Rugved Mhatre

MS. Computer Engineering @NYU Ex-Oracle

2 minute read

“Data Science for Business” course (TECH-GB 2336) at NYU Stern by Chris Volinsky

nyu-header

The “Data Science for Business” course (TECH-GB 2336) at NYU Stern by Chris Volinsky focuses on how data science can solve real business problems. The course teaches students how to approach decisions using data, apply machine learning models, and communicate insights effectively. With hands-on projects and real-world datasets, it’s ideal for anyone looking to apply data science in areas like marketing, product management, or consulting.

Course Overview

Data Science for Business is a hands-on, application-focused course that teaches students how to approach business problems through the lens of data science. The course emphasizes data-analytic thinking—developing the ability to ask the right questions, use the right tools, and make better decisions using data. Students learn how to apply the full data science process, from problem formulation and exploratory analysis to model building, validation, and deployment.

The course covers core machine learning techniques including decision trees, linear classification and regression, clustering, ensemble methods, and neural networks. Students also explore advanced topics such as modeling text data, building recommender systems, and understanding the ethical implications of data science in business. Throughout the semester, students work with real-world datasets and use Python’s powerful data science libraries like pandas, scikit-learn, and matplotlib.

What sets this course apart is its strong focus on practical, real-world applications. Each topic is tied to business use cases, helping students understand how data science drives decisions in marketing, product management, sales, and operations. In addition to lectures and coding assignments, students engage in classroom discussions about current applications of data science in industry.

The course culminates in a team-based term project. Students choose a business problem, analyze real data, build models, and present their findings. This project simulates the end-to-end workflow of a data science role and gives students experience with solving complex, open-ended problems—preparing them to apply these skills in professional settings.

Course Work and Grading

Note: This is subject to change

  • Homework (4 assignments) - 30%
  • Quiz (2 in-class) - 30%
  • Term Data Project - 35%
  • Participation/Attendance - 5%

Note: As per NYU Stern policy, no more than 35% of students in core classes will receive an A or A-. Final grade cutoffs are adjusted based on overall class performance. Most students typically receive grades in the B range, with 5% or fewer earning a C+ or lower.

Course Notes

My course notes are available here.

Review

I found Data Science for Business to be a very engaging and insightful course. It does a great job highlighting the real-world impact of machine learning models and emphasizes the importance of making informed, data-driven business decisions. While some of the early Python assignments and foundational topics may feel familiar to students with a strong background in machine learning, the course truly shines through its in-class discussions.

The classroom environment brings together a diverse mix of students—MBA candidates from NYU Stern and technical students from Tandon and Courant—which leads to valuable cross-disciplinary conversations. Professor Volinsky’s industry insights add a unique and practical dimension to the learning experience.

In terms of workload, the course is moderate. We had bi-weekly coding assignments, a midterm, a final quiz, and a semester-long group project that allows us to apply the full data science process to a business problem of our choice. I would highly recommend this course to anyone looking to strengthen their understanding of how data science can drive business value, especially those who want to build a strong foundation in applying machine learning in real-world contexts.

You May Also Enjoy