Business Analytics Track
The business analytics track is designed to expose students to the skills, methods, and practices that are useful for data-driven decision-making. This multidisciplinary field has strong roots in computer science, information science, mathematics, operations, and statistics. Topic areas include: data organization and management, computer programming, data mining, and machine learning, optimization, and statistical methods, used to both investigate past business performance and predictively model future performance. This track provides preparation for careers in a wide range of fields at companies that are committed to the use of data to gain insights about their business including consulting, entrepreneurship, financial services, marketing, risk management, sales, social media, and technology), as well as graduate school in the social sciences.
Fundamental Courses
While not required, taking the following courses is recommended. They will provide you with the fundamental knowledge and skills you will need to excel in this field. 
  • Intro to Programming & Data Science (TECH-UB 23)
  • Data Science for Business (TECH-UB 57)
  • Regression and Multivariate Data Analysis (STAT-UB 6017 previously STAT-UB 17)
Recommended Electives
Make sure to check Albert for any course prerequisites. 
  • Calculus II (MATH-UA 122), Linear Algebra (MATH-UA 140), Discrete Mathematics (MATH-UA 120)
  • Decision Models & Analytics (MULT-UB 7)
  • Social Media & Digital Marketing (TECH-UB 38)
  • Forecasting Time Series Data (STAT-UB 6018 previously STAT-UB 18)
These courses would also count for the Computing & Data Science or Statistics concentrations!

You can pick track electives that also count toward your declared concentration. Speak to an academic adviser if you have any questions about how tracks & concentrations overlap.

Headshot of Professor Xi Chen


Track Champion

Contact Professor Chen for any questions about the Business Analytics Track. This can include questions about which courses to choose, career paths, and more. 

Email: xc13@stern.nyu.edu
Office: KMC 8-50


Are you pursuing this track? Let us know by completing the concentration declaration form and adding in the track you are pursuing.