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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 Courses

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:

  • 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)

Plus 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)
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 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

Student can choose one of the following concentration (application areas):

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:

  • 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

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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)

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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 248: Neuroeconomics (not offered after Fall 2017)
  • BCSC 265: Language and the Brain (spring)
  • BCSC 247, Topics in Computational Neuroscience

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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)
  • 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 2019)
  • 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

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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

 

 

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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

 

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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 2020)

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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/apring) (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)

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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 (valid beginning spring 2018)
  • PSCI 248 Discrimination (fall)
  • PSCI/INTR 270 Mechanisms of International Relations (Not offered next year.)
  • 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 2018-19)
  • PSCI 287 Theories of Political Economy (Not offered next year)
  • PSCI 288/ECON 288/288W: Game Theory (fall/spring)

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Supplementary Courses (Only Required for BS)

Only 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)

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)
  • DSCC 201: Tools for Data Science (fall)
  • DSCC 275: Time Series Analysis & Forecasting in Data Science (fall)
Clusters
Data science is a natural science major. It therefore requires majors to complete a humanities cluster and a social science cluster. Up to one course may serve as both an application area course and part of a cluster.
Upper-Level Writing

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

  • WRTG 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.
Sample Schedule

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: MATH 161PREREQUISITE: MATH 162PREREQUISITE: CSC 172CORE: CSC 242
PREREQUISITE: MATH 150PREREQUISITE: CSC 171CORE: MATH 165CORE: DSCC 265
Application Area PrerequisiteWRTG 105CORE: DSCC 262Cluster or Free Elective
Cluster or Free ElectiveAPPLICATION AREA courseCluster or Free ElectiveCluster or Free Elective

Junior

Senior

Fall

Spring

Fall

Spring

CORE: DSCC 240CORE: DSCC 261CORE: DSCC 475CORE: DSCC 383W
BS SUPPLEMENTAL: MATH 201BS SUPPLEMENTAL: MATH 203BS SUPPLEMENTAL: upper level CSC courseAPPLICATION AREA course
WRTG 273Cluster or Free ElectiveCluster or Free ElectiveCluster or Free Elective
APPLICATION AREA courseCluster or Free ElectiveCluster or Free ElectiveCluster or Free Elective