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Data-Driven Business Solutions

BUS 4045
Open 1 spot left
George Brown College
George Brown College
Toronto, Ontario, Canada
Mohsen Selseleh
Professor
5
Timeline
  • January 17, 2025
    Experience start
  • May 1, 2025
    Experience end
Experience
1 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries

Experience scope

Categories
Machine learning Data visualization Data modelling Data science
Skills
effective communication data cleansing predictive modeling business analytics business continuity planning machine learning data visualization data analysis problem solving dashboard
Student goals and capabilities

This experience is designed for postgraduate students specializing in business analytics, equipped with advanced skills in data cleaning, visualization, machine learning, and predictive modeling. Learners are capable of transforming raw data into actionable insights, enabling businesses to make informed decisions. Through collaborative problem-solving and effective communication, students will apply their analytical expertise to real-world projects, delivering solutions that address specific business challenges within a limited timeframe.

Students

Students
Post-graduate
Intermediate levels
50 students
Project
50 hours per student
Students self-assign
Teams of 6
Expected outcomes and deliverables
  • Comprehensive data analysis reports with actionable insights
  • Interactive data visualizations and dashboards
  • Predictive models with business impact assessments
  • Data-driven strategy recommendations
  • Presentation of findings to stakeholders
Project timeline
  • January 17, 2025
    Experience start
  • May 1, 2025
    Experience end

Project Examples

Requirements
  • Develop a predictive model to forecast customer churn and suggest retention strategies
  • Create an interactive dashboard for sales performance analysis across different regions
  • Analyze social media data to identify emerging consumer trends
  • Optimize inventory management using historical sales data
  • Evaluate marketing campaign effectiveness through data analysis
  • Conduct a competitive analysis using publicly available data
  • Identify key factors influencing product returns and propose solutions
  • Assess the impact of pricing strategies on customer purchasing behavior