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 511 | Data Acquisition and Pre-Processing | 3.0 |
DSCI 521 | Data Analysis and Interpretation | 3.0 |
DSCI 631 | Applied Machine Learning for Data Science | 3.0 |
DSCI 632 | Applied Cloud Computing | 3.0 |
Required Capstone Courses | ||
DSCI 591 | Data Science Capstone I | 3.0 |
DSCI 592 | Data Science Capstone II | 3.0 |
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 | ||
or CS 570 | 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 Elective | 3.0 | |
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 Electives | 9.0 | |
Choose 3 of the following: | ||
Introduction to the Digital Environment | ||
Disaster Recovery, Continuity Planning and Digital Risk Assessment | ||
Privacy | ||
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 Credits | 45.0-46.0 |
- *
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 | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
CS 570 | 3.0 | DSCI 521 | 3.0 | DSCI 631 | 3.0 | Vacation | |
DSCI 511 | 3.0 | Foundational Elective | 3.0 | Machine Learning for DS Elective | 3.0 | ||
6 | 6 | 6 | 0 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
Data Engineering Elective | 3.0 | DSCI 632 | 3.0 | Foundational Elective | 3.0 | Vacation | |
Elective | 3.0 | Machine Learning for DS Elective | 3.0 | Big Data Analytics Elective | 3.0 | ||
6 | 6 | 6 | 0 | ||||
Third Year | |||||||
Fall | Credits | Winter | Credits | ||||
DSCI 591 | 3.0 | DSCI 592 | 3.0 | ||||
Elective | 3.0 | ||||||
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 | |||||||
---|---|---|---|---|---|---|---|
Fall | Credits | Winter | Credits | Spring | Credits | Summer | Credits |
CS 570 | 3.0 | DSCI 521 | 3.0 | DSCI 631 | 3.0 | Co-op Experience | |
DSCI 511 | 3.0 | Foundational Elective | 3.0 | INFO 615 | 3.0 | ||
Foundational Elective | 3.0 | Machine Learning for DS Elective | 3.0 | Big Data Analytics Elective | 3.0 | ||
COOP 500 | 1.0 | ||||||
10 | 9 | 9 | 0 | ||||
Second Year | |||||||
Fall | Credits | Winter | Credits | Spring | Credits | ||
Co-op Experience | DSCI 632 | 3.0 | DSCI 592 | 3.0 | |||
DSCI 591 | 3.0 | Machine Learning for DS Elective | 3.0 | ||||
Data Engineering Elective | 3.0 | Elective | 3.0 | ||||
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.
Facilities
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 library.drexel.edu 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.