MacEwan University statistics students will collaborate with your organization to apply advanced analytical techniques such as principal component analysis (PCA), clustering, and discriminant analysis on clean, pre-prepared datasets. Through this experience, students will generate meaningful insights and translate complex statistical findings into actionable recommendations. Ideal for organizations looking to uncover patterns, optimize operations, or make data-informed decisions. 

Student Skills

Already Acquired

• Matrix algebra
• Statistical computing in R
• Data cleaning, wrangling, and visualization

To Be Developed

• Principal Components Analysis (PCA)
• Factor analysis
• Discriminant and cluster analysis
• Canonical correlation analysis
• Machine learning applications
• Data storytelling and stakeholder communication
• Project management and collaboration with external partners

• Application of multivariate and machine learning techniques
• Final report and presentation
• Insights communicated in a meaningful, stakeholder-friendly way