B.S. in Data Science
Students will:
- Use data to construct evidence-based solutions.
- Assimilate skills acquired through the degree program in application to a capstone project providing solutions to real-world challenges.
- Acquire data from a variety of sources including public research, web pages, and social media.
- Convert unstructured and varied data into analysis-ready form.
- Use software packages and libraries to support data analysis.
- Use statistical theory and modern machine learning techniques to model observations and make predictions.
- Implement data storage and processing architectures across clusters of commodity hardware and cloud resources.
- Manage issues related to program performance, scalability, and high-availability.
- Communicate deftly with proficiency in both verbal and nonverbal communication.
- Present actionable results of data analysis in multimedia formats to both technical and nontechnical audiences.
General Education Requirements
For a full description of Embry-Riddle General Education guidelines, please see the General Education section of this catalog. These minimum requirements are applicable to all degree programs.
Communication Theory & Skills (COM 122, COM 219, COM 221) | 9 | |
Humanities - Lower level | 3 | |
Social Sciences - Lower level | 3 | |
Humanities or Social Sciences - Lower or Upper level | 3 | |
Humanities or Social Sciences - Upper level | 3 | |
Computer Science (CS 223 or EGR 115) | 3 | |
Mathematics (MA 241 & MA 242) | 8 | |
Physical and Life Sciences - one course must include a lab | 7 | |
Total Credits | 39 |
Data Science Degree Requirements
UNIV 101 | College Success | 1 |
Data Science Core | ||
CS 222 | Introduction to Discrete Structures | 3 |
CS 225 | Computer Science II | 4 |
CS 315 | Data Structures and Analysis of Algorithms | 3 |
CS 317 | Files and Database Systems | 3 |
DS 444 | Scientific Visualization | 3 |
DS 490 | Data Science Capstone | 3 |
MA 243 | Calculus and Analytical Geometry III | 4 |
MA 412 | Probability and Statistics | 3 |
MA 413 | Statistics | 3 |
MA 432 | Linear Algebra | 3 |
Applied Data Science Concentration | ||
DS 390 | Research Project in Industrial Mathematics | 3 |
DS 440 | Data Mining | 3 |
MA 210 | Introduction to Data Science | 3 |
MA 305 | Introduction to Scientific Computing | 3 |
MA 360 | Mathematical Modeling & Simulation I | 3 |
MA 453 | High Performance Scientific Computing | 3 |
Electives | 15 | |
All students must declare and complete any Minor/Two Degrees of the Same Rank/Double Major (ROTC courses are acceptable) | ||
Any-Level Open Electives | 9 | |
Upper-Level Open Electives | 6 | |
Total Credits | 81 |
Total Degree Credits | 120 |
Year One | ||
---|---|---|
Credits | ||
COM 122 | English Composition | 3 |
COM 219 | Speech | 3 |
EGR 115 | Introduction to Computing for Engineers | 3 |
or CS 223
|
Scientific Programming in C | |
MA 210 | Introduction to Data Science | 3 |
MA 241 | Calculus and Analytical Geometry I | 4 |
MA 242 | Calculus and Analytical Geometry II | 4 |
Physical Science Elective | 3 | |
UNIV 101 | College Success | 1 |
Humanities Lower-Level Elective | 3 | |
Social Science Lower-Level Elective | 3 | |
Credits Subtotal | 30.0 | |
Year Two | ||
MA 243 | Calculus and Analytical Geometry III | 4 |
MA 305 | Introduction to Scientific Computing | 3 |
MA 412 | Probability and Statistics | 3 |
CS 222 | Introduction to Discrete Structures | 3 |
CS 225 | Computer Science II | 4 |
Physical Science Elective | 3 | |
Physical Science Laboratory | 1 | |
Elective * | 3 | |
Open Elective | 6 | |
Credits Subtotal | 30.0 | |
Year Three | ||
COM 221 | Technical Report Writing | 3 |
CS 315 | Data Structures and Analysis of Algorithms | 3 |
CS 317 | Files and Database Systems | 3 |
DS 390 | Research Project in Industrial Mathematics | 3 |
DS 440 | Data Mining | 3 |
MA 360 | Mathematical Modeling & Simulation I | 3 |
MA 413 | Statistics | 3 |
MA 432 | Linear Algebra | 3 |
Elective * | 3 | |
Open Electives | 3 | |
Credits Subtotal | 30.0 | |
Year Four | ||
MA 453 | High Performance Scientific Computing | 3 |
DS 444 | Scientific Visualization | 3 |
DS 490 | Data Science Capstone | 3 |
Lower or Upper-Level Humanities or Social Science Elective | 3 | |
Upper Level Humanities or Social Science Elective | 3 | |
Elective * | 9 | |
Open Electives | 6 | |
Credits Subtotal | 30.0 | |
Credits Total: | 120.0 |