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Analytics for Business Decision Making - Fall 2023

Business 4045-82861
Closed
George Brown College
George Brown College
Toronto, Ontario, Canada
Richard Boire
Richard Boire
Professor
(8)
5
Timeline
  • September 15, 2023
    Experience start
  • December 15, 2023
    Experience end
Experience
6/6 project matches
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries

Experience scope

Categories
Customer segmentation Marketing analytics Machine learning Data analysis Data science
Skills
programming data visualization/reporting
Learner goals and capabilities

Many organization face the challenges of developing information into knowledge, and then converting that intelligence into actionable decisions. Organizations demand the interdisciplinary skills of professionals that can find insights through data, and are capable of telling stories about data that can assist decision makers across various business divisions. More importantly, students will be able to create a narrative on how advanced analytical techniques can be used to provide actionable business solutions.


Your company can be part of a project by accepting to assign a task to a multidisciplinary team of students for a term of 12 weeks.


Learners

Learners
Post-graduate
Any level
30 learners
Project
120 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

· 10 Deliverables over a 15 week engagement period that include:

· A project proposal document

· A fully detailed written report

· An on-site executive summary presentation

Project timeline
  • September 15, 2023
    Experience start
  • December 15, 2023
    Experience end

Project Examples

Requirements

What is included?

· Up to a 4 student multi-disciplinary teams

· 400 Hours of work or more

· A Supervising professor who supports the students and guides the project

· 10 Deliverables over a 15 week engagement period that include:

· A project proposal document which includes:

  • background and introductions
  • problem definition and challenge
  • data requirements and data sources
  • methodology and approach
  • detailed analytical output regarding the use of both advanced and non-advanced analytics with key findings and insights, and how they benefit the organization
  • recommendations and conclusions

· An on-site executive summary presentation


Types of Projects

· Development of Reporting Dashboards with drill down analytical capabilities

· Development of predictive analytics solutions using machine learning techniques which include use of artificial intelligence

· Customer Segmentation Development including clustering-type solutions

· Customer Relationship Management Campaign Design and Implementation

Additional company criteria

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

Employer has source data for students to work with