February 4, 2026
Jeremy Sykes
Business Insider recently reported that big tech firms invested over $300 billion in AI during 2025. At the same time, MIT’s NANDA research group published a report stating “that 95% of organizations are getting zero return” with efforts failing due to “brittle workflows, lack of contextual learning, and misalignment with day-to-day operations.”
This discrepancy between money spent and money generated has been nicknamed the GenAI Divide, and programs like the University of Chicago’s MS in Applied Data Science constantly evolve their curriculum to address this gap. At the helm of curriculum design is Arnab Bose, associate senior instructional professor in UChicago’s Data Science Institute, program director of the MS in Applied Data Science Online program, and chief scientific officer of UST. His goal is for the program to produce career-ready graduates who can marry practical skills in building machine learning models with the ability to operationalize a model to best fit an organization’s needs.
“One of the key strengths of our curriculum is that we have a thriving curriculum committee which is constantly evaluating what is happening in the industry and academia,” Bose said. Other faculty members in the program include professors and industry professionals from a broad swath of industries, from household names like Google to tech leadership from the nation’s leading investment banks, enterprise retailers, and many more.
“Our curriculum seeks to build this combination of man and machine, where you have the machine do the things the machine is strong at and give the right information to assist people in their work,” Bose said.
From Corporate to the Classroom
Bose’s dedication to ensuring models meet practical business needs began with his graduate work in electrical engineering at the University of Southern California, where he studied control systems and the real-world impact of machine learning in self-driving vehicles.
Bose joined a Silicon Valley start-up working on real-time control systems software for varied industry applications from robotics to aircraft automation control. He later entered the finance sector, performing quantitative software engineering for high frequency trading at Citadel. After some time at Credit Suisse (now a part of UBS) as a vice president, he left finance to work in an AI tech startup called Abzooba. Bose started teaching AI, machine learning and time series at the University of Chicago in his spare time, reigniting a passion he’d developed as a teaching assistant in his graduate school years.
“I have always enjoyed teaching; even now I enjoy when I step into the classroom to see those students eagerly waiting,” Bose said.
After Bose’s startup was acquired in 2021, he was hired full-time with the University to manage the growing demand for instructors, the program curriculum, and help build the online master’s program for Applied Data Science, UChicago’s first fully online MS program.
Other Programs Teach Data Science–We Launch Careers
Students in the MS in Applied Data Science program take a series of core courses, which provide the foundation for an ever-evolving array of electives that keep pace with advances in the industry. The electives fall in three broad categories. The first is domain specific (finance, healthcare, marketing, supply chain). The second group delves deep into technical methodologies to broaden skills within a particular area, such as Generative AI, natural language processing, reinforcement learning, computer vision, among others. The third category addresses the critical operational foundations of applied AI that emphasizes the techniques required for effective model training, deployment, and inference in production, with electives in governance, machine learning operations, real-time systems, and AI safety.
“We have core courses like leadership and consulting that talk about taking an existing solution and embedding it into a business workflow,” Bose said. “How do you address different technical challenges, enterprise change management, and operationalize a machine learning model?”
Through the required capstone course, students build practical experience developing solutions and operationalizing machine learning / deep learning models and Large Language Models for specific problem solutions. The students can choose between industry sponsored capstones, in which they pair up with an industry partner to work on existing pain points, or the research capstone wherein they create frontier models to solve a specific problem.
“Our students appreciate the strong synergy between the mathematical concepts they are taught and the actual needs of the enterprise,” said Bose. “Students learn to craft narratives that integrate practical relevance with rigorous foundational concepts in math and theory. That’s the power of our program.”
Arnab Bose is also an advisor to AI companies and the Chief Scientific Officer at UST. Bose has published a book, book chapters, and numerous peer-reviewed conference & journal papers. He enjoys public speaking and has given talks in the US, Australia, Saudi Arabia, and India. He is an avid traveler and has visited the seven wonders of the world. Arnab holds MS and PhD degrees from the University of Southern California and a BTech from the Indian Institute of Technology, Kharagpur, India.