Information Systems for Data Science

Data analytics has become a critical need in industries ranging from health care and financial services to marketing and government. Leveraging the strengths of the College of Management’s faculty, the University of Massachusetts Boston is offering a cutting-edge and flexible doctoral program in this field.

Positioned at the intersection of technology, business, and strategy, the information systems for data sciences track in UMass Boston's Business Administration, PhD Program allows students to have a holistic view of data science and the role it plays in competition. Students will get exposed to a variety of state-of-art research streams in information systems and data science, with a relative focus on both data analytical techniques from a design science perspective and the application and management of data analytics in business settings from an organizational perspective. The program will offer students flexibility to investigate other topics they find interesting in data science and technology fields.


Who should apply?

All students with master degrees who are interested in information technology and data analytics are welcome to apply. Students with degrees in quantitative fields such as statistics, economics, math, computer science, management sciences, information systems, and other related disciplines are particularly encouraged to apply. A master degree in these related fields is a plus, although not required. Previous full-time working experiences in related positions are also a plus.

 

Impact

Rapid increase in the amount of published data results in a data deluge that imposes significant challenges in data analytics. By offering a carefully tailored combination of courses in information technology, applied statistics, and business analytics, our PhD program provides rigorous and in-depth courses of study with emphasis on various research methodologies, tools for data analytics, and relevant academic skill sets involving research design, literature review, theoretical development, empirical validation, and academic writing. Our program also provides students with extensive knowledge in the various emerging research areas in information systems (IS) field through IS research seminars and research collaboration opportunities with faculty members.

Academic advisors will help students configure a program of study which includes a rigorous sequence of courses in a variety of research methodologies, theories, and topics. Students will develop theoretical and methodological competencies in a variety of topics in the field of information systems and data science. Students will develop teaching competences through the teaching seminar, GA assignments to support a professor, and independently deliver courses. In addition to course work, students will actively engage in research with faculty members.

 

Kinds of Research

The PhD program involves close, apprentice-like working relationships with faculty members, and students are introduced early to the world of conferences and publishing. A sampling of faculty projects includes:

  • cybersecurity analytics for massive communication graphs
  • home health care management for dually diagnosed Individuals with mental and physical health problems
  • characterizing managers' decision making patterns under uncertain and competitive environment
  • business intelligence as an IT-enabled agile and competitive business platform
  • social media, big data, and Innovation: an investigation of the software industry in India
  • strategic use of cloud computing and data assets for sustainable competitive advantage
  • decision modeling applications to areas such as technology development, policy, resource management.
  • abysmal behavior in online social networks
  • the role of health IT in hospital acquisitions
  • social influence on Bayesian learning process in post-adoption stage
 

Career Opportunities

There are two main career opportunities for the individuals graduated from this program. They can pursue a career in academia as a faculty member or join an organization as a data scientist. In the first case, they can educate other data scientists and conduct state of the art research to be published in peer-reviewed journals.

For the second, students can become data scientists who use the acquired knowledge to excel the effectiveness of data collection and analytics in their organization and improve its competitiveness in today’s economy.

 

Curriculum

Sample Program of Study

Year One

Fall
Required Courses

BUSADM 700 Business in Context: Markets, Technologies, Societies
BUSADM 740 Information Systems Theory I
BUSADM 742 Regression
BUSADM 744 Quantitative Research

Spring
Required Courses

BUSADM 741 Information Systems Theory II
BUSADM 743 Decision Analysis
BUSADM 745 Multivariate Statistics

Summer

Summer project work (proposal due first week of May; due first week of September)

Year Two

Fall
Required Courses

BUSADM 780 Advanced Data Mining and Predictive Models
BUSADM 782 Optimization 

Elective Courses

Choose two

Spring
Required Courses

BUSADM 785 Big Data
BUSADM 775 Doctoral Teaching Seminar

Elective Courses

Choose one

Also

Formulate structure of dissertation and committee
Comprehensive Exam (Admission to candidacy exam) in April-early June

Year Three

Fall
Requirements

BUSADM 899 Dissertation Research
Submit Notification of Proposed Dissertation Committee
Finish dissertation proposal
Proposal defense
Begin dissertation work

Spring
Requirements

BUSADM 899 Dissertation Research
Dissertation work

Year Four

Fall
Requirements

BUSADM 899 Dissertation Research
Dissertation work
Submit Notification of Intent to Defend Dissertation

Spring
Requirements

BUSADM 899 Dissertation Research
Defend Dissertation

For additional details, download the  Program Roadmap
 

Faculty Profiles

More than a dozen dedicated faculty members are devoted to student learning in this track alone, with additional faculty serving in supporting roles. Faculty are leaders in their fields who regularly publish scholarly articles in top academic journals.  Doctoral students will be paired with faculty advisors based on their area of interest. This intense mentorship program allows students to learn the crafts of research and teaching in a highly collaborative environment. 

Information Systems for Data Science Faculty