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 Certificates
Data Science Foundations
DSCI 511Data Acquisition and Pre-Processing3.0
DSCI 521Data Analysis and Interpretation3.0
Choose 2 courses from the following:6.0
Programming Foundations
Introduction to Programming
Systems Basics
Introduction to Software Design
Quantitative Foundations of Data Science
Foundations of Data and Information
Database Management Systems
Information Systems Analysis and Design
Information Visualization
Introduction to Data Analytics
Information Policy and Ethics (Machine Learning for Data Science Electives (option to complete a certificate))
Machine Learning for Data Science
DSCI 631Applied Machine Learning for Data Science3.0
DSCI 641Recommender Systems3.0
Choose 2 of the following:6.0
Programming Foundations
Introduction to Programming
Data Structures and Algorithms
Introduction to Computer Vision
Machine Learning
Applications of Machine Learning
Deep Learning
Quantitative Foundations of Data Science
Data Analysis and Interpretation
Modeling Natural Language
Natural Language Processing with Deep Learning
Social Network Analytics
Intelligent Search and Language Models
Required Core Course
DSCI 632Applied Cloud Computing3.0
Required Capstone Courses
DSCI 591Data Science Capstone I3.0
DSCI 592Data Science Capstone II3.0
Electives of Optional Third Certificate
Student can choose any combination of any courses listed below for a total of 45.0 credits (12.0 credits beyond the required 33.0 credits). If a student choose all courses listed in a single area, they may apply for a graduate certificate in that area. A degree student may receive a maximum of three certificates.12.0
Students can apply for a third (optional) certificate such as Big Data Analytics PBC, HCI/UX Research and Design PBC, and Applied AI/ML PBC.
A course cannot be used to satisfy multiple parts of the degree requirements.
Big Data Analytics (optional 3rd certificate)
Data Workflow Automation
Applied Cloud Computing
Choose 2 of the following:
Programming Foundations
Introduction to Programming
Data Structures and Algorithms
High Performance Computing
Deep Learning
Distributed Systems Software
Data Analysis at Scale
Introduction to the Digital Environment
Cloud Technology
Cloud Security and Virtual Environments
Quantitative Foundations of Data Science
Data Analysis and Interpretation
Natural Language Processing with Deep Learning
Database Management Systems
Applied Database Technologies
Social Network Analytics
Applied AI/ML (optional 3rd certificate)
Introduction to Programming
Programming Foundations
Applications of Machine Learning
Applied Artificial Intelligence
Choose 1 of the following:
Data Structures and Algorithms
Systems Basics
Quantitative Foundations of Data Science
Data Acquisition and Pre-Processing
Data Analysis and Interpretation
Applied Machine Learning for Data Science
Knowledge-based Systems
Explainable Artificial Intelligence
Human–Artificial Intelligence Interaction
HCI/UX Research and Design (optional 3rd certificate)
Information Innovation through Design Thinking
Design Problem Solving
Designing with Data
Understanding Users: User Experience Research Methods
Prototyping the User Experience
Additional appropriate graduate level (500-899) Data Science (DSCI), Information Systems (INFO), Computer Science (CS), Software Engineering (SE), or Artificial Intelligence, consulting with your advisor.
Optional Co-op 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

Plan of Study Grid
First Year
FallCredits
CS 570 Programming Foundations 3.0
DSCI 511 Data Acquisition and Pre-Processing 3.0
 Credits6
Winter
DSCI 521 Data Analysis and Interpretation 3.0
DS Foundations Elective 3.0
 Credits6
Spring
DSCI 631 Applied Machine Learning for Data Science 3.0
Certificate Course or Elective 3.0
 Credits6
Summer
Vacation  
 Credits0
Second Year
Fall
Machine Learning for DS Elective 3.0
Certificate Course or Elective 3.0
 Credits6
Winter
DSCI 632 Applied Cloud Computing 3.0
DS Foundation Elective 3.0
 Credits6
Spring
Machine Learning for DS Elective 3.0
Certificate Course or Elective 3.0
 Credits6
Summer
Vacation  
 Credits0
Third Year
Fall
DSCI 591 Data Science Capstone I 3.0
Certificate Course or Elective 3.0
 Credits6
Winter
DSCI 592 Data Science Capstone II 3.0
 Credits3
 Total Credits45

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

Plan of Study Grid
First Year
FallCredits
COOP 500 Career Management and Professional Development for Master's Degree Students 1.0
CS 570 Programming Foundations 3.0
DSCI 511 Data Acquisition and Pre-Processing 3.0
DS Foundations Elective 3.0
 Credits10
Winter
DSCI 521 Data Analysis and Interpretation 3.0
DS Foundations Elective 3.0
Machine Learning for DS Elective 3.0
 Credits9
Spring
DSCI 631 Applied Machine Learning for Data Science 3.0
Certificate Courses or Electives 6.0
 Credits9
Summer
DSCI 632 Applied Cloud Computing 3.0
DSCI 591 Data Science Capstone I 3.0
Certificate Course or Elective 3.0
 Credits9
Second Year
Fall
COOP EXPERIENCE  
 Credits0
Winter
COOP EXPERIENCE  
 Credits0
Spring
DSCI 592 Data Science Capstone II 3.0
Machine Learning for DS Elective 3.0
Certificate Course or Elective 3.0
 Credits9
 Total Credits46

Facilities

3675 Market Street

The School of Computer and Information Sciences (SCIS) is located at 3675 Market. Occupying three floors in the modern uCity Square building, SCIS'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 37th and Market Streets, 3675 Market acts as a physical nexus for our school, bridging academic campuses and medical centers to the east and south, the commercial corridors along Market Street and nearby 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
  • Adjacency to future public square
  • Access to Science Center’s nationally renowned business acceleration and technology commercialization programs

Drexel University Libraries

The Drexel University Libraries is a one-stop resource for all members of the Drexel community, providing access to millions of print and online books, journals, databases and other media, as well as hundreds of online course and research guides, workshops, and tutorials. Expert librarians offer a variety of consultation services virtually or in person, including help with course-related projects, strategies for finding and evaluating authoritative information, and approaches to utilizing, organizing, and presenting scholarship.

Students in SCIS also have access to the W. W. Hagerty Library where they can take advantage of the Libraries’ various learning environments, including group study rooms, collaborative and silent study areas, and 24/7 study space in the Dragons’ Learning Den. The Libraries also offers a wellness room, printing and scanning services, and laptops, portable power chargers, and other equipment you can borrow for use in the Library.

SCIS Commons

Located on the 10th floor of 3675 Market Street, the SCIS 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 our students. Community members have access to 3675 Market's fully equipped conference room with 42” displays and videoconferencing capabilities. The SCIS 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 SCIS Commons is reserved for all students taking SCIS courses.

The computers for general use are Microsoft Windows and Apple macOS 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 SCIS Commons and through the W.W. Hagerty Library. SCIS 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 SCIS Commons, student labs, and classrooms have access to networked databases, print and file resources, and the Internet via the University’s network. Email accounts, Internet and BannerWeb access are available through Drexel's Office of Information Resources and Technology.

Computer Support for Teaching

The SCIS server room houses a multitude of servers to support faculty research, staff operations, and student learning. Services provided include a Linux compute cluster which is open to all faculty, staff, and students, multiple virtualization environments to meet different needs of faculty, staff, and students, and other single-purpose servers to support various operations throughout the school. The compute cluster provides a common environment for students to develop software, which makes testing easier for the TAs and faculty. Our virtualization environments allow community members the flexibility of a cloud environment with local support and direct cost recovery options. For those who need dedicated hardware, we also support dedicated research systems.

Classrooms are outfitted with laser projectors, 4K displays, class capture hardware, and the Wolfvision Cynap. The Cynap controls the AV distribution throughout the room and can display up to 4 streams simultaneously. These include the local PC, a laptop connected directly to the podium, or up to 4 streaming devices. Windows, macOS, iOS, and Android devices can all connect wirelessly to the presentation system, allowing collaboration and freedom to roam the classroom for better interactivity. Wireless networking and outlets are also available for students throughout the classrooms. Laptops are available for checkout from the SCIS Commons desk.

Additionally, SCIS hosts and supports multiple Virtual Computing Lab environments for students to use that mimics our physical computer labs. This technology allows both online and face to face students to have the same experience when using computing facilities.

SCIS Virtual Environments

SCIS hosts a variety of virtual environments, which support all levels of research, academics, and administration. These include OpenStack, Proxmox VE, VMWare, and Xen architectures, backed by storage in CEPH. Multiple environments allow SCIS IT to provide researchers with the level of control appropriate for the project at hand and make efficient use of project funding. External cloud vendors such as AWS and Google Cloud Platform are also used when appropriate.

SCIS continues to invest in these virtual environments, and explores emerging environments, to continue to best support research and teaching. CPU cores, storage, and memory are added at every opportunity to these flexible, scalable environments. The current capacity of the system includes:

  • 1760 CPU Cores
  • 6 TB of Memory
  • Over 556 TB of HDD-backed storage
  • 122 TB of high-performance SSD-backed storage
  • 12 GPUs with room for expansion through funded research for high-performance computing needs

Cyber Learning Center

The Cyber Learning Center (CLC), located in 3675 Market Street's SCIS 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 across SCIS.

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

Research Laboratories

SCIS houses multiple research labs, led by SCIS 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), Security and Privacy Analytics Lab (SePAL), Software Engineering and Analytics Research (SOAR), Software Engineering Research Group (SERG), Social Computing Research Group, Vision and Cognition Laboratory (VisCog). For more information on these laboratories, please visit the our research web page.