Business Insights Through Data Mining – Fall 2025

ODS 370
Open Closing on September 11, 2025 / 1 spot left
Salem State University
Salem, Massachusetts, United States
Professor
4
Timeline
  • September 17, 2025
    Experience start
  • December 2, 2025
    Experience end
Experience
1 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any company type
Business & management, Business services, Consumer goods & services, Education

Experience scope

Categories
Data visualization Data analysis Data modelling Data science
Skills
application remediation
Learner goals and capabilities

This experience is designed for learners from the Data Mining course at Salem State University, who are skilled in both supervised and unsupervised machine learning techniques. Participants are adept at applying classification models, evaluating model performance, and conducting clustering and association rule mining. They are also proficient in data preprocessing and visualization. By collaborating with industry partners, learners can apply their skills to real-world datasets, providing actionable insights and recommendations that can enhance business decision-making processes.

Learners

Learners
Undergraduate
Beginner levels
9 learners
Project
10-20 hours per learner
Educators assign learners to projects
Individual projects
Expected outcomes and deliverables
  • Comprehensive data preprocessing and cleaning report
  • Classification model performance analysis with visualizations
  • Clustering analysis report with data visualizations
  • Association rules mining summary with actionable insights
  • Final presentation and written report with recommendations
Project timeline
  • September 17, 2025
    Experience start
  • December 2, 2025
    Experience end

Project Examples

Requirements
  • Customer segmentation analysis for targeted marketing strategies
  • Predictive modeling for product demand forecasting
  • Analysis of transaction data to identify frequent itemsets and association rules
  • Development of a classification model to predict customer churn
  • Exploration of sales data to uncover patterns and trends
  • Evaluation of model performance for a credit scoring system
  • Clustering of social media data to identify emerging topics
  • Data-driven recommendations for optimizing inventory management