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 2022
| Freshman | |||
|---|---|---|---|
| AUCC | Credits | ||
| CO 150 | College Composition (GT-CO2) | 1A | 3 |
| CS 163 or 164 | CS1---No Prior Programming Experience CS1--Computational Thinking with Java | 4 | |
| CS 165 | CS2--Data Structures | 4 | |
| DSCI 100 | First Year Seminar in Data Science | 1 | |
| MATH 160 | Calculus for Physical Scientists I (GT-MA1) | 1B | 4 |
| MATH 161 | Calculus for Physical Scientists II (GT-MA1) | 1B | 4 |
| STAT 158 | Introduction to R Programming | 1 | |
| STAT 315 | Intro to Theory and Practice of Statistics | 3 | |
| Arts and Humanities | 3B | 3 | |
| Biological and Physical Sciences | 3A | 4 | |
| Total Credits | 31 | ||
| Sophomore | |||
| CS 220 | Discrete Structures and their Applications | 4 | |
| CS 253 | Software Development with C++ | 4 | |
| CS 270 | Computer Organization | 4 | |
| DSCI 235 | Data Wrangling | 2 | |
| DSCI 369 | Linear Algebra for Data Science | 4 | |
| MATH 151 | Mathematical Algorithms in Matlab I | 1 | |
| MATH 261 | Calculus for Physical Scientists III | 4 | |
| STAT 341 | Statistical Data Analysis I | 3 | |
| STAT 342 | Statistical Data Analysis II | 3 | |
| Total Credits | 29 | ||
| Junior | |||
| 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 | ||
| Algorithms--Theory and Practice | |||
| Operating Systems | |||
| 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 | ||
| Computer Science Electives (Select one course from the Computer Science Electives List below) | 3-4 | ||
| Data Science Electives (Select at least 6 credits from the Data Science Electives List below) | 6-8 | ||
| Arts and Humanities | 3B | 3 | |
| Biological and Physical Sciences | 3A | 3 | |
| Total Credits | 28-31 | ||
| Senior | |||
| DSCI 445 | Statistical Machine Learning | 4B | 3 |
| DSCI 478 | Capstone Group Project in Data Science | 4A,4C | 4 |
| Computer Science Electives (Select two courses not taken in the junior year from the Computer Science Electives List below) | 7-8 | ||
| Diversity, Equity, and Inclusion | 1C | 3 | |
| Historical Perspectives | 3D | 3 | |
| Social and Behavioral Sciences | 3C | 3 | |
| Electives1 | 6-8 | ||
| Total Credits | 29-32 | ||
| Program Total Credits: | 120 | ||
Computer Science Electives List
| Code | Title | AUCC | Credits |
|---|---|---|---|
| Select three courses from the list below not taken elsewhere in the program: | |||
| CS 201/PHIL 201 | Ethical Computing Systems (GT-AH3) | 3B | 3 |
| CS 320 | Algorithms--Theory and Practice | 3 | |
| CS 345 | Machine Learning Foundations and Practice | 3 | |
| CS 370 | Operating Systems | 3 | |
| CS 420 | Introduction to Analysis of Algorithms | 4 | |
| CS 425 | Introduction to Bioinformatics Algorithms | 4 | |
| CS 430 | Database Systems | 4 | |
| CS 435 | Introduction to Big Data | 4 | |
| CS 440 | Introduction to Artificial Intelligence | 4 | |
| CS 445 | Introduction to Machine Learning | 4 | |
| CS 455 | Introduction to Distributed Systems | 4 | |
| CS 475 | Parallel Programming | 4 | |
Data Science Electives List
| Code | Title | AUCC | Credits |
|---|---|---|---|
| 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 |
| 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 360 | Mathematics of Information Security | 3 | |
| MATH 450 | Introduction to Numerical Analysis I | 3 | |
| MATH 451 | Introduction to Numerical Analysis II | 3 | |
| MATH 460 | Information and Coding Theory | 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 |
- 1
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 | ||
| DSCI 100 | First Year Seminar in Data Science | 1 | |||
| MATH 160 | Calculus for Physical Scientists I (GT-MA1) | 1B | 4 | ||
| Select one course from the following: | X | 4 | |||
| CS1---No Prior Programming Experience | |||||
| CS1--Computational Thinking with Java | |||||
| Arts and Humanities | 3B | 3 | |||
| Total Credits | 15 | ||||
| Semester 2 | Critical | Recommended | AUCC | Credits | |
| CS 165 | CS2--Data Structures | X | 4 | ||
| MATH 161 | Calculus for Physical Scientists II (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 | |||
| Total Credits | 16 | ||||
| Sophomore | |||||
| Semester 3 | Critical | Recommended | AUCC | Credits | |
| CS 220 | Discrete Structures and their Applications | X | 4 | ||
| CS 270 | Computer Organization | X | 4 | ||
| MATH 261 | Calculus for Physical Scientists III | 4 | |||
| STAT 341 | Statistical Data Analysis I | 3 | |||
| Total Credits | 15 | ||||
| Semester 4 | Critical | Recommended | AUCC | Credits | |
| CS 253 | Software Development with C++ | X | 4 | ||
| DSCI 235 | Data Wrangling | 2 | |||
| DSCI 369 | Linear Algebra for Data Science | 4 | |||
| MATH 151 | Mathematical Algorithms in Matlab I | 1 | |||
| STAT 342 | Statistical Data Analysis II | 3 | |||
| Total Credits | 14 | ||||
| Junior | |||||
| Semester 5 | Critical | Recommended | AUCC | Credits | |
| DSCI 320 | Optimization Methods in Data Science | 3 | |||
| Select one course from the following: | X | 3 | |||
| Algorithms--Theory and Practice | |||||
| Operating Systems | |||||
| 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 Elective (See List on Concentration Requirements Tab) | 3-4 | ||||
| Biological and Physical Sciences | 3A | 3 | |||
| Total Credits | 15-16 | ||||
| Semester 6 | Critical | Recommended | AUCC | Credits | |
| DSCI 335 | Inferential Reasoning in Data Analysis | 3 | |||
| DSCI 336 | Data Graphics and Visualization | 1 | |||
| Computer Science Elective (Select one course not previously taken from List on Concentration Requirements Tab) | 3-4 | ||||
| Data Science Elective (See List on Concentration Requirements Tab) | 3-4 | ||||
| Arts and Humanities | 3B | 3 | |||
| Total Credits | 13-15 | ||||
| Senior | |||||
| Semester 7 | Critical | Recommended | AUCC | Credits | |
| DSCI 445 | Statistical Machine Learning | 4B | 3 | ||
| Computer Science Elective (Select course not previously taken from List on Concentration Requirements Tab) | 3-4 | ||||
| Diversity, Equity, and Inclusion | 1C | 3 | |||
| Social and Behavioral Sciences | 3C | 3 | |||
| Elective | 3-4 | ||||
| Total Credits | 15-17 | ||||
| Semester 8 | Critical | Recommended | AUCC | Credits | |
| DSCI 478 | Capstone Group Project in Data Science | X | 4A,4C | 4 | |
| Computer Science Elective (Select course not previously taken from List on Concentration Requirements Tab) | X | 4 | |||
| Historical Perspectives | X | 3D | 3 | ||
| Elective | X | 3-4 | |||
| The benchmark courses for the 8th semester are the remaining courses in the entire program of study. | X | ||||
| Total Credits | 14-15 | ||||
| Program Total Credits: | 120 | ||||