This applied statistics project connects your organization with upper-year undergraduate students trained in data science and multivariate analysis. Students will use advanced statistical techniques and machine learning algorithms to uncover actionable insights from a dataset you provide. Working in small, self-assigned teams, students will apply techniques such as Principal Component Analysis (PCA), clustering, and discriminant analysis to explore data patterns, reduce dimensionality, and identify drivers of key outcomes. The focus is on real-world application and communication of findings in a clear, stakeholder-ready format.   Student Capabilities Pre-existing skills: Statistical computing and data visualization in R Matrix algebra and multivariate statistical foundations Data cleaning, wrangling, and exploratory analysis Skills developed through the project: Principal Component and Factor Analysis Discriminant and Cluster Analysis Canonical Correlation Machine Learning Applications (e.g., classification, unsupervised learning) Executive communication and stakeholder storytelling Team-based project management How Students Will Support Your Organization Students will: Analyze a clean, pre-prepared dataset provided by your organization Apply multivariate and machine learning methods to uncover patterns, trends, and relationships Visualize results using compelling charts and summaries in R Translate complex findings into clear, actionable insights for non-technical stakeholders Work collaboratively with you to ensure alignment on project goals and outcomes Time Commitment 25 hours per student (approx. 100 hours per team of 4) Minimal lift for your organization: Provide a clean dataset and brief background at project start Optional mid-point check-in Attend final presentation and offer feedback

We are seeking project submissions for a course-based WIL project in Bookkeeping and Accounting. Please note that there is no financial cost associated with your participation. Instead, we seek your invaluable commitment in the form of time and expertise. This course is designed to provide learners with the opportunity to integrate and apply accounting knowledge and skills. A game-based project that involves the application of accounting concepts, principles and techniques will be presented in this graded course. Learners will develop, build and evaluate accounting transactions, events and financial statements based on the application of accounting concepts, principles and techniques learned in the previous terms.

We are seeking project submissions for a course-based WIL project in Professional Communication on a business analysis topic. Please note that there is no financial cost associated with your participation. Instead, we seek your invaluable commitment in the form of time and expertise. This course will provide students with an opportunity to apply the different skills and knowledge they have learned throughout the program in a real-world setting.  Working in pre-assigned groups, students will deliver a set of standard business analysis deliverables, as well as specific deliverables outlined by the partner agency. A real ongoing project has been written as a case study and is used in class to be analyzed by the students. An external project advisor provides guidance and coaching to the teams as they develop their work plans and deliverables.  Groups will compete to deliver the best product for the same project.  At the end students present their findings to the partner agency.

This course provides students with the opportunity to apply the different skills and knowledge they have learned throughout the Global Logistics Management Professional Development Certificate program, in a real-world setting .Working in pre-assigned teams, students will deliver a set of specific deliverables outlined by the partner agency to help organizations deliver on their operational objectives. Teams will compete to deliver the best product for the same project. Several teams of 3 to 4 students will work on the project so the Community Partner will receive multiple sets of recommendations.