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 2021
| 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 | |
| ECON 202 | Principles of Microeconomics (GT-SS1) | 3C | 3 |
| ECON 204 | Principles of Macroeconomics (GT-SS1) | 3C | 3 |
| 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 | |
| Total Credits | 30 | ||
| Sophomore | |||
| CS 220 | Discrete Structures and their Applications | 4 | |
| DSCI 235 | Data Wrangling | 2 | |
| DSCI 369 | Linear Algebra for Data Science | 4 | |
| ECON 211 | Gender in the Economy (GT-SS1) | 1C | 3 |
| ECON 304 | Intermediate Macroeconomics | 3 | |
| ECON 306 | Intermediate Microeconomics | 3 | |
| 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 | 30 | ||
| Junior | |||
| DSCI 320 | Optimization Methods in Data Science | 3 | |
| DSCI 335 | Inferential Reasoning in Data Analysis | 3 | |
| DSCI 336 | Data Graphics and Visualization | 1 | |
| ECON 335/AREC 335 | Introduction to Econometrics | 3 | |
| ECON 435 | Intermediate Econometrics | 3 | |
| 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 a minimum of 3 credits from the Data Science Electives list below) | 3-4 | ||
| Arts and Humanities | 3B | 6 | |
| Biological and Physical Sciences | 3A | 3 | |
| Historical Perspectives | 3D | 3 | |
| Total Credits | 31-32 | ||
| Senior | |||
| DSCI 445 | Statistical Machine Learning | 4B | 3 |
| DSCI 478 | Capstone Group Project in Data Science | 4A,4C | 4 |
| Data Science Electives (Select a minimum of 6 credits not previously taken from the Data Science Electives List below) | 6-8 | ||
| Economics Electives (See List below) | 6 | ||
| Biological and Physical Sciences | 3A | 4 | |
| Electives1 | 4-6 | ||
| Total Credits | 27-31 | ||
| Program Total Credits: | 120 | ||
Data Science Electives List
| Code | Title | AUCC | Credits |
|---|---|---|---|
| CS 201/PHIL 201 | Ethical Computing Systems (GT-AH3) | 3B | 3 |
| CS 253 | Software Development with C++ | 4 | |
| CS 270 | Computer Organization | 4 | |
| CS 320 | Algorithms--Theory and Practice | 3 | |
| CS 345 | Machine Learning Foundations and Practice | 3 | |
| CS 370 | Operating Systems | 3 | |
| DSCI 473 | Introduction to Geometric Data Analysis | 2 | |
| DSCI 475 | Topological Data Analysis | 2 | |
| 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 | |
| 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 |
Economics Electives List
| Code | Title | AUCC | Credits |
|---|---|---|---|
| ECON 315 | Money and Banking | 3 | |
| ECON 317 | Population Economics | 3 | |
| ECON 320 | Economics of Public Finance | 3 | |
| ECON 325 | Health Economics | 3 | |
| ECON 327 | Law and Economics | 3 | |
| ECON 332/POLS 332 | International Political Economy | 3 | |
| ECON 340/AREC 340 | Introduction-Economics of Natural Resources | 3 | |
| ECON 346/AREC 346 | Economics of Outdoor Recreation | 3 | |
| ECON 372 | History of Economic Institutions and Thought | 3 | |
| ECON 376 | Marxist Economic Thought | 3 | |
| ECON 379/HIST 379 | Economic History of the United States | 3 | |
| ECON 404 | Macroeconomic Policy | 3 | |
| ECON 410 | Labor Economics | 3 | |
| ECON 440 | Economics of International Trade and Policy | 3 | |
| ECON 442 | Economics of International Finance and Policy | 3 | |
| ECON 460 | Economic Development | 3 | |
| ECON 463 | Regional Economics | 3 | |
| ECON 474 | Recent Economic Thought | 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 | |||
| ECON 202 | Principles of Microeconomics (GT-SS1) | 3C | 3 | ||
| MATH 160 | Calculus for Physical Scientists I (GT-MA1) | 1B | 4 | ||
| Select one course from the following: | 4 | ||||
| CS1---No Prior Programming Experience | X | ||||
| CS1--Computational Thinking with Java | X | ||||
| Total Credits | 15 | ||||
| Semester 2 | Critical | Recommended | AUCC | Credits | |
| CS 165 | CS2--Data Structures | X | 4 | ||
| ECON 204 | Principles of Macroeconomics (GT-SS1) | 3C | 3 | ||
| 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 | |||
| Total Credits | 15 | ||||
| Sophomore | |||||
| Semester 3 | Critical | Recommended | AUCC | Credits | |
| CS 220 | Discrete Structures and their Applications | X | 4 | ||
| ECON 306 | Intermediate Microeconomics | 3 | |||
| MATH 261 | Calculus for Physical Scientists III | 4 | |||
| STAT 341 | Statistical Data Analysis I | 3 | |||
| Total Credits | 14 | ||||
| Semester 4 | Critical | Recommended | AUCC | Credits | |
| DSCI 235 | Data Wrangling | 2 | |||
| DSCI 369 | Linear Algebra for Data Science | X | 4 | ||
| ECON 211 | Gender in the Economy (GT-SS1) | 1C | 3 | ||
| ECON 304 | Intermediate Macroeconomics | 3 | |||
| MATH 151 | Mathematical Algorithms in Matlab I | 1 | |||
| STAT 342 | Statistical Data Analysis II | 3 | |||
| Total Credits | 16 | ||||
| Junior | |||||
| Semester 5 | Critical | Recommended | AUCC | Credits | |
| DSCI 320 | Optimization Methods in Data Science | 3 | |||
| ECON 335/AREC 335 | Introduction to Econometrics | 3 | |||
| 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 | ||||
| Arts and Humanities | 3B | 3 | |||
| Historical Perspectives | 3D | 3 | |||
| Total Credits | 15 | ||||
| Semester 6 | Critical | Recommended | AUCC | Credits | |
| DSCI 335 | Inferential Reasoning in Data Analysis | 3 | |||
| DSCI 336 | Data Graphics and Visualization | 1 | |||
| ECON 435 | Intermediate Econometrics | 3 | |||
| Data Science Elective (See List on Concentration Requirements Tab) | 3-4 | ||||
| Arts and Humanities | 3B | 3 | |||
| Biological and Physical Sciences | 3A | 3 | |||
| Total Credits | 16-17 | ||||
| Senior | |||||
| Semester 7 | Critical | Recommended | AUCC | Credits | |
| DSCI 445 | Statistical Machine Learning | 4B | 3 | ||
| Data Science Electives (See List on Concentration Requirements Tab) | 3-4 | ||||
| Economics Elective (See List on Concentration Requirements Tab) | 3 | ||||
| Biological and Physical Sciences | 3A | 4 | |||
| Elective | 3 | ||||
| Total Credits | 16-17 | ||||
| Semester 8 | Critical | Recommended | AUCC | Credits | |
| DSCI 478 | Capstone Group Project in Data Science | X | 4A,4C | 4 | |
| Data Science Electives (See List on Concentration Requirements Tab) | X | 3-4 | |||
| Economics Elective (See List on Concentration Requirements Tab) | X | 3 | |||
| Elective | X | 3 | |||
| The benchmark courses in the 8th semester are the remaining courses in the entire program of study. | X | ||||
| Total Credits | 13-14 | ||||
| Program Total Credits: | 120 | ||||