309 Hinds Hall
Associate Dean for Academic Affairs
School of Information Studies
110A Hinds Hall
Associate Dean, Masters Programs
Whitman School of Management
Graduate Programs, Room 315
School of Information Studies Faculty: Bei Yu, Yang Wang, Jeff Saltz, Joon Park, Jeff Hemsley, Nancy McCracken, Lu Xiao, Michael Fudge, Martha Garcia-Murillo, Gary Krudys
Whitman School of Management Faculty: Donald E. Harter, Anna Chernobai, Dinesh Gauri, Thomas Barkley, Mary Ann Monforte, John Park, Lai Xu, Reja Velu
Offered jointly by the School of Information Studies and the Martin J. Whitman School of Management, the Master of Applied Data Science degree program is designed to be a professional program of study, with a strong emphasis on the applications of data science to enterprise operations and processes, particularly in the areas of data capture, management, analysis and communication for decision making.
All candidates should have a bachelor’s degree or equivalent. In addition, it is recommended that potential students have a strong background in a data-intensive domain such as business, science, statistics, research, or information technology. The online program may be of particular interest to early- or mid-career professionals who cannot, or prefer not to, relocate. Applicants should have an interest in interdisciplinary work focused on managing large data sets using information technologies as tools to enable solutions for such organizations as business and public enterprises. Prospective students who have an interest in data science, but lack the recommended undergraduate background, are encouraged to inquire. Individual consultations are available for such prospective students to explore their potential candidacy.
- Graduate Program Application
- $75 non-refundable application fee
- Official academic credentials
- 500-word personal statement
- Two (2) letters of recommendation
- Required exam scores: GRE General Exam Scores (We can accept GMAT or LSAT scores in lieu of GRE scores, although GRE General Exam scores are preferred.); TOEFL or IELTS (international students only)
- Resume or Curriculum Vitae (CV)
- Video Submission (optional)
- Financial documents (international students only)
Merit scholarships are available for the on campus program.
Current classrooms, computer labs and laptop carts within the School of Information Studies and the Whitman School are available for this program; Online facilities provide complete coverage of all required course activities.
MS in Applied Data Science
Student Learning Outcomes
Successful students in the Master’s of Applied Data Science program will be able to:
1. Describe a broad overview of the major practice areas in data science.
2. Collect and organize data.
3. Identify patterns in data via visualization, statistical analysis, and data mining.
4. Develop alternative strategies based on the data.
5. Develop a plan of action to implement the business decisions derived from the analyses.
6. Demonstrate communication skills regarding data and its analysis for managers, IT professionals, programmers, statisticians, and other relevant professionals in their organization.
7. Synthesize the ethical dimensions of data science practice (e.g., privacy).
The Common Core (18 credits) includes foundational knowledge in databases, data analysis and business analytics. Students will complete Common Core courses in an order which builds foundational knowledge and skills in preparation for more advanced work.
Applications Analytics Core
The Applications Analytics Core (3-6 credits*) provides an opportunity for the student to choose one or two functional area specializations in accounting analytics, financial analytics, marketing analytics, and supply chain analytics. Students will complete one or two chosen Application Analytics Core course(s) as a way to develop deeper exploration of particular application area(s) for data science techniques.
The Electives (12-15 credits) include coursework in linear models, time series, scripting for data analysis, natural language processing, information visualization, data warehouse, text mining, advanced database management and information policy.
Students will also complete a Portfolio to provide an assessment of learning for their program. Students will choose assignments and projects worked on in courses during the course of study which reflect abilities specified in the program learning outcomes for inclusion in their personal portfolio. A panel of faculty who teach the courses included in the program will review the portfolios of graduates during the students’ final term. The panel will approve the portfolio for each student as a transcript milestone required for the degree.
Total Credits Required: 36
6 credits in related coursework can be transferred from other universities with the approval of the Program Director.
U.S. citizens, and non-citizens with the appropriate visa and/or immigration permissions for part-time study, may pursue this program on a part-time basis.
Students are required to have a 3.0 grade point average or higher to maintain satisfactory progress.
On-campus courses are delivered through the traditional semester format in which students take courses in the fall and spring semester, with optional internships in the summer. Section sizes for on-campus classes range from 20-45 students. Online courses are delivered with four (4) starts per year, where courses run for 11 weeks with required contact hours achieved through a mix of asynchronous and synchronous course interaction. Online section sizes run from 12-18 students.