Data Science is the discovery of knowledge and insight through the analysis of data. As such, it draws on the study of algorithms and their implementation from computer science, the power of abstraction and of geometric and topological formalism from mathematics, and the modeling and analysis of data from statistics. It has emerged as a separate field in response to the avalanche of data from web enabled sensors and instrumentation, mobile devices, web logs and transactions, and the availability of computing power for data storage and analysis. Modern data is challenging not only due to its large scale, but also because it is increasingly heterogeneous and unstructured. Information gleaned from this data none-the-less is revolutionizing diverse areas of human endeavor from health policy to high energy physics.
Effective Fall 2023
| Freshman | |||
|---|---|---|---|
| AUCC | Credits | ||
| CO 150 | College Composition (GT-CO2) | 1A | 3 |
| CS 150B | Culture and Coding: Python (GT-AH3) | 3B | 3 |
| CS 164 | CS1--Computational Thinking with Java | 4 | |
| DSCI 100 | First Year Seminar in Data Science | 1 | |
| DSCI 369 | Linear Algebra for Data Science | 4 | |
| MATH 1561 | Mathematics for Computational Science I (GT-MA1) | 1B | 4 |
| STAT 158 | Introduction to R Programming | 1 | |
| STAT 315 | Intro to Theory and Practice of Statistics | 3 | |
| Biological and Physical Sciences | 3A | 4 | |
| Diversity, Equity, and Inclusion | 1C | 3 | |
| Total Credits | 30 | ||
| Sophomore | |||
| CS 165 | CS2--Data Structures | 4 | |
| CS 220 | Discrete Structures and their Applications | 4 | |
| DSCI 235 | Data Wrangling | 2 | |
| MATH 151 | Mathematical Algorithms in Matlab I | 1 | |
| MATH 2561 | Mathematics for Computational Science II | 4 | |
| STAT 341 | Statistical Data Analysis I | 3 | |
| STAT 342 | Statistical Data Analysis II | 3 | |
| Biological and Physical Sciences | 3A | 3 | |
| Historical Perspectives | 3D | 3 | |
| Social and Behavioral Sciences | 3C | 3 | |
| Total Credits | 30 | ||
| Junior | |||
| CS 201/PHIL 201 | Ethical Computing Systems (GT-AH3) | 3B | 3 |
| DSCI 320 | Optimization Methods in Data Science | 3 | |
| DSCI 335 | Inferential Reasoning in Data Analysis | 3 | |
| DSCI 336 | Data Graphics and Visualization | 1 | |
| Select one course from the following: | 3 | ||
| Writing Arguments (GT-CO3) | 2 | ||
| Writing in the Disciplines: Sciences (GT-CO3) | 2 | ||
| Writing in Digital Environments (GT-CO3) | 2 | ||
| Strategic Writing and Communication (GT-CO3) | 2 | ||
| Data Science Electives (Select at least 6 credits from the Data Science Electives List below)2 | 6-9 | ||
| Math Electives (Select two courses from the Math Electives List below) | 6 | ||
| Electives | 3 | ||
| Total Credits | 28-31 | ||
| Senior | |||
| DSCI 445 | Statistical Machine Learning | 4B | 3 |
| DSCI 478 | Capstone Group Project in Data Science | 4A,4C | 4 |
| Data Science Electives (Select at least six credits from the Data Science Electives List below not taken in Junior year)2 | 6-9 | ||
| Math Electives (Select two courses from the Math Electives List not taken in Junior year) | 6 | ||
| Electives3 | 10 | ||
| Total Credits | 29-32 | ||
| Program Total Credits: | 120 | ||
Data Science Electives List2
| Code | Title | AUCC | Credits |
|---|---|---|---|
| Select a minimum of 15 total credits from the list below: | |||
| CS 214 | Software Development | 3 | |
| CS 250 | Computer Systems Foundations | 4 | |
| CS 270 | Computer Organization | 4 | |
| CS 314 | Software Engineering | 3 | |
| CS 320 | Algorithms--Theory and Practice | 3 | |
| CS 370 | Operating Systems | 3 | |
| CS 435 | Introduction to Big Data | 4 | |
| CS 440 | Introduction to Artificial Intelligence | 4 | |
| CT 301 | C++ Fundamentals | 2 | |
| DSCI 473 | Introduction to Geometric Data Analysis | 2 | |
| DSCI 475 | Topological Data Analysis | 2 | |
| ECON 202 | Principles of Microeconomics (GT-SS1) | 3C | 3 |
| ECON 204 | Principles of Macroeconomics (GT-SS1) | 3C | 3 |
| ECON 304 | Intermediate Macroeconomics | 3 | |
| ECON 306 | Intermediate Microeconomics | 3 | |
| ECON 435 | Intermediate Econometrics | 3 | |
| STAT 400 | Statistical Computing | 3 | |
| STAT 420 | Probability and Mathematical Statistics I | 3 | |
| STAT 421 | Introduction to Stochastic Processes | 3 | |
| STAT 430 | Probability and Mathematical Statistics II | 3 | |
| STAT 440 | Bayesian Data Analysis | 3 | |
| STAT 460 | Applied Multivariate Analysis | 3 | |
Math Electives List
| Code | Title | Credits |
|---|---|---|
| Select four courses from the list below: | ||
| MATH 301 | Introduction to Combinatorial Theory | 3 |
| MATH 317 | Advanced Calculus of One Variable | 3 |
| MATH 331 | Introduction to Mathematical Modeling | 3 |
| MATH 332 | Partial Differential Equations | 3 |
| MATH 345 | Differential Equations | 4 |
| MATH 360 | Mathematics of Information Security | 3 |
| MATH 417 | Advanced Calculus I | 3 |
| MATH 430/ECE 430 | Fourier and Wavelet Analysis with Apps | 3 |
| MATH 455 | Mathematics in Biology and Medicine | 3 |
| MATH 460 | Information and Coding Theory | 3 |
- 1
The calculus requirement for the major may alternatively be satisfied by completion of MATH 160, MATH 161, and MATH 261.
- 2
A minimum of 15 total credits must be selected from the Data Science Electives in the Junior and Senior years.
- 3
Select enough elective credits to bring the program total to a minimum of 120 credits, of which at least 42 must be upper-division (300- to 400-level).
| Freshman | |||||
|---|---|---|---|---|---|
| Semester 1 | Critical | Recommended | AUCC | Credits | |
| CO 150 | College Composition (GT-CO2) | 1A | 3 | ||
| CS 150B | Culture and Coding: Python (GT-AH3) | X | 3B | 3 | |
| DSCI 100 | First Year Seminar in Data Science | 1 | |||
| MATH 156 | Mathematics for Computational Science I (GT-MA1) | 1B | 4 | ||
| Diversity, Equity, and Inclusion | X | 1C | 3 | ||
| Total Credits | 14 | ||||
| Semester 2 | Critical | Recommended | AUCC | Credits | |
| CS 164 | CS1--Computational Thinking with Java | X | 4 | ||
| DSCI 369 | Linear Algebra for Data Science | 4 | |||
| STAT 158 | Introduction to R Programming | X | 1 | ||
| STAT 315 | Intro to Theory and Practice of Statistics | X | 3 | ||
| Biological and Physical Sciences | 3A | 4 | |||
| Total Credits | 16 | ||||
| Sophomore | |||||
| Semester 3 | Critical | Recommended | AUCC | Credits | |
| CS 165 | CS2--Data Structures | X | 4 | ||
| STAT 341 | Statistical Data Analysis I | X | 3 | ||
| Historical Perspectives | 3D | 3 | |||
| Social and Behavioral Sciences | 3C | 3 | |||
| Total Credits | 13 | ||||
| Semester 4 | Critical | Recommended | AUCC | Credits | |
| CS 220 | Discrete Structures and their Applications | X | 4 | ||
| DSCI 235 | Data Wrangling | 2 | |||
| MATH 151 | Mathematical Algorithms in Matlab I | 1 | |||
| MATH 256 | Mathematics for Computational Science II | 4 | |||
| STAT 342 | Statistical Data Analysis II | 3 | |||
| Biological and Physical Sciences | 3A | 3 | |||
| Total Credits | 17 | ||||
| Junior | |||||
| Semester 5 | Critical | Recommended | AUCC | Credits | |
| DSCI 320 | Optimization Methods in Data Science | 3 | |||
| Data Science Elective (See List on Concentration Requirements Tab) | 3-4 | ||||
| Math Elective (See List on Concentration Requirements Tab) | 3 | ||||
| Select one course from the following: | 2 | 3 | |||
| Writing Arguments (GT-CO3) | 2 | ||||
| Writing in the Disciplines: Sciences (GT-CO3) | 2 | ||||
| Writing in Digital Environments (GT-CO3) | 2 | ||||
| Strategic Writing and Communication (GT-CO3) | 2 | ||||
| Elective | 3 | ||||
| Total Credits | 15-16 | ||||
| Semester 6 | Critical | Recommended | AUCC | Credits | |
| CS 201/PHIL 201 | Ethical Computing Systems (GT-AH3) | 3B | 3 | ||
| DSCI 335 | Inferential Reasoning in Data Analysis | 3 | |||
| DSCI 336 | Data Graphics and Visualization | 1 | |||
| Data Science Elective (See List on Concentration Requirements Tab) | 3-5 | ||||
| Math Elective (See List on Concentration Requirements Tab) | 3 | ||||
| Total Credits | 13-15 | ||||
| Senior | |||||
| Semester 7 | Critical | Recommended | AUCC | Credits | |
| DSCI 445 | Statistical Machine Learning | 4B | 3 | ||
| Data Science Elective (See List on Concentration Requirements Tab) | 3-4 | ||||
| Math Elective (See List on Concentration Requirements Tab) | 3 | ||||
| Electives | 6 | ||||
| Total Credits | 15-16 | ||||
| Semester 8 | Critical | Recommended | AUCC | Credits | |
| DSCI 478 | Capstone Group Project in Data Science | X | 4A,4C | 4 | |
| Data Science Elective (See List on Concentration Requirements Tab) | X | 3-5 | |||
| Math Elective (See List on Concentration Requirements Tab) | X | 3 | |||
| Electives | X | 4 | |||
| The benchmark courses for the 8th semester are the remaining courses in the entire program of study. | X | ||||
| Total Credits | 14-16 | ||||
| Program Total Credits: | 120 | ||||