University of Rochester Institute for Data Science

Undergraduate Degree in Data Science

The university offers an interdepartmental BA/BS with a focus on data science. 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.

The major consists of:

  • Prerequisite courses, which would typically be completed or in progress before declaring the major.
  • Required Core courses Mathematics, Computer Science, and Statistics.
  • Supplementary courses, which are recommended but not required.
  • Three upper-level courses in an application area. 

In order to plan and declare your major, please contact Michelle Saile <michelle.saile@rochester.edu>.

 

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.

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

            or MTH 235 Linear Algebra

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

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

 

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 Machine Learning
  • CSC 247 Natural Language Processing
  • CSC 248 Statistical Speech & Language Processing
  • CSC 249 Machine Vision
  • CSC 252 Computer Organization

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
  • 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 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
  • 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

 

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

 

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
  • 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 281 Formal Models in Political Science
  • PSC 288 / ECO 288/288W Game Theory

 

Back to degree programs