A High-Demand Field With Growth Opportunities
Ignite your data science career with a cutting-edge curriculum that provides you with skills in machine learning, big data, and data visualization. Gain insight from top industry experts and dive into real-world projects. Our M.S. in Data Science offers coursework in topics such as database management systems, data mining and machine learning algorithms, data visualization, statistics, text analytics, and big data. Graduates of the Data Science program will obtain a variety of skills required to analyze large datasets and to develop modeling solutions to support decision making. Join our network and fast-track your path to high-demand roles in data science, artificial intelligence and machine learning!
Ready to build a career in Data Science? Enroll now and take the first step toward your future.
- Degree Type
- MS
- Area of Interest
- Computing & Technology
- Associated Colleges or Schools
- Program Location
- Queens Campus
- Required Credit Hours
- 30
Contact Us
We are here to answer your questions about the Data Science program and admission process.
Please contact:
Justin Goldberg
Graduate Assistant Dean
ccpsgrad@stjohns.edu
Program Director
Dr. Christina Schweikert
Degree Requirements
The M.S. program in Data Science requires 30 credits that include the following:
Core Courses (9 credits required)
- CUS 510 Database Management Systems
- CUS 610 Data Science Concepts and Methods
- CUS 752 Machine Learning
Data Analysis/Applied Statics Courses (6 credits chosen from the following)
- BUA 609 Advanced Managerial Statistics
- BUA 633 Predictive Analytics and Forecasting Models
Elective Courses (6 credits in one of the following areas)
- CUS 620 Introduction to Programming for Analytics
- CUS 625 Data Visualization Programming
- CUS 640 Natural Language Processing and Large Language Models
- CUS 680 Distributed Big Data Analytics I
- CUS 681 Distributed Big Data Analytics II
- CUS 725 Advanced Database Management Systems
- CUS 756 Deep Learning Models in Machine Learning & Generative AI
Capstone Course
- CUS 690 Applied Analytics Project
Specialization (6 credits in one of the following areas):
- Big Data
- CUS 680 Distributed Big Data Analytics I
- CUS 681 Distributed Big Data Analytics II
- Cyber and Information Security
- CYB 611 Foundations in Cyber Security
- CYB 615 Protection of Digital Infrastructure
- CYB 621 Cybersecurity Laws, Regulations, and Best Practices
- CYB 625 Principles of Secure Scripting and Cryptography
- CYB 711 Intrusion Detection and Analysis
- DFR 711 Cyber-Forensic and Malware Analysis
- Healthcare Analytics
- HCI 520 Medical and Health Informatics
- HCI 525 Applied Healthcare Analytics
- Marketing Analytics
- MKT 600 Decisions in Marketing Management
- MKT 611 Data-Driven Marketing
Program at a Glance
Specializations include Big Data Analytics, Cyber and Information Security, Healthcare Analytics, Marketing Analytics
Degree Credits Required
months (full-time)
Estimated Time To Complete
Evening Courses Available
Program Educational Objectives
The Program Educational Objectives for the M.S. degree in Data Science are that within a few years after graduation, graduates are expected to:
- Develop and use data science applications in industry or research.
- Develop data science applications for use in a variety of domains.
- Continue learning and professional development to remain current in data science and computing topics.
- Contribute to the field of data science and society as an entrepreneur, innovator, or researcher.
Student Outcomes
Graduates of the program will have the ability to:
- Analyze a complex problem and apply principles of computing and other relevant disciplines to elaborate students to it.
- Design, implement, and evaluate a computing-based solution to meet a given set of requirements in the context of the program's discipline.
- Communicate effectively in a variety of professional contexts.
- Recognize professional responsibilities and make informed judgements in computing practice based on legal and ethical principles.
- Function effectively as a member and leader of a team engaged in activities appropriate to the program's discipline.
Admission Requirements
All applicants must possess a bachelor’s degree from an accredited institution or the international equivalent before enrollment at the graduate level. In addition to the application form and non-refundable application fee, candidates should submit the following:
- Statement of professional goals and resume, which can be uploaded as part of the application for admission.
- Official transcripts from all undergraduate, graduate, and professional schools attended.
- One letter of recommendation obtained from a professional or academic reference.
- Sufficient previous coursework in calculus/statistics or equivalent mathematics courses.
- Official TOEFL, IELTS, PTE or Duolingo scores are required for applicants whose native language is not English.
- Students with international credits must submit a course-by-course foreign credit evaluation with GPA calculation from a NACES member.
For additional information, please contact:
Office of Graduate Admission
gradhelp@stjohns.edu
718-990-1601
Shiqi Chen ’18MS
“Transitioning from a medical background to the field of data science was a significant turning point in my career. The inclusive and diverse environment at St. John’s University provided me with the opportunity to pursue my passion for data science, despite coming from a nontraditional background. Since completing the M.S. program, I have witnessed significant growth in my career as a data scientist.”
Peter Tadrous ’20CCPS, ’21GCCPS
“The Data Science program at St. John’s has been instrumental in equipping me with robust skills in statistics, data wrangling, and data visualizations. The program inspired me to see the malleability of data and how I can shape it into meaningful insights. The educators at St. John’s have fueled my curiosity and propelled a continual journey of discovery in data science.”
Brad Rose ’20MS
“I was thankfully able to use my military benefits to continue my higher education at St. John’s, which led to employment in the fintech industry at J.P.Morgan. I did so late in life; it’s never too late to get an education. For those of you who are getting one early, remember how fortunate you are to have the support structure and ability to do so.”
Potential Careers by Specialization
- Big Data Engineer
- Business Intelligence Analyst
- Data Analyst
- Data Architect
- Data Mining Specialist
- Data Scientist
- Machine Learning Engineer
- Malware Analyst
- Security Analyst
- Security Architect
- Security Data Scientist
- Security Engineer
- Security Operations Center (SOC) Analyst
- Threat Intelligence Analyst
- Healthcare Analytics Manager
- Healthcare Business Intelligence Analyst
- Healthcare Data Analyst
- Healthcare Data Scientist
- Healthcare Informatics Specialist
- Healthcare Predictive Modeler
- Customer Insights Analyst
- Digital Marketing Analyst
- Marketing Analyst
- Marketing Data Strategist
- Marketing Operations Manager
- Marketing Research Analyst
- Marketing Strategy Consultant
The Institute for Data Science
The Institute for Data Science at St. John’s University serves as a hub for research activities, with an emphasis on data mining and analytics. Researchers explore opportunities for data science initiatives among disciplines within The Lesley H. and William L. Collins College of Professional Studies and across the University.
AI Evolution: What is a Large Language Model?
Many people use Artificial Intelligence (AI) chatbots like ChatGPT and Gemini. They give you the answers you want without doing an extensive deep dive through a Google search. But have you ever wondered what a large language model is and how it can generate such excellent responses?
Events and Seminars
Explore upcoming events and seminars featuring top industry professionals and student networking opportunities.
Wednesday, May 3rd, 2023
Data Science Alumni Career Panel
Click here to view the flyer!
Meet Our Faculty
-
Assistant Professor,
- Associate Professor
Department
Computer Science, Mathematics and Science
-
Assistant Professor,
- Associate Professor
Department
Computer Science, Mathematics and Science
