Data Science

Major: Data Science
Degree Awarded: Bachelor of Science in Data Science (BSDS)
Calendar Type: Quarter
Total Credit Hours: 188.0
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 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 a large audience 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 and 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 Information Technology3.0
INFO 105Introduction to Informatics3.0
INFO 108Foundations of Software3.0
INFO 151Web Systems and Services I3.0
INFO 152Web Systems and Services II3.0
INFO 153Applied Data Management3.0
INFO 154Software System Construction3.0
INFO 200Systems Analysis I3.0
INFO 210Database Management Systems3.0
or CS 461 Database Systems
INFO 215Social Aspects of Information Systems3.0
INFO 216Issues in Information Policy 3.0
INFO 240Introduction to Data Science3.0
INFO 250Information Visualization3.0
INFO 300Information Retrieval Systems3.0
INFO 310Human-Computer Interaction II3.0
INFO 324Team Process and Product3.0
INFO 333Introduction to Information Security3.0
INFO 371Data Mining with Machine Learning3.0
INFO 440Social Media Trend Spotting3.0
INFO electives: Select 2 INFO courses not otherwise required6.0
Data Science electives: Select 2 of the following courses:6.0
Ubiquitous Information Technologies
Geographic Information Science
Visual Analytics
Software Project Management
Information Services
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
Mathematics and Statistics 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
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
ENGL 102Composition and Rhetoric II: Advanced Research and Evidence-Based Writing3.0
ENGL 103Composition and Rhetoric III: Themes and Genres3.0
COM 230Techniques of Speaking3.0
or COM 310 Technical Communication
University and College Requirements
UNIV CI101The Drexel Experience2.0
or CI 120 CCI Transfer Student Seminar
CIVC 101Introduction to Civic Engagement1.0
COOP 101Career Management and Professional Development0.0
Minor Requirements 124.0
Free Electives31.0
Total Credits188.0

1 Students should consult their academic advisor regarding a minor that requires more than 24.0 credits.

Sample Plan of Study

Term 1Credits
CI 101Computing and Informatics Design I2.0
ENGL 101Composition and Rhetoric I: Inquiry and Exploratory Research3.0
INFO 101Introduction to Information Technology3.0
INFO 108Foundations of Software3.0
MATH 101
or 121
Introduction to Analysis I
Calculus I
4.0
UNIV CI101The Drexel Experience1.0
 Term Credits16.0
Term 2
CI 102Computing and Informatics Design II2.0
CIVC 101Introduction to Civic Engagement1.0
ENGL 102Composition and Rhetoric II: Advanced Research and Evidence-Based Writing3.0
INFO 105Introduction to Informatics3.0
INFO 151Web Systems and Services I3.0
MATH 102
or 122
Introduction to Analysis II
Calculus II
4.0
COOP 101Career Management and Professional Development0.0
 Term Credits16.0
Term 3
CI 103Computing and Informatics Design III2.0
ENGL 103Composition and Rhetoric III: Themes and Genres3.0
INFO 152Web Systems and Services II3.0
INFO 215Social Aspects of Information Systems3.0
MATH 180Discrete Computational Structures4.0
UNIV CI101The Drexel Experience1.0
COOP 101Career Management and Professional Development0.0
 Term Credits16.0
Term 4
INFO 153Applied Data Management3.0
INFO 200Systems Analysis I3.0
INFO 333Introduction to Information Security3.0
PSY 101General Psychology I3.0
Free Elective 3.0
 Term Credits15.0
Term 5
INFO 210Database Management Systems3.0
INFO 216Issues in Information Policy 3.0
INFO 240Introduction to Data Science3.0
INFO 300Information Retrieval Systems3.0
STAT 201Introduction to Business Statistics4.0
 Term Credits16.0
Term 6
COM 230
or 310 [WI]
Techniques of Speaking
Technical Communication
3.0
INFO 250Information Visualization3.0
INFO 310Human-Computer Interaction II3.0
INFO Elective 3.0
STAT 202Business Statistics II4.0
 Term Credits16.0
Term 7
INFO 440Social Media Trend Spotting3.0
PSY 330Cognitive Psychology3.0
Data Science Elective3.0
Free Elective 3.0
Minor Course3.0
 Term Credits15.0
Term 8
INFO 371Data Mining with Machine Learning3.0
INFO 324Team Process and Product3.0
Free elective3.0
Minor course3.0
Science sequence4.0
 Term Credits16.0
Term 9
INFO elective3.0
Data Science Elective3.0
Free elective3.0
Minor course3.0
Science sequence 4.0
 Term Credits16.0
Term 10
CI 491 [WI] Senior Project I3.0
Free electives7.0
Minor courses6.0
 Term Credits16.0
Term 11
CI 492 [WI] Senior Project II3.0
INFO elective3.0
Free elective3.0
Minor courses6.0
 Term Credits15.0
Term 12
CI 493 [WI] Senior Project III3.0
Free electives9.0
Minor course3.0
 Term Credits15.0
Total Credit: 188.0

Minor in Data Science

Data Science provides a foundation for problem-solving in a data-driven society. The minor in Data Science combined basic courses in statistics, information and technology and social contexts to address problems that require large and disparate datasets.

Any student in any major can benefit from a minor in data science. Graduates with such background knowledge are prepared to actively participate in the application of data science within their major area of study.

The minor is available to all University students in good standing, with the exception of students majoring in data science.

STAT 201Introduction to Business Statistics4.0
STAT 202Business Statistics II4.0
INFO 240Introduction to Data Science3.0
INFO 371Data Mining with Machine Learning3.0
INFO 440Social Media Trend Spotting3.0
Select 3 of the following:9.0
Ubiquitous Information Technologies
Geographic Information Science
Information Visualization
Visual Analytics
Information Services
Database Systems
Database Management Systems
Total Credits26.0

Accelerated Degrees

The College of Computing & Informatics offers several Accelerated Degree programs designed to allow students to complete both a bachelor's degree and a graduate degree along with cooperative educational experience in fewer years than would be typical if pursuing the degrees separately. Students accepted in this program can combine any of the College bachelor's and master's degree programs as well as other options.

  • Any CCI BS/any CCI MS Accelerated Degree (BS & MS in five years, including 2 Co-ops)
  • Any CCI BS/MBA Accelerated Degree (BS/MBA)
  • Any CCI BS/JD Accelerated Degree (BS/JD)

For more information on the criteria for entering this program, visit the BS/MS Accelerated Degree page on Drexel's website.

For more information on how to apply for the BS/MS Accelerated Degree program, please visit the College of Computing & Informatics' website.

Co-op/Career Opportunities

Co-Op Options

Three co-op options are available for this program:

  • 5-year/3 co-op
  • 4-year/1 co-op
  • Accelerated Degree (BS & MS): 5-year/2 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.

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 four physical locations, including W. W. Hagerty Library, Hahnemann 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.

iCommons

Located in Room 106 of the Rush Building, the College’s iCommons is an open lab and collaborative work environment for students. It features desktop computers, a wireless/laptop area, free black and white printing, more collaborative space for its students and a furnished common area. There is a fully equipped conference room for student use with a 42” display and videoconferencing capabilities. The iCommons provides technical support to students, faculty, and administrative staff. In addition, the staff provides audio-visual support for all presentation classrooms within the Rush Building. Use of the iCommons 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 iCommons 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 iCommons 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 iCommons, 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.

Rush Building

The Rush Building houses classrooms, CCI administrative offices (academic advising, graduate admissions, faculty, etc.) and the iCommons computer lab (open to all CCI students). The building holds 6 classrooms equipped for audio-visual presentation. These rooms typically contain a networked PC, HD video player, ceiling mounted projectors, and other equipment for presentations and demonstrations. Four of these classrooms are fully equipped to function as laptop computing labs for networking, programming and database-related projects.

The Information Technology Laboratory, located in the Rush Building, consists of enterprise class information technology hardware that students would encounter in industry positions. The hardware includes 20 high powered workstations that are available to students and specialized networking lab simulation software. The hardware is networked and reconfigurable utilizing multiple virtual technologies as needed for the various classes the laboratory supports. In addition, a special system has been built into to the classroom to allow for conversion into a standard laptop computing lab utilizing motorized monitor lifts that allow the monitors and keyboards to recess into the desk.

University Crossings - Cyber Learning Center and Computer Lab

CCI also has classrooms, administrative office and faculty offices located in University Crossings, located at the corner of JFK Blvd. and Market Street. The building houses the Cyber Learning Center, a student computer lab, as well as several classrooms with video-conference enabled technology and media projection capabilities.

The Cyber Learning Center (CLC) provides consulting and other learning resources for students taking computer science classes. The CLC is staffed by graduate and undergraduate computer science students from the College of Computing & Informatics.

Both the CLC and UC Lab now serve as a central hub for small group work, student meetings, and TA assistance. The UC Lab is organized with desk space around the perimeter of the lab for individual or partner/pair-programmed student work, as well as with clusters of tables which can be connected as needed into pods to create workspaces for larger groups.

Research Laboratories

The College houses multiple research labs, led by CCI faculty, across Drexel’s main campus including: the Auerbach and Berger Families Cybersecurity Laboratory, Drexel Health and Risk Communication Lab, Socio-Technical Studies Group, Intelligent Information & Knowledge Computing Research Lab, Evidence-based Decision Making Lab, Applied Symbolic Computation Laboratory (ASYM), Geometric and Intelligent Computing Laboratory (GICL), High Performance Computing Laboratory (SPIRAL), Privacy, Security and Automation Laboratory (PSAL), Drexel Research on Play (RePlay) Laboratory, Software Engineering Research Group (SERG), 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.

Alumni Garden

The Rush Building’s Alumni Garden provides additional collaborative space for students, faculty, professional staff and alumni. The Garden features wireless networking, tables with built-in power outlets, accessible covered patio and balconies and a bicycle rack. The Alumni Garden may be reserved for Drexel events.

3401 Market Street

3401 Market Street houses faculty offices and doctoral student workspaces. It also is home to College research groups such and University initiatives such as the Isaac L. Auerbach Cybersecurity Institute. The Institute’s Auerbach and Berger Families Cybersecurity Laboratory serves as University’s first training facility dedicated to identifying challenges and discovering solutions in the areas of cyber infrastructure protection and incident response.

Evaluations

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. Information behavior, public libraries, gender, children, young adults, multicultural materials.
Larry Alexander, PhD (University of Pennsylvania) Executive in Residence. Research Professor. Large scale modeling and simulation, pattern recognition, the future of information technology.
Yuan An, PhD (University of Toronto, Canada). 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) Head of Department of Information Science; 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.
Jennifer Booker, PhD (Drexel University). Associate Teaching Professor. Software engineering, systems analysis and design, networking, statistics and measurement, process improvement, object-oriented analysis and design, bioinformatics, and modeling of biological systems.
Christopher Carroll, MS (Drexel University). Assistant 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.
Catherine D. Collins, MLIS (Indiana University). Associate Teaching Professor. Knowledge management, collection development, management of information organizations, information sources and services, international development.
John D'Ignazio, MS (Carnegie Mellon University). Assistant Teaching Professor. Human information interaction, digital curation, design of information infrastructures, methods development to elicit and evaluate impact on information environments, metadata schemes.
Prudence W. Dalrymple, PhD (University of Wisconsin-Madison) Director, Institute for Healthcare Informatics. Research and Teaching Professor. User-centered information behaviors, particularly in the health arena, health informatics, evidence based practice, education for the information professions and evaluation, and translation of research into practice.
M. Carl Drott, PhD (University of Michigan). Associate Professor. Systems analysis techniques, web usage, competitive intelligence.
Andrea Forte, PhD (Georgia Institute of Technology). 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.
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.
Gregory W. Hislop, PhD (Drexel University) Senior Associate Dean for Academic Affairs. 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). 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.
Linda S. Marion, PhD (Drexel University). Teaching Professor. 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.
Delia Neuman, PhD (The Ohio State University). Professor Emeritus. Learning in information-rich environments, instructional systems design, the use of media for learning, and school library media.
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. Archives and records, digital humanities, digital curation, pedagogy, diversity and inclusivity in the LIS profession
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). Assistant Professor. Computer-supported cooperative work, human-computer interaction, healthcare informatics, crisis informatics, social analysis of information and communications technology (ICT).
Il-Yeol Song, PhD (Louisiana State University) PhD in Information Studies Program Director. Professor. Conceptual modeling, ontology and patterns, data warehouse and OLAP, object-oriented analysis and design with UML, medical and bioinformatics data modeling & integration,.
Deborah Turner, PhD (University of Washington). Assistant Professor. Information behavior/interaction, management of information institutions, orality and information.
Kristene Unsworth, PhD (University of Washington). Assistant Professor. Information policy, ethics, government information.
Rosina Weber, PhD (Federal University of Santa Catarina). Associate Professor. Knowledge-based systems; case-based reasoning; textual case-based reasoning; computational intelligence; knowledge discovery; uncertainty, mainly targeting knowledge management goals in different domains, e.g., software engineering, military, finance, law, bioninformatics, and health sciences.
Christopher C. Yang, PhD ( University of Arizona, Tucson). Associate 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.
Valerie Ann Yonker, PhD (Drexel University). Associate Teaching Professor. Human service information systems, systems analysis and design, measurement in software evaluation, knowledge engineering.

Emeritus Faculty

Michael E. Atwood, PhD (University of Colorado) Associate Dean for Research and for Undergraduate Education. 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.
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.
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.
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