Data Science MSDS

Major: Data Science
Degree Awarded: Master of Science in Data Science (MSDS)
Calendar Type: Quarter
Minimum Required Credits: 45.0
Co-op Option: Graduate Co-op
Classification of Instructional Programs (CIP) code: 30.7001
Standard Occupational Classification (SOC) code: 15-1111

About the Program

The Master of Science in Data Science program provides a strong foundation in the emerging area of data science with foci on data management and accountability, visualization and communication, and computational, algorithmic, and applied processing techniques. Students gain competency in fundamental methods and techniques for data collection, management, analysis, and result interpretation. Their fundamental understanding and skills will be applied to real-world data analysis tasks through state-of-the-art technologies, tools, and platforms.

A graduate co-op is available; for more information, visit the Steinbright Career Development Center's website.

Admission Requirements

The Master of Science in Data Science accepts applicants who hold a bachelor's degree from an accredited university. Please visit the College of Computing & Informatics website for more information on admission requirements. 

Additional Information

For more information, please visit the College of Computing & Informatics (CCI) website.

Degree Requirements

Required Core Courses
DSCI 511Data Acquisition and Pre-Processing3.0
DSCI 521Data Analysis and Interpretation3.0
DSCI 631Applied Machine Learning for Data Science3.0
DSCI 632Applied Cloud Computing3.0
Required Capstone Courses
DSCI 591Data Science Capstone I3.0
DSCI 592Data Science Capstone II3.0
Foundational Electives (option to complete a certificate)6.0
Choose 2 of the following:
Quantitative Foundations of Data Science
Information Systems Analysis and Design
Information Visualization
Understanding Users: User Experience Research Methods
Information Policy and Ethics
Systems Basics
Introduction to Programming
Programming Foundations
Introduction to Software Design
Machine Learning for Data Science Elecitve (option to complete a certificate)6.0
Choose 2 of the following:
Recommender Systems for Data Science
Natural Language Processing with Deep Learning
Introduction to Computer Vision
Machine Learning
Applications of Machine Learning
Deep Learning
Cognitive Systems
Big Data Analytics Elective (option to complete a certificate)3.0
Choose 1 of the following:
High Performance Computing
Distributed Systems Software
Data Analysis at Scale
Cloud Technology
Cloud Security and Virtual Environments
Social Network Analytics
Data Engineering Elective3.0
Choose 1 of the following:
Fundamentals of Databases
Data Structures and Algorithms
Data Structures and Algorithms I
Foundations of Data and Information
Data and Digital Stewardship
Database Management Systems
Advanced Database Management
Applied Database Technologies
Designing with Data
Information Retrieval Systems
Information Systems Management
Metadata and Resource Description
Information Assurance
Security Engineering
General Electives9.0
Choose 3 of the following:
Introduction to the Digital Environment
Disaster Recovery, Continuity Planning and Digital Risk Assessment
Parallel Programming
Information Innovation through Design Thinking
Principles of Cybersecurity
Perspectives on Information Systems
Human-Computer Interaction
Knowledge-based Systems
Applied Artificial Intelligence
Understanding Users: User Experience Research Methods
Prototyping the User Experience
Explainable Artificial Intelligence
Human–Artificial Intelligence Interaction
The above elective areas not used to fulfill the concentration requirement
Additional appropriate graduate level (500-899) Computer Science, Software Engineering, or Artificial Intelligence courses with subject codes CS and SE, consulting with your advisor
Up to 2 appropriate graduate-level (500-899) computing-related courses outside of Computer Science, Software Engineering, and Artificial Intelligence approved by the College
Optional Coop Experience *0-1
Career Management and Professional Development for Master's Degree Students
Total Credits45.0-46.0

Co-op is an option for this degree for full-time on-campus students. To prepare for the 6-month co-op experience, students will complete COOP 500. The total credits required for this degree with the co-op experience is 46.0

Students not participating in the co-op experience will need 45.0 credits to graduate.

Sample Plan of Study

Part-time, no co-op

First Year
CS 5703.0DSCI 5213.0DSCI 6313.0Vacation
DSCI 5113.0Foundational Elective3.0Machine Learning for DS Elective3.0 
 6 6 6 0
Second Year
Data Engineering Elective3.0DSCI 6323.0Foundational Elective3.0Vacation
Elective3.0Machine Learning for DS Elective3.0Big Data Analytics Elective3.0 
 6 6 6 0
Third Year
DSCI 5913.0DSCI 5923.0  
 6 3  
Total Credits 45

Note: Third Year Winter is less than the 4.5-credit minimum required (considered half-time status) of graduate programs to be considered financial aid eligible. As a result, aid will not be disbursed to students this term

Full-time with co-op

First Year
CS 5703.0DSCI 5213.0DSCI 6313.0Co-op Experience
DSCI 5113.0Foundational Elective3.0INFO 6153.0 
Foundational Elective3.0Machine Learning for DS Elective3.0Big Data Analytics Elective3.0 
COOP 5001.0   
 10 9 9 0
Second Year
Co-op ExperienceDSCI 6323.0DSCI 5923.0 
 DSCI 5913.0Machine Learning for DS Elective3.0 
 Data Engineering Elective3.0Elective3.0 
 0 9 9 
Total Credits 46

Note: Second Year Summer is less than the 4.5-credit minimum required (considered half-time status) of graduate programs to be considered financial aid eligible. As a result, aid will not be disbursed to students this term.


3675 Market Street

The College of Computing & Informatics is located at 3675 Market. Occupying three floors in the modern uCity Square building, CCI's home offers state-of-the-art technology in our classrooms, research labs, offices, meeting areas and collaboration spaces. 3675 Market offers Class A laboratory, office, coworking, and convening spaces. Located at the intersection of Market Street and 37th Street, 3675 Market acts as a physical nexus, bridging academic campuses and medical centers to the east and south, the commercial corridors along Market Street and Chestnut Street, and the residential communities to the north and west.

The uCity Square building offers:

  • Speculative lab/office space
  • World-class facilities operated by CIC
  • Café/restaurant on-site
  • Quorum, a two-story, 15K SF convening space and conference center
  • Adjacent to future public square
  • Access to Science Center’s nationally renowned business acceleration and technology commercialization programs

Drexel University Libraries

Drexel University Libraries is a learning enterprise, advancing the University’s academic mission through serving as educators, supporting education and research, collaborating with researchers, and fostering intentional learning outside of the classroom. Drexel University Libraries engages with Drexel communities through three physical locations, including W. W. Hagerty Library, Queen Lane Library, and the Library Learning Terrace, as well as a vibrant online presence which sees, on average, over 8,000 visits per day. In the W.W. Hagerty Library location, College of Computing & Informatics students have access to private study rooms and nearly half a million books, periodicals, DVDs, videos and University Archives. All fields of inquiry are covered, including: library and information science, computer science, software engineering, health informatics, information systems, and computing technology. Resources are available online at or in-person at W. W. Hagerty Library.

The Libraries also make available laptop and desktop PC and Mac computers, printers and scanners, spaces for quiet work or group projects and designated 24/7 spaces. Librarians and library staff—including a liaison librarian for computing and informatics—are available for individual research consultations and to answer questions about materials or services.

CCI Commons

Located on the 10th floor of 3675 Market Street, the CCI Commons is an open lab and collaborative work environment for students. It features desktop computers, a wireless/laptop area, free black and white printing, and more collaborative space for its students. Students have access to 3675 Market's fully equipped conference room with 42” displays and videoconferencing capabilities. The CCI Commons provides technical support to students, faculty, and professional staff. In addition, the staff provides audio-visual support for all presentation classrooms within 3675 Market. Use of the CCI Commons is reserved for all students taking CCI courses.

The computers for general use are Microsoft Windows and Macintosh OSX machines with appropriate applications which include the Microsoft Office suite, various database management systems, modeling tools, and statistical analysis software. Library-related resources may be accessed at the CCI Commons and through the W.W. Hagerty Library. The College is a member of the "Azure Dev Tools for Teaching” platform that allows students free access to a wide array of Microsoft software titles and operating systems.

The CCI Commons, student labs, and classrooms have access to networked databases, print and file resources within the College, and the Internet via the University’s network. Email accounts, Internet and BannerWeb access are available through the Office of Information Resources and Technology.

CCI Learning Center

The CCI Learning Center (CLC), located in 3675 Market Street's CCI Commons student computer lab, provides consulting and other learning resources for students taking courses offered by the Computer Science Department. The CLC is staffed by graduate and undergraduate computer science students from the College of Computing & Informatics.

The CLC and CCI Commons serve as a central hub for small group work, student meetings, and TA assistance. 

Research Laboratories

The College houses multiple research labs, led by CCI faculty, in 3675 Market Street including: the Metadata Research Center (MRC), Interactive Systems for Healthcare (IS4H) Research, Economics and Computation (EconCS), The TeX-Base Lab, SPiking And Recurrent SoftwarE (SPARSE) Coding, Human-System Evaluation and Analysis Lab (H-SEAL), Applied Symbolic Computation Laboratory (ASYM), Software Engineering Research Group (SERG), Social Computing Research Group, Vision and Cognition Laboratory (VisCog) and the Vision and Graphics Laboratory. For more information on these laboratories, please visit the College’s research web page.

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