M.S. in Data Science & Strategic Analytics
The Data Science and Strategic Analytics (DSSA) Program at Stockton is a self-standing, master’s degree program. A student entering the program will acquire substantial experience in sophisticated, industry standard, computational software and programming tools that will allow the student to explore data driven problems in the science, business, social science, medicine and/or the humanities.
Students will also develop skills in data analysis, presentation, and visualization; skills that will permit them to visualize results and make predictions. The course work is supplemented with real world projects and/or internships with industry providing experience and networking opportunities in industry or research.
In 2020, it was estimated that on average 1.7 million bytes of data were created every second by every human. You can envisage 1.7 million bytes as a 100,000-word essay! Data is created by individuals (through social networks and mobile phones - in 2021 there were approximately 4.3 billion unique mobile phone users.); machines (through real-time, network connected, sensors – “the internet of things”); business and commerce (e.g. transaction records); science (e.g. bioinformatics, large scale simulation); medicine (MRI scans and EEGs). Much of this data is real time and georeferenced through GPS. Making sense of this vast ocean of data for the use and benefit of society is considered an imperative of the coming years, indeed most companies and organizations are already vigorously pursuing their “big data” agenda. Data scientists develop solutions for gathering, cleaning, archiving, analyzing and visualizing data for the purposes of making informed decisions, usually building upon their domain knowledge in their chosen field (e.g. biology, medicine, engineering, psychology). They develop models based on deep learning (a term now synonymous with artificial intelligence). Some examples of data science projects include the following.
Business: Use historical discounting data from a department chain store at one thousand locations to predict how sales vary with department, season and location.
Entertainment: Perform a sentiment analysis on the tweets about new Netflix shows and use this to predict future successful projects based on genre, actors etc.
Neuroscience and psychology: Analyze EEG data (electrical activity of the brain) via a wearable (or implant) and map this to behaviour.
Astronomy: Analyze the jpg images of one million galaxies to categorize them according to their morphology.
Medicine: Cancer detection by artificially intelligent, automated image analysis.
Criminal Justice: Gather and visualize real time crime data for a city for efficient resource deployment.
Education: Create a web-based dashboard for describing student performance metrics across a school district.
Since the program is interdisciplinary in scope, resources are drawn from various schools and programs across the University; they include, but are not limited to business, healthcare, education, government, science, psychology, engineering and humanities. Due to the crucial industrial and applied aspects of the program, it is also important to involve professional and/or adjunct faculty who are current leaders in the various organizational types included in the program.
The Master’s degree program consists of 30 credit hours (10 graduate courses) that can be completed in full-time (or part-time study). In full-time study it may be completed in one calendar year (Fall, Spring, Summer). The courses are offered online as a hybrid (students will meet with faculty once a week at Stockton's Kramer Hall Instructional Site).
The self-standing Master’s degree program consists of 30 credit hours (10 graduate courses) that can be completed in full-time or part-time study. The courses are offered online as hybrid or blended courses. Full-time study is the preferred route and in this mode the degree may be completed in one calendar year.
DSSA Curriculum - 30 credits
All courses are for 3 graduate credits.
DSSA 5001 Introduction to data science and analytics
DSSA 5101 Data exploration
DSSA 5102 Data gathering and warehousing
DSSA 5103 Data Visualization
DSSA 5104 Deep Learning
DSSA 5201 Machine Learning
DSSA 5202 Data Entrepreneurship
DSSA 5203 Data Stewardship
DSSA 5301 Communicating Data Stories
DSSA 5302 Data Practicum
For course descriptions, please visit The University's Course Catalog.
Fall enrollment only: July 1
- Baccalaureate degree from a regionally accredited institution of higher education.
- Minimum undergraduate GPA of 3.0 and an average GPA of 3.2 or better derived from all quantitative courses.
Students with undergraduate degrees in any domain area (e.g. science, math, computer science, business) with experience in descriptive statistics, and college algebra with excellent digital literacy will make up most applications and enrollments; however, applicants from any domain area can enter the data science field if they possess the relevant computing skills. Students deficient in computer skills may be given an acceptance conditional upon them acquiring and demonstrating those skills. All applicants will be evaluated individually by a faculty committee.
To be considered for admission to the DSSA master's program, applicants must submit the following:
- Click here to start your application.
- Application fee: $50 (non-refundable), submitted with your online application
- Graduate application essay describing computing experience
- Three current letters of recommendation sent electronically via the online application (preferably at least one from a faculty member).
- Official transcripts from all colleges/universities attended (including Stockton) showing successful completion of a baccalaureate degree from a regionally-accredited institution.
- The TOEFL Exam is required of students for whom English is the second language.
Acceptance into the DSSA program will be based on a review of the entire application packet. Admission to the program is competitive and acceptance is not guaranteed. Specific minimum requirements may be waived at the discretion of the DSSA Admissions Committee. Additionally, students lacking in the required prerequisites may be asked to take remedial online courses in computing and/or statistics.
Direct Entry is available for Stockton students who meet the requirements.
Please visit the Direct Entry page for more information.
Get up to speed with the latest data science technologies including Linux, Python, and R Basics by taking free online courses offered through www.W3schools.org. These courses are especially helpful to those considering applying and in need of some remediation prior to starting graduate classes. You should take the courses entitled, "Learn Data Science", "Learn Python" and "Learn R".
Standardized test scores (GRE or MAT) are not required for admission. However, proof of English proficiency is required of students for whom English is the second language.
Yes. Students may take up to two classes (6 credits) as a non-matriculated student.
Please fill out the Online Graduate Non-Matriculated Registration form.
Additionally, undergraduate students at Stockton, may, with permission, take two graduate courses as undergraduates. These courses will count towards their undergraduate degree and to their graduate degree.
Provided that the courses sufficiently match corresponding Stockton courses, the University will accept up to nine credits of appropriate, relevant graduate credit from other regionally-accredited colleges and universities. Graduate credit will only be accepted upon application to Stockton. Once students have matriculated at the University, students will be required to finish the remainder of their course work at Stockton.