MS in Data Analytics and Business Computing

The MS in Data Analytics and Business Computing, offered jointly by NYU Stern and NYU Shanghai, prepares recent graduates with strong analytical backgrounds for careers in the fast-growing field of business analytics. Over 36 credits, students learn to use a data-driven approach to solve business challenges in the era of big data, combining business (finance, marketing, operations), data science (statistics, econometrics, data mining, visualization) and management science (optimization, stochastic modeling, simulation), with hands-on training in Python and R.

The Master of Science in Data Analytics and Business Computing is a sister program of NYU Stern's MS in Business Analytics and AI (MSBAi), tailored for pre-experience students and recent graduates rather than senior professionals. The program equips students with a broad yet rigorous curriculum spanning business, data science and management science, with a strong emphasis on quantitative and technical methods and their application across finance, marketing and operations. Students begin their studies in the summer at NYU Stern in New York and complete the remainder at NYU Shanghai, earning a New York University Master of Science degree. The curriculum develops data-analytic thinking, data management, predictive modelling, optimization and practical programming skills in Python and R, preparing graduates to derive business value from large, complex datasets.

• Joint NYU Stern and NYU Shanghai program with a New York University Master of Science degree
• 36-credit curriculum across business, data science and management science
• Summer start at NYU Stern in New York, remainder at NYU Shanghai
• Hands-on training in Python and R for data analytics and predictive modelling
• 10-month or 18-month duration options to suit career plans

A four-year bachelor's degree from an accredited college or university in the U.S., or an equivalent degree from a foreign country.

TOEFL minimum 90, new TOEFL minimum 4.5, or IELTS minimum 6.5, for non-native English speakers or those without an English-taught university degree.

Calculus (3 credits, minimum grade B) before enrollment; elementary to intermediate proficiency in R is highly preferred (a self-study plan is required if not already proficient).

• Academic transcripts
• Prerequisite course evidence
• Resume/CV
• One letter of recommendation
• Personal statement
• English test score (if required)

Check your eligibility and confirm the Calculus prerequisite | Prepare academic transcripts and resume/CV | Prepare your personal statement and one recommendation letter | Complete the online application and pay the application fee | Submit English test scores if required | Await the admission decision (rolling rounds)

Not stated on official pages.

The 36-credit program combines business (finance, marketing, revenue management, operations), data science (statistics, econometrics, data mining, data visualization) and management science (optimization, stochastic modelling, simulation). Learning objectives include data-analytic thinking, full data-management processes, predictive modelling, mathematical formulation of business problems, and programming in Python and R, applied across finance, marketing and operations.

Graduates pursue careers in business analytics, data and information analysis, technical consultancy and IT project management across finance, marketing and operations, using data-driven decision-making to create business value.