Bachelor of Science in
Data Science

The B.S. in Data Science provides students the experience of pairing a data science skillset with knowledge from a choice of specialized domains.

Data science is a rapidly developing technical field where scientists can utilize 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 offers a comprehensive and innovative curriculum that prepares students to excel in the data-driven world. Students learn skills to collect, manage, interpret and analyze data to help make data-driven decisions.

The goal of the program is to educate and graduate professionals equipped for employment as data scientists or to continue their graduate school education. Through a project-based approach, and a strong emphasis on research and practical applications, students develop the skills and experience necessary to thrive in data science.

Why You Should Study this Degree

In today's data-driven world, the ability to analyze and interpret large volumes of data is increasingly critical across all sectors. A career in data science may be for you if you:

Desire a versatile skill set that allows for adaptation in various roles
Enjoy tackling complex problems and finding innovative solutions
Get excited about working with cutting-edge technology and tools, including artificial intelligence and machine learning
Have a passion for solving puzzles
Seek high-demand career opportunities
Thrive on asking questions and seeking answers

Student Learning Outcomes

While earning a data science degree, you will have the opportunity to:

Acquire data from various sources including public research, web pages and social media.
Convert unstructured and varied data into analysis-ready form.
Implement data storage across clusters of commodity hardware and cloud resources.
Use data to construct evidence-based solutions.
Use statistical theory and modern machine learning techniques to make predictions.

Data Science Career Opportunities

Careers and Employers

Embry-Riddle graduates often enter the workforce with top companies such as:

Amazon
Bank of America
Global Retail Companies
Google (Alphabet)
Goldman Sachs Group
Healthcare Providers
Major and Regional Airlines
Wells Fargo

Data Science degree graduates often secure roles as:

AI Specialist
Applied Statisticians
Data Analysts
Data Scientists
Financial Analysts
Market Research Analysts
Quantitative Analyst

Data Science Salary Information

Receiving a degree in data analysis from Embry-Riddle provides the opportunity for competitive salaries, averaging $108,020 annually as of 2023.

DETAILS

This offering is available at the following campuses. Select a campus to learn more.

Daytona Beach, FL Campus

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Daytona Beach, FL Campus

Prescott, AZ Campus

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Prescott, AZ Campus

About Data Science at the Daytona Beach, FL Campus

The B.S. in Data Science places an emphasis on applied data science research, fostering collaboration with industry partners and integrating it into the curriculum.

The data science degree focuses on a project-based learning approach, where students work on real-world data science projects, applied statistics and data analysis.

Embry‑Riddle offers a combined program that allows qualified students the chance to begin graduate work toward their Master of Science in Data Science from our Daytona Beach Campus while completing their B.S. in Data Science.

Data Science Information

Credits: 120
Online or In-Person: In-Person

Helpful Links

Tour our Daytona Beach Campus
Discover the College’s Faculty
Explore the Fields of Study: Computers & Technology, Applied Science and Business
Find Related Clubs & Organizations

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

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

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Summary

120 Credits

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About Data Science at the Prescott, AZ Campus

Data scientists are skilled professionals who possess expertise in analyzing data and have knowledge in a specific field. Embry-Riddle's Prescott Campus offers a Bachelor of Science in Data Science that provides a unique educational experience to students focusing on aerospace data analytics and visualization.

This program combines data analysis skills with in-depth knowledge in one of the many specialized domains the university has to offer. Students collaborate with faculty members from each field, gaining expertise in their chosen field, making them well-equipped to succeed in their future career as data scientists.

Data Science Information

Credits: 120
Online or In-Person: In-Person

Helpful Links

Tour our Prescott Campus
Discover the College's Faculty
Explore the Fields of Study: Computers & Technology, Applied Science and Business
Find Related Clubs & Organizations

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

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 - Social Science Lower-Level Elective ^#		3
General Education - Humanities or Social Science Lower or Upper-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
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

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