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 level3
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 lab7
Total Credits39

Data Science Degree Requirements

UNIV 101College Success1
Data Science Core
CS 222Introduction to Discrete Structures3
CS 225Computer Science II4
CS 315Data Structures and Analysis of Algorithms3
CS 317Files and Database Systems3
DS 444Scientific Visualization3
DS 490Data Science Capstone3
MA 243Calculus and Analytical Geometry III4
MA 412Probability and Statistics3
MA 413Statistics3
MA 432Linear Algebra3
Applied Data Science Concentration
DS 390Research Project in Industrial Mathematics3
DS 440Data Mining3
MA 210Introduction to Data Science3
MA 305Introduction to Scientific Computing3
MA 360Mathematical Modeling & Simulation I3
MA 453High Performance Scientific Computing3
All students must declare and complete any Minor/Two Degrees of the Same Rank/Double Major (ROTC courses are acceptable)
Any-Level Open Electives9
Upper-Level Open Electives6
Total Credits81
Total Degree Credits120
Year One
COM 122 English Composition 3
COM 219 Speech 3
EGR 115 Introduction to Computing for Engineers 3
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 Subtotal30.0
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 Subtotal30.0
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 Subtotal30.0
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 Subtotal30.0
 Credits Total: 120.0