Ohio University
Ohio University
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Knowledge Discovery & Data Management

11220
Open Closing on January 20, 2025 / 2 spots left
MacEwan University
MacEwan University
Edmonton, Alberta, Canada
Reagan Lusk He / They
Experiential Learning Facilitator
(4)
7
Timeline
  • January 20, 2025
    Experience start
  • April 5, 2025
    Experience end
Experience
1/3 project matches
Dates set by experience
Preferred companies
Canada
Startup, Large enterprise, Non profit, Small to medium enterprise
Any industries

Experience scope

Categories
Data visualization Data analysis Data modelling
Skills
python (programming language) predictive modeling data analysis knowledge discovery power bi hypertext markup language (html) dashboard data mining data management
Learner goals and capabilities

Is your organization seeking some extra support with analyzing data from a fresh perspective in a controlled manner? If so, we have 4th year business students that are seeking the opportunity to help you by going through a complete model development process to clean data, develop prediction models, and present results using dashboards (Excel or PowerBI). In this course students learn to develop proficiency in data mining techniques and the knowledge discovery process.



Learners

Learners
Undergraduate
Intermediate, Advanced levels
40 learners
Project
35 hours per learner
Educators assign learners to projects
Teams of 5
Expected outcomes and deliverables

Students required to submit the following documents as part of the final report:

  • Dataset (Raw Form)
  • Python Scripts
  • Python Script with Results saved an HTML
  • Dashboard in PowerBI (If required by Community Partner)
  • Final Report (Word Format) 


Project timeline
  • January 20, 2025
    Experience start
  • April 5, 2025
    Experience end

Project Examples

Requirements

As a guide to consider what projects your organization would be interested in submitting, below are some examples of potential projects:

  • Customer segmentation based on purchasing behaviour to tailor marketing strategies.
  • Fraud detection in financial transactions using classification techniques.
  • Product recommendations system for an e-commerce platform to suggest products to customers based on their browsing and purchasing history.
  • Predictive maintenance for manufuacting equipment to schedule maintaince and reduce downtime.

Additional company criteria

Companies must answer the following questions to submit a match request to this experience:

You agree to provide relevant data sets in relation to the identified issue.

You agree to attend in-person or virtually a final project presentation delivered by the student group.

You agree to ongoing communication with the course intructor and requests for additonal information or data as needed.