B.S. in Data Science

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 Skills9
Computer Science/Information Technology3
Mathematics6
Physical and Life Sciences (Natural Sciences)6
Humanities and Social Sciences12
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 Credits36

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 460Big Data Analytics and Machine Learning *3
COM 122English Composition #3
CS 118Fundamentals of Computer Programming #3
CS 125Computer Science I4
CS 315Data Structures and Analysis of Algorithms *3
CS 317Files and Database Systems *3
DS 150Data Science I: Introduction3
DS 151Data Science II: Foundations3
DS 244Data Acquisition and Manipulation3
DS 312Machine Learning3
DS 317Statistical Software3
DS 411Data Visualization3
DS 413Statistics for Data Science3
DS 483Cloud Computing3
DS 490Data Science Capstone3
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 225Introduction to Discrete Structures3
MA 241Calculus and Analytical Geometry I #4
MA 242Calculus and Analytical Geometry II #4
MA 243Calculus and Analytical Geometry III4
MA 335Introduction to Linear and Abstract Algebra **3
MA 412Probability and Statistics3
SE 300Software Engineering Practices **3
Social Science Upper-Level Elective3
UNIV 101College Success1
Total Credits92

Natural Science (with one lab attached to course) choose two (8 credits)

BIO 120
& BIO 120L		Foundations of Biology I
and Foundations of Biology I Laboratory #		4
BIO 121
& BIO 121L		Foundations of Biology II
and Foundations of Biology II Lab #		4
CHM 110
& CHM 110L		General Chemistry I
and General Chemistry I Laboratory #		4
CHM 111
& CHM 111L		General Chemistry II
and General Chemistry II Laboratory #		4
PS 161Physics 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 Psychology15

Open Electives (6 Credits)

Open Electives6
Total Credits121
*

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
FallCredits
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 Subtotal14.0
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 Subtotal17.0
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 Subtotal16.0
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 Subtotal15.0
Junior Year
Fall
COM 221 Technical Report Writing 3
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 Subtotal16.0
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 Subtotal15.0
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 Subtotal15.0
Spring
DS 490 Data Science Capstone 3
Open Elective 3
Social Science Upper-Level Elective 3
Humanities Upper-Level Elective 3
 Credits Subtotal12.0
 Credits Total: 120.0