Data Science Major (M.S.)
Data science comprises the concepts, techniques, tools and body of knowledge supporting Big Data, the acquisition, management, analysis and display of large, rapidly changing, and varied sets of information. It supports the extraction of actionable knowledge directly from data through a process of discovery, or hypothesis formulation and hypothesis testing. Data science encompasses activities ranging from collecting the raw data, processing and extracting knowledge from the data, to decision making based on the data, implementing a solution. The data science field presents career entry, advancement and transition opportunities for practitioners and researchers in industry, government and academia at various levels of expertise.
A data scientist is a practitioner who has extensive knowledge in the overlapping realms of business needs, domain knowledge, analytical skills, and software and systems engineering to manage the end-to-end data processes in the data life cycle. Such a practitioner is skilled in data management and processing, analyzing business and scientific processes, and communicating findings for effective decision making.
The Master of Science in Data Science Program equips students with the knowledge and competencies required to become data science and analytics professionals. Applying tools and methods such as probability theory, statistical analysis and computing, and exploring subjects such as data collection, manipulation, processing, analysis and visualization, the students learn how to solve data-driven problems and practice analytics-driven decision making. Furthermore, students learn how to automate these activities by cloud computing and machine learning platforms as the amount of accumulated data grows immensely.
General Admission Requirements
Applicants must submit the following materials (please note that an application will not be reviewed until all required materials have been submitted):
- Completed Graduate Application with Fee
- Résumé
- Personal Statement
- Three Letters of Recommendation
- Transcript(s)
- GRE General Exam Scores (maybe waived according to academic record of candidate, please contact the Director of Graduate Studies to request a waiver)
Admission Requirements for International Applicants
In addition to the general admission requirements for the M.S. in Data Science program, international applicants must submit the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS) scores.
Degree Requirements
Total number of credits for both capstone and thesis track: 30 credits
| Code | Title | Hours |
|---|---|---|
| Required Courses | ||
| Take the following 5 required courses (15 credits): | 15 | |
| Big Data Analytics | ||
| Machine Learning | ||
| Data Mining | ||
| Data Visualization | ||
| Statistics for Data Science | ||
| Elective Courses | ||
| Select 3 courses (9 credits) of the following: | 9 | |
| Deep Learning | ||
| Data Engineering | ||
| Machine Learning | ||
| ST - Adv Machine Learning | ||
| Text Mining | ||
| Operations Research | ||
| Intern in Visual Analytics | ||
| Network Analysis | ||
| Special Topics in Data Science (*) | ||
| Special Topics in Data Science (*) | ||
| Ethical Challenges of Big Data | ||
| Cognition for Visualization | ||
| Specialization | ||
| Select one of the following Tracks: | 6 | |
M.S. Capstone Track (6 credits) | ||
Elective | ||
| Data Science Project | ||
M.S. Thesis Track (6 credits) | ||
DASC 9412 | ||
| M.S. Thesis | ||
| Total Hours | 30 | |
- *
Represent one and two credit special topics courses that may be scheduled in three- credit course sequences.
Apply to Seton Hall
Home to nearly 10,000 undergraduate and graduate students, Seton Hall has reached new heights in academic excellence, faculty research and student success. Ready to take the next steps on your academic or career path?
