Master of Science in Decision Sciences (MS), University of Colorado Denver Business School (CU Denver)

Faculty in the Decision Sciences program include internationally recognized scholars who produce theoretical and applied research in the best academic journals in their fields. Cutting-edge research guarantees that the curriculum reflects recent advances in tools and methods.

Decision Sciences faculty have received numerous, prestigious teaching awards. These include Business School and campus-wide Teacher of Year awards, the Teacher Who Makes a Difference awarded by Rocky Mountain News/Channel 4, and Professor of the Year by the 11-Month MBA program. Our faculty have coauthored textbooks and published seminal academic research about the teaching of decision science topics.

 Peter G. Bryant, PhD

Professor of Management Science and Information Systems

I have long been intrigued by how quantitative methods support, complement, and focus business thinking and decision making. In the not-too-distant past, computing power was the bottleneck in making quantitative techniques practical. That is less true now. We can compute many things we formerly couldn’t. This, in turn, has re-emphasized the need to think hard about what we need to compute and how we’ll use the results. That’s what decision sciences is all about – data, computing, business, and people.

Deborah L. Kellogg, PhD

 Associate Professor of Operations Management

I came to Decision Sciences and Operations Management from a nursing background. That might seem strange to most people, but I soon learned that most of the day-to-day problems that I saw when practicing nursing were decision sciences problems; staffing, scheduling, inventory, quality, and work flow are just a few. So while this field often seems full of numbers, there is a real human side to it. When we find solutions to business problems, we never do that in isolation of what it means for management, the employees, or the customers.

Gary Kochenberger, PhD

Professor of Operations Management

These are exciting times for the decision sciences.  Organizations all over the world are applying decision sciences methodologies such as data mining and forecasting to important workplace problems.  The use of formal modeling and mathematical analysis is showing up in every conceivable industry, like hospitality, retailing, healthcare, manufacturing, sports entertainment, and many more.  In all these settings, firms are learning that quantitative approaches to decision-making lead to substantial improvements in measurable outcomes like financial performance or customer satisfaction. In our MS program, students study the latest in decision science methodologies along with the most popular software available for implementing various applications.  Our strong applied orientation is designed to prepare students for an exciting career of solving important problems in both the private and public sectors.

Marlene Smith, PhD

Associate Professor of Quantitative Methods

As the Director of the MS Decision Sciences program, I have the enviable task of keeping in close contact with each of our MS students. I consult with them about their professional interests and assist students in choosing elective courses to help them achieve their career goals. For example, students expressing an interest in becoming a data mining professional will be directed toward that course along with forecasting, project management, and research methods. I refer students to other decision sciences faculty who have specific expertise in their areas of interest. I also alert students to scholarships, internships, conferences, lectures, and job placement resources. 

Coming from an engineering background, I was looking for a degree that would bridge the gap between the engineering and business world. The MS in Decision Sciences was a perfect fit, allowing me to diversify my skills and move ahead in my career.

Trevor Daly
MS in Decision Sciences student

Decision sciences uses data to find solutions to real-life business problems. When I registered for my classes, I’d never taken a statistics class. I thought data analysis was just a core requirement to get out of the way. I realized how much I enjoy statistical analysis after working fourteen hours straight on a case and feeling completely satisfied with the way I’d spent my weekend.  Before that first semester, nobody had told me statistics is, at its core, investigative.  I love it!  Statistical analysis doesn’t happen in a vacuum – when I graduate, I want a job where I analyze data and turn my analysis into concrete business recommendations.

Eliza Klinger
MS in Decision Sciences student

Number crunching, a staple for decades in the quantifiable domains of engineering and finance, has spread in recent years into marketing and sales. Companies can now model and optimize operations, and can calculate the return on investment on everything from corporate jets to Super Bowl ads. These successes have led to the next math project: the worker.

Business Week, March 12, 2009
Data Mining Moves to Human Resources

From fledglings like Inform to tech powerhouses such as IBM, companies are hitching mathematics to business in ways that would have seemed fanciful even a few years ago.

Business Week, January 23, 2006
Math Will Rock your World

The rising stature of statisticians…is a byproduct of the recent explosion of digital data. 'We’re rapidly entering a world where everything can be monitored and measured,’ said Erik Brynjolfsson, an economist and director of the Massachusetts Institute of Technology’s Center for Digital Business. ‘But the big problem is going to be the ability of humans to use, analyze and make sense of the data.’

The New York Times, August 6, 2009
For Today’s Graduates, Just One Word: Statistics

The analytics branch [of IBM], launched in April, will use more than 4,000 consultants and a growing arsenal of software to help companies better understand their swelling piles of data, focusing on complex problems like supply chain management, fraud detection and financial risk assessment. Predictive analytics, like those offered by SPSS, find leading indicators that can allow data mining to forecast future trends and help companies prepare for them.

Forbes, July 28, 2009
IBM Buys a Profitable Prophet

Students team up to model downtown traffic flows

Cassie Milestone, Urban Planning Manager at the Downtown Denver Partnership (DDP) and long-time partner of the Business School, was staring at a giant puzzle.

What if the city closed off streets near Union Station to accommodate a street festival? What would happen to traffic flows?

A quick conversation with Dean Sueann Ambron led to the idea of designing a class project around the puzzle at hand. Business School Professor Gary Kochenberger agreed it would make a great problem for his Decision Sciences Capstone Master class—a three-semester-hour “practicum” applied to a real-world challenge.

Redesigning traffic flows around Union Station pushed critical-thinking skills to limit

His six students got to work analyzing and modeling traffic flows in lower downtown Denver. They used Simio, a robust simulation tool, to model what might happen if the city closed certain streets during peak and non-peak times of the day. The project stretched the students’ critical-thinking and team skills to the limit.

“I had some sleepless nights because of the complexity,” confessed Kochenberger. “These students were tasked with building a dynamic logic model for traffic flow while learning a whole new software program."

Downtown Denver Partnership noted the model's 'visual sophistication'

The students presented their preliminary recommendations to a room packed with urban planners and transportation experts. Their demo of the Simio simulation, built intersection by intersection, captivated the attention of the group.

“The visual sophistication of this model is amazing,” said Milestone. The simulation showed cars queuing at intersections, bicycles cruising the streets and realistic building details.

“I can see using a simulation like this to show condo owners a neighborhood concept, or to get buy-in on a future urban project,” Milestone added.

Students involved in the project were Matt Berkeland, Ethan Chen, Trevor Daly, Derek Serlet, Michael Skorupka, and Lisa Zuniga. No surprise: They aced the class.

For more information about this project contact Professor Kochenberger at gary.kochenberger@ucdenver.edu.​​​