Bachelor of Science in
Data Science
This interdisciplinary and comprehensive program provides students the experience of pairing a data science skillset with knowledge from a choice of specialized domains.
Data science is an exciting, rapidly developing technical field. Data scientists use machine learning and other computer techniques to extract insight and value from large data sets and communicate the results of their analysis clearly.
The Bachelor of Science in Data Science degree program is interdisciplinary and comprehensive. The curriculum centers around the computing techniques and mathematical skills necessary to be successful practitioners in this exciting field. The program emphasizes machine learning, handling of big data, communication skills and an area of concentration, such as business, aeronautical engineering, physics, global security and intelligence, meteorology, air traffic management, safety science and more. Graduates will be fully prepared to enter careers in data science, predictive analytics and applied statistics, or pursue advanced degrees.
Data scientists are creative problem solvers and effective communicators. Applicants should be interested in computing,
technology and applying quantitative methods to solve practical problems. A data scientist is an important member of a team, and so prospective students should have an interest in developing as collaborators and communicators, interfacing with both technical and nontechnical roles, and creating vivid narratives based on factual evidence.
Employment prospects for data scientists are very positive. Employers across all sectors recognize the impact data has on value. Data scientists are key contributors in aviation and aerospace, cybersecurity, business and supply chain management, finance, health care, government and more. The Bureau of Labor Statistics projects a 22% growth rate in Computer and Information Research Scientist jobs compared to 8% growth across all industries and reports a median salary of $126,830.
DETAILS
About Data Science at the Prescott, AZ Campus
Embry-Riddle is a great place to become a data scientist. Embry-Riddle has developed strategic strengths in select disciplines. At the Prescott campus, strengths include Aeronautical Engineering, Space Physics (where we have a LIGO collaborator group), Global Security and Intelligence Studies, Applied Meteorology, Air Traffic Management, Safety Science and more.
A data scientist creates value by possessing data analysis skills and being steeped in a particular domain. Embry-Riddle Prescott’s B.S. in Data Science degree offers students a unique educational experience of pairing the data science skillset with a depth of knowledge in one of our many specialized domains, collaborating side-by-side with faculty from each of these fields.
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
| Communication Theory and Skills | 9 | |
| Computer Science/Information Technology | 3 | |
| Mathematics | 6 | |
| Physical and Life Sciences (Natural Sciences) | 6 | |
| Humanities and Social Sciences | 12 | |
3 hours of Lower-Level Humanities | ||
3 hours of Lower-Level Social Science | ||
3 hours of Lower-Level or Upper-Level Humanities or Social Science | ||
3 hours of Upper-Level Humanities or Social Science | ||
| 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 |
| COM 219 | Speech # | 3 |
| COM 221 | Technical Report Writing # | 3 |
| or COM 222 | Business Communication | |
| 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 |
| General Education - Humanities Lower-Level Elective # | 3 | |
| General Education - Humanities Upper-Level Elective # | 3 | |
| General Education - Social Science Lower-Level Elective # | 3 | |
| General Education - Humanities or Social Science Upper-Level Elective # | 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 |
| Social Science Upper-Level Elective | 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)
| Track Electives: Choose five (5) electives from a single discipline, subject to program chair approval, including: | ||
| Business, Computer Science, Cyber Security, Economics, Intelligence, Math, Physics, or Psychology | 15 | |
Open Electives (6 Credits)
| Open Electives | 6 | |
| Total Credits | 121 | |
- *
Offered in Fall Only
- **
Offered in Spring Only
- #
General Education Courses
All Army ROTC students are required to complete SS 321 - U.S. Military History 1900-Present (3 credits) in order to commission.
Data Science - General
| 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 | |
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Summary
121 Credits
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