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.
Distinctive Requirements for Degree Program:
TO PREPARE FOR FIRST SEMESTER: The curriculum for the Major in Data Science assumes students enter college prepared to begin a year‐long calculus sequence (either MATH 155/MATH 255 or MATH 160/MATH 161) in the first semester of their first year. LIFE 102 requires high school chemistry as a prerequisite; CHEM 111 requires Algebra II as a prerequisite (this prerequisite is met by having Algebra II by test credit, transfer credit, or placement out of MATH 117 and MATH 118 on Math Placement Exam).
| Freshman |
|---|
| Semester 1 | Critical | Recommended | AUCC | Credits |
|---|
| CO 150 | College Composition (GT-CO2) | X | | 1A | 3 |
| CS 150B | Culture and Coding: Python (GT-AH3) | X | | 3B | 3 |
| DSCI 100 | First Year Seminar in Data Science | X | | | 1 |
| MATH 156 | Mathematics for Computational Science I (GT-MA1) | | | 1B | 4 |
| PSY 100 | General Psychology (GT-SS3) | X | | 3C | 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 |
| LIFE 102 | Attributes of Living Systems (GT-SC1) | X | | 3A | 4 |
| STAT 158 | Introduction to R Programming | X | | | 1 |
| STAT 315 | Intro to Theory and Practice of Statistics | X | | | 3 |
| | Total Credits | | | | 16 |
| Sophomore |
|---|
| Semester 3 | Critical | Recommended | AUCC | Credits |
|---|
| CHEM 107 | Fundamentals of Chemistry (GT-SC2) | X | | 3A | 4 |
| CHEM 108 | Fundamentals of Chemistry Laboratory (GT-SC1) | X | | 3A | 1 |
| CS 165 | CS2--Data Structures | X | | | 4 |
| MATH 256 | Mathematics for Computational Science II | | | | 4 |
| STAT 341 | Statistical Data Analysis I | X | | | 3 |
| | Total Credits | | | | 16 |
| Semester 4 | Critical | Recommended | AUCC | Credits |
|---|
| CS 220 | Discrete Structures and their Applications | X | | | 4 |
| DSCI 235 | Data Wrangling | X | | | 2 |
| MATH 151 | Mathematical Algorithms in Matlab I | X | | | 1 |
| STAT 342 | Statistical Data Analysis II | X | | | 3 |
| Select one course from the following: | X | | | 3-4 |
| Molecular and General Genetics | | | | |
| Introductory Genetics: Molecular/Immunological/Developmental (GT-SC2) | | | 3A | |
| | Total Credits | | | | 13-14 |
| Junior |
|---|
| Semester 5 | Critical | Recommended | AUCC | Credits |
|---|
| BMS 300 | Principles of Human Physiology | X | | | 4 |
| DSCI 320 | Optimization Methods in Data Science | X | | | 3 |
| PSY 252 | Mind, Brain, and Behavior | X | | | 3 |
| Select one course from the following: | X | | | 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 (see list on Concentration Requirements tab) | X | | | 4 |
| | Total Credits | | | | 17 |
| Semester 6 | Critical | Recommended | AUCC | Credits |
|---|
| CS 201/PHIL 201 | Ethical Computing Systems (GT-AH3) | | | 3B | 3 |
| DSCI 335 | Inferential Reasoning in Data Analysis | X | | | 3 |
| DSCI 336 | Data Graphics and Visualization | X | | | 1 |
| Diversity, Equity, and Inclusion | | | 1C | 3 |
| Historical Perspectives | X | | 3D | 3 |
| | Total Credits | | | | 13 |
| Senior |
|---|
| Semester 7 | Critical | Recommended | AUCC | Credits |
|---|
| BMS 325 | Cellular Neurobiology | X | | | 3 |
| DSCI 445 | Statistical Machine Learning | X | | 4B | 3 |
| Neuroscience Elective (See List on Concentration Requirements Tab) | X | | | 3 |
| Electives | | | | 7-8 |
| | Total Credits | | | | 16-17 |
| Semester 8 | Critical | Recommended | AUCC | Credits |
|---|
| BMS 345 | Functional Neuroanatomy | X | | | 4 |
| DSCI 478 | Capstone Group Project in Data Science | X | | 4A,4C | 4 |
| PSY 458 | Cognitive Neuroscience | X | | | 3 |
| Neuroscience Elective (See List on Concentration Requirements Tab) | X | | | 3 |
| The benchmark courses in the 8th semester are the remaining courses in the entire program of study. | X | | | |
| | Total Credits | | | | 14 |
| | Program Total Credits: | | | | 120 |
