Program Overview

Program Overview

The NYU Stern Master of Science in Business Analytics and AI is an advanced executive business degree program that teaches students to understand the role of evidence-based data in decision making and to leverage data and AI tools as strategic assets. The program is designed for experienced professionals interested in gaining competitive advantage through the predictive potential of data.

Program Description

The Master of Science in Business Analytics and AI is designed to accommodate participants’ busy schedules. Participants attend six concentrated, rigorous sessions in New York (5) and one at a rotating global location over a one-year period. Participants have the opportunity to cross-learn and share best practices among their cohort and through their exposure to top faculty and business leaders in the growing field of Business Analytics and AI. With a degree from New York University, graduates will join NYU’s extensive global alumni network.

Admissions

The admissions process is thorough and selective, taking various factors into consideration. Because the program is designed specifically for experienced professionals, a candidate's record of professional achievement is a critical factor in determining admission. To apply to the Master of Science in Business Analytics and AI Program, you must have a Bachelor degree and strong Grade Point Average; demonstrated high aptitude for quantitative analysis and academic success as evidenced by undergraduate and graduate coursework, as applicable.

• Record of professional success and employment profile
• Written Essay
• Selection Interview (by invitation only)
• University transcript(s)
• One professional recommendation
• Fluent English (TOEFL for non-native English speakers may be requested)

A non-refundable application fee of US $103 is required. The Program fee for the Class of 2026 is US $90,400. Included are all courses, tuition, course materials, some meals and official events. Hotel and travel expenses are not included.

Program Requirements

The program requires the completion of 35 credits, comprised of the following:

Course List

• XBA1-GB 8336: Intro to Analytics and AI (2 credits)
• XBA1-GB 8150: Digital Mktg Analytics (2 credits)
• XBA1-GB 8106: Found of Stat Using R (2 credits)
• XBA1-GB 8111: Databases for Business Analytics (1 credit)
• XBA1-GB 8346: Big Data (1 credit)
• XBA1-GB 8217: Dealing With Data Using Python (1 credit)
• XBA1-GB 8350: Decision Models (2 credits)
• XBA1-GB 8237: Machine Learning (3 credits)
• XBA1-GB 8354: Data Driven Dec-Making (2 credits)
• XBA1-GB 8215: Network Analytics (1 credit)
• XBA1-GB 8314: Operations Analytics (2 credits)
• XBA1-GB 8216: Decision Under Risk (2.5 credits)
• XBA1-GB 8348: Data Visualization (1.5 credits)
• XBA1-GB 8120: AI and Recommender Systems (2 credits)
• XBA1-GB 8271: Modern Artificial Intelligence (2 credits)
• XBA1-GB 8330: Revenue Mgmt & Pricing (2 credits)
• XBA1-GB 8600: Capstone (6 credits)

Sample Plan of Study

The sample plan of study is as follows:

• 1st Semester/Term:
  • XBA1-GB 8336: Intro to Analytics and AI (2 credits)
  • XBA1-GB 8150: Digital Mktg Analytics (2 credits)
  • XBA1-GB 8106: Found of Stat Using R (2 credits)
  • XBA1-GB 8111: Databases for Business Analytics (1 credit)
  • XBA1-GB 8346: Big Data (1 credit)
  • XBA1-GB 8217: Dealing With Data Using Python (1 credit)
  • XBA1-GB 8350: Decision Models (2 credits)
  • Total Credits: 17
• 2nd Semester/Term:
  • XBA1-GB 8314: Operations Analytics (2 credits)
  • XBA1-GB 8216: Decision Under Risk (2.5 credits)
  • XBA1-GB 8348: Data Visualization (1.5 credits)
  • Total Credits: 6
• 3rd Semester/Term:
  • XBA1-GB 8120: AI and Recommender Systems (2 credits)
  • XBA1-GB 8271: Modern Artificial Intelligence (2 credits)
  • XBA1-GB 8330: Revenue Mgmt & Pricing (2 credits)
  • Total Credits: 6
• 4th Semester/Term:
  • XBA1-GB 8600: Capstone (6 credits)
  • Total Credits: 6
• Total Credits: 35

Learning Outcomes

Upon successful completion of the program, graduates will:

1. Be prepared to manage various data sources, types, and sizes and apply mathematical modeling techniques.
2. Utilize statistical and regression-based modeling, data mining techniques, simulations, and other mathematical and statistical modeling to inform business decisions.
3. Employ the fundamental concepts of data science and data mining across various industries to solve business problems, all while identifying ethical issues and policy implications related to data and privacy.
4. Add value to firms and organizations through strategic approaches to data analytics across domain areas such as operations, marketing, finance, and revenue management.
5. Emerge with a toolkit consisting of methodologies and data visualization techniques that can be applied to real-world business analytics management issues.

Policies

University-wide policies can be found on the New York University Policy pages. Additional academic policies can be found on the Stern Graduate Academic Policies page.