Data Science and Strategic Analytics, MS

Introduction

The world is projected to generate an astounding 500 billion bytes of data every single day—that’s the equivalent of over 200 million DVDs. This data comes from many sources: people sharing on social networks or using smartphones, devices connected through the Internet of Things, businesses processing transactions, and scientists conducting large-scale research. Much of this data is generated in real time and is often tied to specific locations through GPS technology. 

Making sense of this overwhelming amount of data to benefit society is the challenge and opportunity of the “big data” era we are living in. Data scientists are at the forefront of this effort, creating solutions to gather, store, clean, analyze, and visualize big data to support informed decision-making.  A few examples are:

• Analyzing social media sentiment, like tweets about summer blockbuster movies, to predict future box office performance;

• Analyzing images from the James Webb Space Telescope to search for black holes; and

• Visualizing real-time crime data to optimize resource deployment.

Data science is inherently interdisciplinary, drawing on expertise from fields such as business, health sciences, arts & humanities, science and engineering, and the social sciences. The Data Science and Strategic Analytics (DSSA) program integrates resources and knowledge from across the University, ensuring a comprehensive and dynamic learning experience. The program's faculty includes adjuncts with significant experience as data scientists. This ensures students gain both theoretical knowledge and practical insights from those actively working in data-driven organizations.

Program Organization

The DSSA program offers a master’s degree designed to equip students with cutting-edge skills and proficiency with programming tools for tackling diverse data-driven challenges. Through its emphasis on providing hands-on experience with advanced computational software and developing skills in data analysis, presentation, and visualization, the program enables students to solve complex problems and discover meaningful insights in big data.

The program consists of 10 graduate courses (30 credit hours) which include projects involving real-world data to bridge the gap between theory and practice, and internship opportunities allow students to apply their knowledge in professional settings, build connections with industry leaders, and enhance their career prospects. The program can be completed either on a full-time or part-time schedule, with full-time students finishing in one calendar year (fall, spring, and summer). Courses are offered in a hybrid format during the fall and spring semesters and online in the summer session.

Whether you're looking to advance your career, transition to a data-focused role, or gain expertise in strategic analytics, the DSSA program provides the tools and experiences to help you succeed. 

Admission Requirements

The DSSA program welcomes applicants with diverse educational backgrounds. Though many applicants have undergraduate majors in quantitative fields such as science, math, computer science, or business, individuals possessing relevant computing skills are encouraged to apply regardless of their undergraduate major. Experience in descriptive statistics, data processing, and computing strengthen a student’s application. All applications are evaluated individually by a faculty admissions committee. 

• Admission to the DSSA program requires the applicant to

• Complete the official graduate admissions application;

• Hold a baccalaureate degree from a regionally accredited institution (official transcripts from all colleges/universities attended are required);

• Provide three letters of recommendation (preferably at least one from a faculty member); and

• Have a cumulative undergraduate GPA of at least 3.0, with a GPA of at least 3.2 in quantitative courses.

Recent Stockton graduates should check eligibility for the Direct Entry Option, a streamlined admissions path. For non-native English speakers, TOEFL scores or another Stockton-approved proof of proficiency must be submitted as part of the application.

Admission decisions are competitive, and acceptance is not guaranteed. The DSSA Admissions Committee may waive specific requirements at its discretion. Applicants lacking prerequisite skills may be asked to complete online courses in statistics or computing.

 Transfer Credit

Up to nine graduate credits may be transferred from regionally accredited institutions if the courses align with those at Stockton. Transfer credit requests are only accepted after formal application to Stockton. Matriculated students who are accepted for graduate work at Stockton are expected to complete all remaining course work at the University.

Graduation with Distinction

Program distinction is awarded to students who achieve a final GPA of 3.95 or above and earn a grade of A for the capstone course DSSA 5302 (Data Practicum). 

Academic Progress/Dismissal

Master’s degree students are bound by the academic progress requirements of the University’s graduate school, covered earlier in this Bulletin. A cumulative GPA of 3.0 or above is required to remain in good academic standing. Students who fall below this threshold are placed on probation. Failure to raise the GPA to 3.0 or higher upon subsequent coursework will lead to dismissal from the program. 

Program Completion Timeline

Students must complete all aspects of the program within six years of matriculation, excluding approved leaves of absence. Students who do not meet this timeline will be dismissed from the program. 

For more information about the program, explore the MS in Data Science and Strategic Analytics website. For general information regarding graduate education, consult the Graduate Studies section of this Bulletin. The full curriculum of the Data Science and Strategic Analytics program can be explored using the Degree Works web-based tool.