A student analyzing computers displaying data

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 21% growth in Computer and Information Research Scientist jobs from 2021 to 2031, much greater than the average for all occupations.

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.

Learn more about the Prescott, AZ Campus

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
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 - Communications Elective ^#		6
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

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
	Communications Elective	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
	Communications Elective	3
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

For course descriptions, education requirements, curriculum, and suggested plan of study, please see the Bachelor's Degree in Data Science in the Prescott Catalog.