATH 235: Linear Algebra. The following shows the courses need for the math minor and how it is permitted with exceptions from the overlap policy.
 MATH150 – no overlap per policy/prereqs in data science/foundational for math minor
 MATH161 & MATH 162  no overlap per policy/prereqs in data science/foundational for math minor (or MATH 141, 142 & 143 OR MATH 171 & MATH 172)
 MATH165  no overlap per policy/core for data science/foundational for math minor
 MATH201 – overlap/BS requirement for data science/advanced course for math minor
 MATH203 – overlap/BS requirement for data science/advanced course for math minor
 MATH235 – no overlap
Computer Science Minor
A data science major can earn a computer science minor by taking two additional computer science courses. The following shows the courses need for the computer science minor and how it is permitted with exceptions from the overlap policy.
 CSC171  no overlap per policy/prereqs in data science
 CSC172  no overlap per policy/prereqs in data science
 CSC/DSCC240, CSC/DSCC261, CSC/DSCC242 – Use two of these courses for the two overlaps/core requirement for data science/computer science minor
 Two non overlapping computer science courses above the level of 130.
No more than two of the six courses for the minor may be completed at other institutions unless all the external courses are taken as part of the University's education abroad program.
UpperLevel Writing
Every data science major is required to complete TWO upperlevel 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 (i.e. ECON 231W, PHIL 257W)
 Creation of a research paper or published technical report as part of an independent study (DSCC 391W), with advisor approval.
Sample Schedule
Included below is an example of a possible fouryear schedule for a data science student pursuing a Bachelor of Science (BS). Sample BA and alternative BS sample schedules are available via PDF.
CLASS YEAR  FALL  SPRING 

FIRST YEAR 


SOPHOMORE 


JUNIOR 


SENIOR 

