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The Krenicki Center is dedicated to the growth and education of all Purdue University students. We facilitate this growth through educational enhancements by organizing student events such as data dives, case competitions and workshops. We also offer informative and educational webinars related to real time analytical issues facing the corporate world. Thus making us an ideal platform for students to conduct research in data analytics.



Through the Krenicki Center qualified students will also have the opportunity to work on real-life projects providing solutions to companies’ analytical issues. Members of our team will not only be developing practical skills through working on projects with leading companies in the country, they also will be showcasing talents and knowledge in the field of analytics. Student employees of the Krenicki Center are paid an hourly wage for the work completed on their assigned project(s). Students interested in applying should meet the following qualifications:
  • The ability to handle large-scale data using software such as R or Python
  • A knowledge of machine learning algorithms
  • The availability to dedicate 10-15 hours per week on assigned project
All Undergraduate and Graduate majors are eligible to apply.  Open to both International and Domestic students. 


The requested Research Statement is not required and will not hinder possible opportunities with our center. If unavailable, please just upload your resume/CV.


"The cutting-edge technologies, information strategies and analytic techniques that I mastered during my Krannert education serve as a foundation for my lifelong career in data analytics."
- Rachel Crouch, Krannert senior
Software developer, GM, August 2016

Featured Projects

Car with an assortment of parts spread around it

Prediction Accuracy

Our team developed mathematical models which supported and augmented the efficiency of legacy heuristics that were followed by retail locations selling spares. Throughout the project we obtained binary results for about more than 14000 SKUs, and determined which SKUs to stock based on various constraints like cost, space, minimum quantity.

Learn more about the Predition Accuracy project

Illustration of model comparisons

Optimization Solution to Minimize Costs

The team looked at how retailers can better handle inventory to suit customer needs while also managing cash flow for more efficient operations. We discovered that our client’s predicted demand had the highest total cost due to poor forecasted demand accuracy. As our model with highly accurate predicted demand showed least total cost, we suggested the client focus their efforts in improving the accuracy of their demand forecasts.

Learn more about the Optimization Solution to Minimize Costs project

Image of store with overlay of time, store, demand as 3 axis

Machine Learning for Demand Forecasting

This study observed the applicability of a deep-learning based workflow to complex scenarios which retailers can face like adding new locations, new products, and other external factors. We built our own workflow from scratch on a problem by using open-source technologies only, and compared how our solution performed versus another data science team’s proprietary solution.

Learn more about the Machine Learning for Demand Forecasting project

Graph of estimating price sensitivity for revenue management

Promotion Effects on Profits

This study’s purpose was to understand the impact of historical promotions on the demand of a product while creating predictive models that best predict demand based on specific parameters. We used a traditional log-log model to provide some insight about elasticity, but the relationship among demand and price is often nonlinear. We predicted our demand model could provide a dynamic decreasing elasticity curve that would generate more revenue and yield better profit than traditional approaches.

Learn more about the Promotion Effects on Profits project

View all projects

Featured Events

Illustration of two people presenting data

MIS & Analytics Big 10 Research Conference

The 2019 MIS and Analytics Big 10 Research Conference provided a forum for our Big 10 partners working in Information System and Analytics. Teams were able to present their findings and exchange views on the latest advances in the field. The conference showcased Information System and Analytics research from world-renowned researchers within the BIG 10 in conjunction with research from top-notch students from each university.

The winning team: team TSM

Esport Data Hackathon: 24 Hour Challenge

Centered on the Purdue University Campus, West Lafayette, IN teams competed in a 24-hour Challenge. Through using cutting-edge technology such as machine learning, AI, AR/VR, NLP, and many more teams developed innovative data solutions that solved problems for Data Visualizations and Predictive Analytics. These solutions were able to support Fan Experience, eAthlete Training & Performance.

Learn more about the Esport Data Hackathon

The winning team of Purdue/IU Case Challenge

Purdue/IU Case Challenge Sponsored by Eli Lilly

Similar to real-world business projects, the STAMINA4 case competition is an intensive, experiential learning opportunity that allows students to showcase their critical thinking and analytical abilities. While also communicating their ideas, and demonstrating mental tenacity through team work. STAMINA4 participants only have four hours to analyze a case and create a presentation to share their recommendations.

Data Science for Business and Economics Conference

Data Science for Business and Economics Conference

This conference was an opportunity to have students be immersed in real-world knowledge from professionals that use data science in their everyday lives. Speakers were featured from professions such as business, economics, statistics, computer science, and other areas.

Learn more about the Data Science for Business and Economics Conference

View all events