Data mining for business analytics

BSTA478/BSTA678
Closed
Main contact
Concordia University
Montreal, Quebec, Canada
Professor, John Molson School of Business
1
Timeline
  • January 14, 2020
    Program start
  • March 7, 2020
    Students present of a first draft of their project.
  • April 11, 2020
    In-class presentation
  • April 21, 2020
    Final report presentation
  • April 21, 2020
    Program end
Program
6 projects wanted
Dates set by program
Preferred companies
Anywhere
Any
Any industries

Program scope

Categories
Information technology Data analysis Sales strategy Marketing strategy
Skills
python big data sas em r data mining
Learner goals and capabilities

Student-consultants will analyze data sets using state-of-the-art technologies to identify trends, and submit recommendations based on predictive models that can enhance decision-making in your organization.

Learners

Learners
Undergraduate
Any level
50 learners
Project
40 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

Final deliverables will include:

  • A slide deck with conclusions and recommendations for a 15-minute consulting presentation.
  • A written report with details of the predictive models that were developed, including insights and overall recommendations that are inferred from such models.
  • All applicable source code.
Project timeline
  • January 14, 2020
    Program start
  • March 7, 2020
    Students present of a first draft of their project.
  • April 11, 2020
    In-class presentation
  • April 21, 2020
    Final report presentation
  • April 21, 2020
    Program end

Project examples

Requirements

Data Mining for Business Analytics has become one of the most dynamically growing areas of business and knowledge discovery. Data Mining, an art-and-science of identifying useful patterns in data, has become an indispensable tool for companies operating in a highly competitive environment.

Beginning in late February, a class of student-consultants in groups of four will spend 60 hours per team performing a thorough investigation of data sets to tackle a managerial question. Tools such as SAS EM, and Python packages will be leveraged for data modeling, machine learning, and visualization.

Based on the information that you provide, groups will develop predictive models and managerial insights into key aspects of your organization. Each team will propose solutions and implementation strategies to improve your decision making processes and outcomes.

Example projects include, but are not limited to:

  • Analysis of customer segmentation relative to different products and services, to enhance marketing campaigns and refocus your products/services.
  • Investigate predictive models to understand trends in sales, attrition rates, and profits that impact your business.
  • Propose new ways to visualize data through tables and plots that can provide new insights for managers.
  • Reduce customer churn.
  • Maximize revenue through up-sell and cross-sell.
  • Determine sales trends.
  • Accurately predict customer behaviours.
  • Improve new customer acquisition.

Additional company criteria

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

  • Q1 - Checkbox
  • Q2 - Checkbox
  • Q3 - Checkbox
  • Q4 - Checkbox
  • Q5 - Checkbox