Student Learning Outcomes
Students will:
- Use data to construct evidence-based solutions.
- 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.
- Assimilate skills acquired through the degree program in application to a capstone project providing solutions to real-world challenges.
Degree Requirements
The Bachelor of Science in Data Science can be earned in eight semesters assuming appropriate background and fulltime enrollment. Successful completion of a minimum of 121 credit hours is required, with a CGPA of 2.0 or higher. For Data Science majors, all MA and CS courses must be passed with a grade of C or better.
Students are required to choose a track of specialization. Some fields which complement Data Science are Air Traffic Control, Business/Economics, Computer Science, Cyber Security, Mathematics, Physics, and Psychology. Students are afforded 15 credits in Track Elective to pursue this area of focus in addition to 6 credits of open electives required in the program.
Students will be encouraged to have an applied practicum experience. This requirement may be fulfilled in several ways, including co-ops, internships, or working on an on-campus research team. Practicums provide opportunities to gain practical experience in real-world settings. A practicum experience is highly regarded by employers and increases the student’s employment potential after graduation. Typically, students will engage in practical experience activities throughout the degree program so they can take maximum advantage of their undergraduate experience.
Program Requirements
General Education
Embry-Riddle degree programs require students to complete a minimum of 36 hours of General Education coursework. For a full description of Embry-Riddle General Education guidelines, please see the General Education section of this catalog.
Students may choose other classes outside of their requirements, but doing so can result in the student having to complete more than the degree's 121 credit hours. This will result in additional time and cost to the student
| 9 |
| 3 |
| 6 |
| 6 |
| 12 |
| |
| |
| |
| |
Total Credits | 36 |
Data Science Core (92 Credits)
The following course of study outlines the quickest and most cost-efficient route for students to earn their B.S. in Data Science. Students are encouraged to follow the course of study to ensure they complete all program required courses and their prerequisites within four years.
Courses in the core with a # will satisfy your general education requirements.
CI 460 | Big Data Analytics and Machine Learning * | 3 |
COM 122 | English Composition # | 3 |
CS 118 | Fundamentals of Computer Programming # | 3 |
CS 125 | Computer Science I | 4 |
CS 315 | Data Structures and Analysis of Algorithms * | 3 |
CS 317 | Files and Database Systems * | 3 |
DS 150 | Data Science I: Introduction | 3 |
DS 151 | Data Science II: Foundations | 3 |
DS 244 | Data Acquisition and Manipulation | 3 |
DS 312 | Machine Learning | 3 |
DS 317 | Statistical Software | 3 |
DS 411 | Data Visualization | 3 |
DS 413 | Statistics for Data Science | 3 |
DS 483 | Cloud Computing | 3 |
DS 490 | Data Science Capstone | 3 |
# | 6 |
# | 3 |
# | 3 |
# | 3 |
# | 3 |
MA 225 | Introduction to Discrete Structures | 3 |
MA 241 | Calculus and Analytical Geometry I # | 4 |
MA 242 | Calculus and Analytical Geometry II # | 4 |
MA 243 | Calculus and Analytical Geometry III | 4 |
MA 335 | Introduction to Linear and Abstract Algebra ** | 3 |
MA 412 | Probability and Statistics | 3 |
SE 300 | Software Engineering Practices ** | 3 |
| 3 |
UNIV 101 | College Success | 1 |
Total Credits | 92 |
Natural Science (with one lab attached to course) choose two (8 credits)
BIO 120 & 120L | Foundations of Biology I and Foundations of Biology I Laboratory # | 4 |
BIO 121 & 121L | Foundations of Biology II and Foundations of Biology II Lab # | 4 |
CHM 110 & 110L | General Chemistry I and General Chemistry I Laboratory # | 4 |
CHM 111 & 111L | General Chemistry II and General Chemistry II Laboratory # | 4 |
PS 161 | Physics I & II for Engineers # | 4 |
Track Electives (15 Credits)
Open Electives (6 Credits)
All Army ROTC students are required to complete SS 321 - U.S. Military History 1900-Present (3 credits) in order to commission.
Suggested Plan of Study
Freshman Year |
---|
Fall | Credits |
---|
COM 122 |
English Composition | 3 |
CS 118 |
Fundamentals of Computer Programming | 3 |
DS 150 |
Data Science I: Introduction | 3 |
MA 241 |
Calculus and Analytical Geometry I | 4 |
UNIV 101 |
College Success | 1 |
| Credits Subtotal | 14.0 |
Spring | |
---|
COM 219 |
Speech | 3 |
CS 125 |
Computer Science I | 4 |
DS 151 |
Data Science II: Foundations | 3 |
| HU LL Elective |
3 |
MA 242 |
Calculus and Analytical Geometry II | 4 |
| Credits Subtotal | 17.0 |
Sophomore Year |
---|
Fall | |
---|
MA 225 |
Introduction to Discrete Structures | 3 |
MA 243 |
Calculus and Analytical Geometry III | 4 |
| Natural Science Elective |
3 |
| Social Science Lower-Level Elective |
3 |
| Track Elective |
3 |
| Credits Subtotal | 16.0 |
Spring | |
---|
DS 244 |
Data Acquisition and Manipulation | 3 |
MA 335 |
Introduction to Linear and Abstract Algebra | 3 |
MA 412 |
Probability and Statistics | 3 |
SE 300 |
Software Engineering Practices | 3 |
| Track Elective |
3 |
| Credits Subtotal | 15.0 |
Junior Year |
---|
Fall | |
---|
COM 221 |
Technical Report Writing | 3 |
or COM 222
|
Business Communication | |
CS 315 |
Data Structures and Analysis of Algorithms | 3 |
DS 312 |
Machine Learning | 3 |
| Natural Science with Lab Elective |
4 |
| Track Elective |
3 |
| Credits Subtotal | 16.0 |
Spring | |
---|
CI 460 |
Big Data Analytics and Machine Learning | 3 |
DS 317 |
Statistical Software | 3 |
DS 413 |
Statistics for Data Science | 3 |
| Humanities or Social Science Upper-Level Elective |
3 |
| Track Elective |
3 |
| Credits Subtotal | 15.0 |
Senior Year |
---|
Fall | |
---|
CS 317 |
Files and Database Systems | 3 |
DS 411 |
Data Visualization | 3 |
DS 483 |
Cloud Computing | 3 |
| Open Elective |
3 |
| Track Elective |
3 |
| Credits Subtotal | 15.0 |
Spring | |
---|
DS 490 |
Data Science Capstone | 3 |
| Open Elective |
3 |
| Social Science Upper-Level Elective |
3 |
| Humanities Upper-Level Elective |
3 |
| Credits Subtotal | 12.0 |
| Credits Total: | 120.0 |