McQuiad Hall, Room 212A
(973) 761-9466
dava@shu.edu
www.shu.edu/academics/graduate-certificate-data-visualization-analysis.cfm
Director: Manfred Minimair, Ph.D.
The program is offered by the Department of Mathematics and Computer Science and the Department of Psychology.
This program provides the skills and knowledge that data analysts need to succeed. The students learn how to analyze and visualize complex data with industry-standard applications, including Tableau, and programming languages such as R and Python. The program trains the students to communicate information clearly and effectively through graphic depictions that stimulate and encourage viewer engagement. The students practice preparing real-world data for storing in databases, analyzing data with statistics and machine-learning tools, and using visualization to explore data and present findings. The program is an online program. Students are not required to be present on campus.
Prerequisites
- Minimum undergraduate GPA: 2.75
- Graduate admission to Seton Hall University
Credits for Graduate Programs
The graduate certificate provides credits for two graduate programs in the College of Arts and Sciences, M.S. in Data Science and Masters in Public Administration. Nine credits count equally for all programs and the remaining credits depend on the statistics course.
- M.S. in Data Science: total of 12 credits towards the curriculum if DASC 6811 Statistics for Data Science is taken
- Masters in Public Administration: total of 12 credits towards the curriculum if PSMA 6002 Research Methods-Stat Analy or DASC 6811 Statistics for Data Science is taken
Graduate Curriculum: Certificate in Data Analytics
The updated curriculum consists of three required courses (9 credits) and one elective course (3 credits). The program is 100% online.
Course List
Code |
Title |
Hours |
| 9 |
| Data Visualization | |
| Data Mining | |
| |
| Research Methods-Stat Analy | |
| Statistics for Data Science * | |
| Biostatistics | |
| Interm Statistical Methods I and Interm Statistical Methods II | |
| Res Methods and Stat Analysis | |
| Res Design and Analy I and Res Design and Analy II ** | |
| 3 |
| Text Mining | |
| Machine Learning *** | |
| Ethical Challenges of Big Data | |
| Cognition for Visualization | |
Total Hours | 12 |