Data Science

Major: Data Science
Degree Awarded: Bachelor of Science in Data Science (BSDS)
Calendar Type: Quarter
Total Credit Hours: 187.0
Co-op Options: Three Co-op (Five years); One Co-op (Four years)
Classification of Instructional Programs (CIP) code: 11.0401; 11.0501; 11.0802
Standard Occupational Classification (SOC) code:
15-1121; 15-1141

About the Program

The Bachelor of Science in Data Science (BSDS) prepares students to meet the challenges presented by the explosive growth of very large scale and complex data sources. The availability of data from sources such as business activities, social media and scientific instruments constantly creates new problems requiring data-driven solutions and opportunities and problems for innovation. BS in Data Science students develop the knowledge and skill to address these opportunities for the benefit of individuals and organizations. Students in the degree complete a minor, typically in business or the sciences, to provide knowledge and skill in a specific subject area to which data science techniques can be applied.

Data Science students learn to:

  • Define domain specific and context-relevant data analytics questions and hypotheses for individuals and organizations.
  • Select relevant data sources and transform data suitable for solving data analytics problems.
  • Identify appropriate techniques and tools for acquiring, retrieving, analyzing, and making use of the data. 
  • Apply data analytics techniques and skills to build analytical and predictive models for answering data science questions.
  • Create visualizations and communicate data analytics results to stakeholders and decision makers.  
  • Assess the necessary skills arising from the interdisciplinary nature of data science as a combination of hacking skills, analytical techniques, and domain knowledge.

The degrees in Computing and Security Technology, Data Science, and Information Systems share a common first year. This allows students to easily switch among the degrees early in their studies. In addition, some of the electives in each degree are accessible to students in the other two majors; this provides a deeper and broader set of advanced topics for students in all three majors.

Additional Information

For more information about this program, please visit the BS in Data Science web page on the College of Computing & Informatics' website.

Degree Requirements

Data Science Requirements
INFO 101Introduction to Computing and Security Technology3.0
INFO 102Introduction to Information Systems3.0
INFO 103Introduction to Data Science3.0
INFO 200Systems Analysis I3.0
INFO 202Data Curation3.0
INFO 210Database Management Systems3.0
or CS 461 Database Systems
INFO 212Data Science Programming I3.0
INFO 213Data Science Programming II3.0
INFO 215Social Aspects of Information Systems3.0
INFO 250Information Visualization3.0
INFO 300Information Retrieval Systems3.0
INFO 323Cloud Computing and Big Data 3.0
INFO 332Exploratory Data Analytics 3.0
INFO 371Data Mining Applications3.0
INFO 432Advanced Data Analytics 3.0
INFO 440Social Media Data Analysis3.0
INFO 442Data Science Projects 3.0
CCI Electives6.0
Select 2 CCI courses that are at 200 or above level and not otherwise required
Data Science Electives6.0
Select 2 of the following courses:
Web and Mobile App Development
Artificial Intelligence
Machine Learning
Advanced Database Management Systems
Visual Analytics
Systems Analysis II
Software Project Management
Computing and Informatics Requirements
CI 101Computing and Informatics Design I2.0
CI 102Computing and Informatics Design II2.0
CI 103Computing and Informatics Design III2.0
CI 491 [WI] Senior Project I3.0
CI 492 [WI] Senior Project II3.0
CI 493 [WI] Senior Project III3.0
Introductory Programming
INFO 151Web Systems and Services I3.0
CS 171Computer Programming I3.0
CS 172Computer Programming II3.0
Mathematics Requirements
Select one of the following sequences:12.0
Introduction to Analysis I
and Introduction to Analysis II
and Discrete Computational Structures
Calculus I
and Calculus II
and Discrete Computational Structures
Statistics Requirements
STAT 201Introduction to Business Statistics4.0
STAT 202Business Statistics II4.0
Natural Science Requirements
Science electives: Select from ANAT, BIO, CHEM, ENVS, FDSC, NFS, PHEV, PHYS. Courses from other departments may be considered with advisor approval.8.0
Behavioral and Social Science Requirements
PSY 101General Psychology I3.0
PSY 330Cognitive Psychology3.0
Arts and Humanities Requirements
ENGL 101Composition and Rhetoric I: Inquiry and Exploratory Research3.0
or ENGL 111 English Composition I
ENGL 102Composition and Rhetoric II: Advanced Research and Evidence-Based Writing3.0
or ENGL 112 English Composition II
ENGL 103Composition and Rhetoric III: Themes and Genres3.0
or ENGL 113 English Composition III
COM 230Techniques of Speaking3.0
or COM 310 Technical Communication
University and College Requirements
CIVC 101Introduction to Civic Engagement1.0
COOP 101Career Management and Professional Development **1.0
UNIV CI101The Drexel Experience2.0
or CI 120 CCI Transfer Student Seminar
Minor Requirements *24.0
Choose a minor in a data science application area including business and natural science
Free Electives26.0
Total Credits187.0

Writing-Intensive Course Requirements

In order to graduate, all students must pass three writing-intensive courses after their freshman year. Two writing-intensive courses must be in a student's major. The third can be in any discipline. Students are advised to take one writing-intensive class each year, beginning with the sophomore year, and to avoid “clustering” these courses near the end of their matriculation. Transfer students need to meet with an academic advisor to review the number of writing-intensive courses required to graduate.

A "WI" next to a course in this catalog may indicate that this course can fulfill a writing-intensive requirement. For the most up-to-date list of writing-intensive courses being offered, students should check the Writing Intensive Course List at the University Writing Program. Students scheduling their courses can also conduct a search for courses with the attribute "WI" to bring up a list of all writing-intensive courses available that term.

Sample Plan of Study

5 year, 3 co-op

First Year
CI 1012.0CI 1022.0CI 1032.0VACATION
ENGL 101 or 1113.0CIVC 1011.0CS 1723.0 
INFO 1013.0COOP 101*1.0ENGL 103 or 1133.0 
INFO 1513.0CS 1713.0INFO 1033.0 
MATH 101 or 1214.0ENGL 102 or 1123.0MATH 1804.0 
UNIV CI1011.0INFO 1023.0UNIV CI1011.0 
 MATH 102 or 1224.0  
 16 17 16 0
Second Year
  INFO 2023.0INFO 2153.0
  INFO 210 or CS 4613.0INFO 2503.0
  INFO 2123.0PSY 1013.0
  STAT 2014.0STAT 2024.0
 0 0 16 16
Third Year
  INFO 3003.0PSY 3303.0
  INFO 3233.0Data Science Elective3.0
  INFO 3713.0Free Elective3.0
  Science Elective4.0Science Elective4.0
 0 0 16 16
Fourth Year
  INFO 4403.0Free Elective3.0
  Data Science Elective3.0Minor Electives6.0
  Minor Elective3.0CCI Elective3.0
  Free Elective3.0 
 0 0 15 15
Fifth Year
CI 4913.0CI 4923.0CI 4933.0 
Free Electives5.0CCI Elective3.0Free Electives6.0 
Minor Electives6.0Free Electives6.0Minor Electives6.0 
 Minor Elective3.0  
 14 15 15 
Total Credits 187

4 year, one co-op

First Year
CI 1012.0CI 1022.0CI 1032.0VACATION
ENGL 101 or 1113.0CIVC 1011.0CS 1723.0 
INFO 1013.0CS 1713.0ENGL 103 or 1133.0 
INFO 1513.0ENGL 102 or 1123.0INFO 1033.0 
MATH 101 or 1214.0INFO 1023.0MATH 1804.0 
UNIV CI1011.0MATH 102 or 1224.0UNIV CI1011.0 
 16 16 16 0
Second Year
COOP 101*1.0INFO 2133.0COM 230 or 3103.0INFO 3323.0
INFO 2003.0INFO 2153.0INFO 3003.0PSY 3303.0
INFO 2023.0INFO 2503.0INFO 3233.0Data Science Elective3.0
INFO 210 or CS 4613.0PSY 1013.0INFO 3713.0Free Elective3.0
INFO 2123.0STAT 2024.0Science Elective4.0Science Elective4.0
STAT 2014.0   
 17 16 16 16
Third Year
  INFO 4403.0CCI Elective3.0
  Data Science elective3.0Free Elective3.0
  Minor Elective3.0Minor Elective6.0
  Free Elective3.0 
 0 0 15 15
Fourth Year
CI 4913.0CI 4923.0CI 4933.0 
Free Electives5.0CCI Elective3.0Free Electives6.0 
Minor Electives6.0Free Electives6.0Minor Electives6.0 
 Minor Electives3.0  
 14 15 15 
Total Credits 187

Co-op/Career Opportunities

Co-Op Options

Two co-op options are available for this program:

  • 5-year/3 co-op
  • 4-year/1 co-op

Career Opportunities

The new data science major provides valuable skills that can be transported to a number of job settings. The demand for graduates with data science knowledge is strong, and employers often want evidence of additional communication and problem-solving skills that can be applicable to specific disciplines. Data science program graduates could potentially serve as key members of organizational data science teams able to create novel information products, with an emphasis on solving problems that can only be addressed using large and disparate data sources. The program is also an excellent preparation for graduate study in data science.

Sample job titles for data science graduates include:

  • Data Scientist
  • Business Intelligence Officer
  • Information Architect
  • Usability Analyst

Visit the Drexel Steinbright Career Development Center page for more detailed information on co-op and post-graduate opportunities.

3675 Market Street

In March 2019, the College of Computing & Informatics relocated to 3675 Market. For the first time in the College's history, all CCI faculty, students and professional staff are housed under one roof. Occupying two floors in the brand new uCity Square building, CCI's new home offers state-of-the-art technology in our classrooms, labs, meeting areas and collaboration spaces. 3675 Market offers Class A laboratory, office, coworking, and convening spaces. In fall 2019, the College will open a third floor which will include additional offices, classrooms, a research lab, a maker space, and a ground-breaking DXC Technology Innovation Lab. Located at the intersection of Market Street and 37th Street, 3675 Market will act 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 Rational SEED Program which provides cutting-edge software development and project management software for usage in the CCI Commons and CCI classrooms. The College is also a member of the Microsoft Academic Alliance known also as “DreamSpark” 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 (CCILC), located in 3675 Market Street's CCI Commons student computer lab, provides consulting and other learning resources for students taking computer science classes. The CCILC is staffed by graduate and undergraduate computer science students from the College of Computing & Informatics.

The CCILC 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 Drexel Health and Risk Communication Lab, Interactive Systems for Healthcare, Socio-Technical Studies Group, Intelligent Information & Knowledge Computing Research Lab, Evidence-based Decision Making Lab, Applied Symbolic Computation Laboratory (ASYM), High Performance Computing Laboratory (SPIRAL), Drexel Research on Play (RePlay) Laboratory, 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.


The College of Computing & Informatics works continually to improve its degree programs. As part of this effort, the Data Science degree is evaluated relative to the following Objectives and Outcomes.

BS Data Science Program Educational Objectives

Within three to five years of graduation, alumni of the program are expected to achieve one or more of the following milestones:

  • Be valued contributors to private or public organizations as demonstrated by promotions, increased responsibility, or other professional recognition
  • Contribute to professional knowledge as demonstrated by published papers, technical reports, patents, or conference presentations
  • Succeed in continuing professional development as demonstrated by completion of graduate studies or professional certifications
  • Display commitment and leadership within the professional and community as demonstrated by contributions towards society's greater good and prosperity.

BS Data Science Program Student Outcomes

The program enables students to attain, by the time of graduation

  • An ability to apply knowledge of computing and mathematics appropriate to the discipline
  • An ability to analyze a problem, and identify and define the computing requirements appropriate to its solution
  • An ability to design, implement, and evaluate a computer-based system, process, component, or program to meet desired needs
  • An ability to function effectively on teams to accomplish a common goal
  • An understanding of professional, ethical, legal, security and social issues
  • An ability to communicate effectively with a range of audiences
  • An ability to analyze the local and global impact of computing on individuals, organizations, and society
  • Recognition of the need for and an ability to engage in continuing professional development
  • An ability to use current techniques, skills, and tools necessary for computing practice

Information Science Faculty

Denise E. Agosto, PhD (Rutgers, The State University of New Jersey). Professor. Youth information behaviors, public libraries, multicultural issues in youth library services, and qualitative research methods.
Yuan An, PhD (University of Toronto, Canada) Director of International Programs. Associate Professor. Conceptual modeling, schema and ontology mapping, information integration, knowledge representation, requirements engineering, healthcare information systems, semantic web.
Ellen Bass, PhD (Georgia Institute of Technology) Joint Appointment with the College of Nursing and Health Professions. Professor. Characterizing human judgement and decision making, modeling human judgement when supported by information automation, computational models of human-human and human-automation coordination.
Christopher Carroll, MS (Drexel University) BSCST Program Director. Associate Teaching Professor. Information technology within healthcare companies, computer networking and design, IT infrastructure, server technology, information security, virtualization and cloud computing.
Chaomei Chen, PhD (University of Liverpool). Professor. Information visualization, visual analytics, knowledge domain visualization, network analysis and modeling, scientific discovery, science mapping, scientometrics, citation analysis, human-computer interaction.
Michael Chu, MSE (University of Pennsylvania). Associate Teaching Professor. System, server, computer networking and design; IT infrastructure; information technology management and security; Web system programming; database and mobile application development.
Catherine D. Collins, MLIS (Indiana University). Associate Teaching Professor. Knowledge management, collection development, management of information organizations, information sources and services, international development.
M. Carl Drott, PhD (University of Michigan). Associate Professor. Systems analysis techniques, web usage, competitive intelligence.
Andrea Forte, PhD (Georgia Institute of Technology) PhD Program Director, and MS in Information Program Director. Associate Professor. Social computing, human-computer interaction, computer-supported cooperative work, computer-supported collaborative learning, information literacy.
Susan Gasson, PhD (University of Warwick). Associate Professor. The co-design of business and IT-systems, distributed cognition & knowledge management in boundary-spanning groups, human-centered design, social informatics, online learning communities, grounded theory.
Tim Gorichanaz, PhD (Drexel University). Assistant Teaching Professor. Human information behavior, human-centered computing, neo-documentation studies, and information ethics.
Jane Greenberg, PhD (University of Pittsburgh) Alice B. Kroeger Professor. Metadata, ontological engineering, data science, knowledge organization, information retrieval
Peter Grillo, PhD (Temple University) Associate Department Head for Undergraduate Affairs, Information Science. Teaching Professor. Strategic applications of technology within organizations.
Thomas Heverin, PhD (Drexel University). Associate Teaching Professor. Computer security, ethical hacking, computer forensics, network forensics, cloud security and cybersecurity.
Gregory W. Hislop, PhD (Drexel University). Professor. Information technology for teaching and learning, online education, structure and organization of the information disciplines, computing education research, software evaluation and characterization.
Xiaohua Tony Hu, PhD (University of Regina, Canada). Professor. Data mining, text mining, Web searching and mining, information retrieval, bioinformatics and healthcare informatics.
Weimao Ke, PhD (University of North Carolina at Chapel Hill). Associate Professor. Information retrieval (IR), distributed systems, intelligent filtering/recommendation, information visualization, network science, complex systems, machine learning, text/data mining, multi-agent systems, the notion of information.
Xia Lin, PhD (University of Maryland) Department Head, Information Science. Professor. Digital libraries, information visualization, visual interface design, knowledge mapping, human-computer interaction, object-oriented programming, information retrieval, information architecture, information-seeking behaviors in digital environments.
Danuta A. Nitecki, PhD (University of Maryland at College Park) Dean of Libraries. Professor. Library metrics and use in management, library as place, and academic library service models.
Jung-ran Park, PhD (University of Hawaii at Manoa). Associate Professor. Knowledge organization and representation, metadata, computer-mediated communication, cross-cultural communication, multilingual information access.
Alex Poole, PhD (University of North Carolina). Assistant Professor. Digital curation, archives and records management, digital humanities, and diversity, inclusivity, and equity.
Lori Richards, PhD (University of North Carolina). Assistant Professor. Archives, digital curation, electronic records management, information technology and digital collections, cloud computing and record keeping, management of information organizations.
Michelle L. Rogers, PhD (University of Wisconsin-Madison). Associate Professor. Human-computer interaction, healthcare informatics, human factors engineering, socio-technical systems, health services research, patient safety.
Aleksandra Sarcevic, PhD (Rutgers University). Associate Professor. Computer-supported cooperative work, human-computer interaction, and healthcare informatics.
Bupesh Shetty, PhD (University of Iowa). Assistant Teaching Professor. Process pattern mining, data mining, operations management, sports analytics, information systems, and machine learning applications.
Brian Smith, PhD (Northwestern University) Senior Associate Dean of Academic Affairs. Professor. Design of computer-based learning environments, computer science education, human-computer interaction, creativity and innovation; design sciences; informal/everyday learning.
Il-Yeol Song, PhD (Louisiana State University). Professor. Conceptual modeling, ontology and patterns, data warehouse and OLAP, object-oriented analysis and design with UML, medical and bioinformatics data modeling & integration,.
Rosina Weber, PhD (Federal University of Santa Catarina). Associate Professor. Case-based reasoning, explainable artificial intelligence, machine learning, textual analytics, natural language understanding, language models, recommender systems, technological aspects of knowledge management, project management, and requirements engineering.
Jake Williams, PhD (University of Vermont). Assistant Professor. Data science, scientific programming, computational social science, computational linguistics and natural language processing, mathematics, machine learning, algorithms, and scalability.
Erjia Yan, PhD (Indiana University Bloomington). Assistant Professor. Network science, information analysis and retrieval, scholarly communication methods and applications.
Christopher C. Yang, PhD (University of Arizona, Tucson). Professor. Web search and mining, security informatics, knowledge management, social media analytics, cross-lingual information retrieval, text summarization, multimedia retrieval, information visualization, information sharing and privacy, artificial intelligence, digital library, and electronic commerce.

Emeritus Faculty

Michael E. Atwood, PhD (University of Colorado). Professor Emeritus. Human-computer interaction, computer-supported cooperative work, organizational memory.
Thomas A. Childers, PhD (Rutgers University). Professor Emeritus. Measurement, evaluation, and planning of information and library services, the effectiveness of information organizations.
David E. Fenske, PhD (University of Wisconsin-Madison). Dean Emeritus and Professor. Digital libraries, informatics, knowledge management and information technologies.
Linda Marion, PhD (Drexel University). Teaching Professor Emerita. Formal and informal communication, bibliometric studies of scholarly communication, diffusion of information, information use in the social sciences, academic and public libraries, information science education.
Katherine W. McCain, PhD (Drexel University). Professor Emeritus. Scholarly communication, information production and use in the research process, development and structure of scientific specialties, diffusion of innovation, bibliometrics, evaluation of information retrieval systems.
Carol Hansen Montgomery, PhD (Drexel University) Dean of Libraries Emeritus. Research Professor. Selection and use of electronic collections, evaluation of library and information systems, digital libraries, economics of libraries and digital collections.
Delia Neuman, PhD (The Ohio State University). Professor Emerita. Learning in information-rich environments, instructional systems design, the use of media for learning, and school library media.
Gerry Stahl, PhD (University of Colorado). Professor Emeritus. Human-computer interaction, computer-supported cooperative work, computer-supported collaborative learning, theory of collaboration.
Howard D. White, PhD (University of California at Berkeley). Professor Emeritus. Literature information systems, bibliometrics, research methods, collection development, online searching.
Susan Wiedenbeck, PhD (University of Pittsburgh). Professor Emeritus. Human-computer interaction, end-user programming/end-user development, empirical studies of programmers, interface design and evaluation.
Valerie Ann Yonker, PhD (Drexel University). Associate Teaching Professor Emerita. Human service information systems, systems analysis and design, measurement in software evaluation, knowledge engineering.
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