University of Rochester Institute for Data Science

Undergraduate Degree in Data Science

The university offers an interdepartmental major in 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 areas of computational 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 161 is satisfied by demonstration of knowledge of Python programming; CSC 160, by knowledge of MATLAB programming; and CSC 171, by 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 161 The Art of Programming

            or CSC 160 Engineering Programming

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 211 Applied Statistics for the Social Sciences I or

            or STT 212 Applied Statistics for the Biological and Physical Sciences I

CSC 263 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

CSC 244 Machine Learning

CSC 247 Natural Language Processing

CSC 248 Statistical Speech & Language Processing

CSC 249 Machine Vision

CSC 252 Computer Organization

MTH 201 Introduction to Probability

MTH 203 Introduction to Mathematical Statistics

 

Application Area Courses

Prerequisite for particular application area courses (beyond those included in the prerequisites and core for the data science major) are noted below.

Biology

This area requires as prerequisites:

            BIO 110/BIO 112 Principles of Biology I

            BIO 111/BIO 113  Principles of Biology II

It requires:

BIO 198 Principles of Genetics

            or BIO 190 Genetics and the Human Genome

Plus any two of the following:

BIO 205 Evolution

BIO 206 Eukaryotic Genomes

BIO 253 Computational Biology

BIO 265 Molecular Evolution

 

Brain & Cognitive Sciences

Any three of the following courses:

BCS 151 Perception & Action

            Pre: BCS 110 Neural Foundations of Behavior or 

                        BCS 111 Foundations of Cognitive Science

BCS 152 Language & Psycholinguistics

            Pre: BCS 110 or BCS 111

BCS 153 Cognition

            Pre: BCS 110 or BCS 111

BCS 221 Auditory Perception

            Pre: BCS 110 or BCS 111

BCS 223 Vision and the Eye

BCS 240 Basic Neurobiology

            Pre: BIO 110 or BIO 112 ,  BIO 111 or BIO 113, BIO 111P or BIO 113P

BCS 244 Neuroethology

            Pre: BCS 240

BCS 245 Sensory & Motor Neuroscience

            Pre: BCS 240

BCS 265 Language & the Brain

            Pre: BCS 110 or BCS 111 or BCS 240, BCS 152 or LIN 110

 

Computer Science, Statistics, and Mathematics

Any three of the following courses:

CSC 244 Machine Learning

CSC 247 Natural Language Processing

CSC 248 Statistical Speech & Language Processing

CSC 249 Machine Vision

CSC 252 Computer Organization

CSC 256 Operating Systems

            Pre: CSC 252 Computer Organization

ECE 206 GPU Parallel C/C++ Programming

MTH 201 Introduction to Probability

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

STT 422 Design of Experiments

 

Earth and Environmental Science

Any three of the following courses:

EES 211 Geohazards and Their Mitigation: Living on an Active Planet

            Pre: EES 101 Introduction to Geological Sciences

EES 212 A Climate Change Perspective to Chemical Oceanography

            Pre: EES 101, CHM 131 Chemistry Concepts Systems Practice I

EES 222 Energy Resources

            Pre: EES 101, CHM 131, 132 Chemistry Concepts Systems Practice II

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

            Pre: PHY 122/142 Electricity & Magnetism,

                        PHY 123/143 Waves & Modern Physics

PHY 227 Thermodynamics & Statistical Mechanics

            Pre: PHY 237, MTH  281

PHY 246 Quantum Theory

            Pre: PHY 237, MTH 281

PHY 373 Physics and Finance

            Pre: PHY 227

 

Economics and Business

Any three of the following courses:

ECO 207 Intermediate Microeconomics

            Pre : ECO 108 Principles of Economics

ECO 209 Intermediate Macroeconomics

            Pre: ECO 207

ECO 214 Economic Theory of Organizations

            or ECO 217 Economics of Organizations

                        Pre: ECO 207

            or ECO 217 Economics of Contracts, Organizations & Markets

                        Pre: ECO 207

ECO 231W Econometrics

ECO 288 / PSC 288 Game Theory

            Pre: ECO 207

ACC 201 Financial Accounting

MKT 203 Principles of Marketing

            Pre: ECO 207, ACC 201

 

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 Game Theory

            Pre: ECO 207

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