Master of Science in
Data Science
In the era of big data revolution, deriving insights from available data to support or optimize decisions is becoming a necessity in businesses, industries, and governments. Embry-Riddle’s Master of Science in Data Science (MSDS) is designed to provide students with the practical skills they need to lead the industry into the future. Whether you seek to enhance your marketability in the fields of safety, cybersecurity, business, homeland security or a broader area, the MSDS will prepare you to transform ubiquitous data into reliable, actionable decisions applying a combination of data understanding, analytical skills, and domain knowledge.
Embry-Riddle graduates are in demand in aviation & aerospace, as well as in business, cybersecurity, homeland security, safety and more. Interdisciplinary coursework, world-renowned faculty, and state-of-the-art labs prepare students to work collaboratively, and to employ the most powerful computational and analytic tools to meet the complex challenges of today. Five distinct tracks allow students to choose their specialization and build on current knowledge. Internships provide the opportunity to earn real-world experience outside the classroom, and a collaborative capstone project provides access to rea-world data sets and equips students to communicate findings effectively with data visualization.
DETAILS
About Data Science at the Daytona Beach, FL Campus
The Master of Science in Data Science degree program is designed to prepare students to use the latest computational and analytic tools to solve data intensive problems that arise in business, industry or government. The goal is to provide the students with the knowledge and skills of data collection, pre-processing, analysis, visualization and ethical implication associated with large heterogeneous data arising within the various domain areas.
All the core courses within the degree program are project-based allowing for hands-on experience culminating in a final capstone project or an internship in the student’s chosen domain area. The program offers the flexibility for the students to choose one of the five tracks (Aviation Safety, Aviation Business, High Performance Computing and Big Data, Cybersecurity and Homeland Security) to specialize in depending, on their interest.
Introduction
The Master of Science in Data Science degree program is designed to prepare students to use the latest computational and analytic tools to solve data intensive problems that arise in business, industry or government. The goal is to provide the students with the knowledge and skills of data collection, pre-processing, analysis, visualization and ethical implication associated with large heterogeneous data arising within the various domain areas.
All the core courses within the degree program are project-based allowing for hands-on experience culminating in a final capstone project or an internship in the student’s chosen domain area. The program offers the flexibility for the students to choose one of the five tracks (Aerospace Engineering, Aviation Safety, Aviation Business, Cybersecurity, High Performance Computing and Big Data, and Homeland Security) to specialize in, depending on their interest.The students have to choose the track by the start of the second semester into the program.
Degree Requirements
The curriculum consists of 15 credits of required coursework, with an additional 3 credits of track-specific required course and 12 credits of electives and/or thesis research.
The core courses provide the foundation of the Data Science principles and require an undergraduate degree in a technical field (a degree with at least four semesters of college-level Math) for preparation. Students with a non-technical undergraduate degree will be required to complete additional modules
| Program Core | ||
| CS 540 | Database and Data Retrieval | 3 |
| MA 506 | Probability and Statistical Inference | 3 |
| MA 540 | Data Mining | 3 |
| MA 544 | Data Visualization | 3 |
| MA 615 | Data Driven Modeling | 3 |
| Total Credits | 15 | |
Aerospace Engineering Track
| Required Courses | ||
| AE 514 | Introduction to the Finite Element Method | 3 |
| AE 516 | Computational Aeronautical Fluid Dynamics | 3 |
| AE 523 | Modeling and Simulation of Linear Dynamic Systems | 3 |
| Select one of the following | 3 | |
AE 5XX Aerospace Engineering Elective | ||
EP 501 | Numerical Methods for Engineers and Scientists | |
MA 532 | Numerical Linear Algebra for Engineers | |
| Total Credits | 12 | |
Aviation Business Track
| Required Course | ||
| BA 612 | Data Analytics for Aviation Business | 3 |
| Electives - Select 9 hrs from the following: | 9 | |
ACC 517 | Accounting for Decision Making | |
BA 511 | Operations Research | |
BA 523 | Advanced Aviation Economics | |
BA 610 | Airline Optimization and Simulation Systems | |
BA 645 | Airport Operations and Management | |
FIN 518 | Managerial Finance | |
| Total Credits | 12 | |
Aviation Safety Track
| Required Course | ||
| MSA 628 | Data Analytics for Aviation Safety | 3 |
| Electives - Select 9 hrs from the following: | 9 | |
MSA 516 | Applications in Crew Resource Management | |
MSA 545 | Human Factors in the Aviation/Aerospace Industry | |
MSA 611 | Aviation/Aerospace System Safety | |
MSA 621 | Aviation/Aerospace Safety Program Management | |
| Total Credits | 12 | |
Cybersecurity Track
| Required Course | ||
| CS 602 | Big Data Analytics for Cybersecurity | 3 |
| Electives - Select 9 hrs from the following: | 9 | |
CS 525 | Current Topics in Cybersecurity | |
CS 527 | System Exploitation and Penetration Testing | |
CS 528 | Multi-Agent Systems | |
CS 529 | Computer Security | |
CS 532 | Software Security Assessment | |
CS 538 | Applied Cryptography | |
| Total Credits | 12 | |
Homeland Security Track
| Required Course | ||
| HS 602 | Data Analytics for Counterterrorism | 3 |
| or HS 604 | Data Analytics for GIS | |
| Electives - Select 9 hrs from the following: | 9 | |
MHSR 501 The Internet, Security, and Governance | ||
MHSR 510 Introduction to Human Security | ||
MHSR 520 Principles of International Conflict Resolution | ||
MHSR 530 Environmental Security | ||
MHSR 540 Foundations of Resilience | ||
MSHR 615 | ||
| Total Credits | 12 | |
| Capstone Project or Thesis | 3 | |
MA 680 | Data Science Capstone Project | |
Track specific elective (Thesis) | ||
| Totals | 30 | |
| Year One | ||
|---|---|---|
| Credits | ||
| MA 506 | Probability and Statistical Inference | 3 |
| MA 544 | Data Visualization | 3 |
| CS 540 | Database and Data Retrieval | 3 |
| MA 540 | Data Mining | 3 |
| Specified Electives | 6 | |
| Credits Subtotal | 18.0 | |
| Year Two | ||
| MA 553 | High Performance Scientific Computing | 3 |
| MA 615 | Data Driven Modeling | 3 |
| MA 680 | Data Science Capstone Project | 3 |
| Specified Elective | 3 | |
| Credits Subtotal | 12.0 | |
| Credits Total: | 30.0 | |
High Performance Computing & Big Data Track
| Required Course | ||
| MA 553 | High Performance Scientific Computing | 3 |
| Electives- Select 9 hrs from the following: | 9 | |
MA 510 | Fundamentals of Optimization | |
MA 605 | Statistical Quality Analysis | |
MA 625 | Computing for Data Compression, Image and Signal Processing | |
MA 630 | Complex Networks and Applications | |
| Total Credits | 12 | |
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