Capstone Project for Business Analytics

BUSS 5802
Closed
Cape Breton University
Sydney, Nova Scotia, Canada
ER
Associate Professor of Data Analytics
(4)
5
Timeline
  • September 27, 2022
    Experience start
  • October 5, 2022
    Progress report
  • October 19, 2022
    Meeting with the company
  • November 23, 2022
    Second progress report
  • December 3, 2022
    Experience end
Experience
7/10 project matches
Dates set by experience
Preferred companies
Anywhere
Startup, Non profit, Large enterprise, Any, Small to medium enterprise
Education, Banking & finance, Hospital, health, wellness & medical, It & computing
Categories
Machine learning Artificial intelligence Databases Data visualization Data analysis
Skills
business analytics machine learning data visualization data analysis problem analysis
Learner goals and capabilities

The capstone project provides students with an opportunity to apply and integrate concepts learned in the Business Analytics program to solve a business problem using data analytics tools and solutions. The overall aim of the capstone project is to help students relate acquired business analytic knowledge to real business problems. This looks like an internship, a learning experience well-recognized for its impact on the student’s application of classroom knowledge to the world of work, and student’s knowledge of careers and critical career success factors. This course is an excellent opportunity for students to practice problem-solving skills. The course uses Python as the analytical tool to formulate and solve problems based on a variety of mathematical modelling techniques. Companies can define projects in data visualization, data analytics, database systems, data analysis, cloud-based services, software development, and general business analytics projects.

Learners
Post-graduate
Any level
40 learners
Project
50 hours per learner
Learners self-assign
Teams of 3
Expected outcomes and deliverables
  • Data analysis using statistical models, machine learning algorithms, and AI-based techniques
  • Visualizing data using Tableau software
  • Creating data pipeline including but not limited to data cleaning, data preparation, and data analysis
  • Technical documents as the result of data analysis
Project timeline
  • September 27, 2022
    Experience start
  • October 5, 2022
    Progress report
  • October 19, 2022
    Meeting with the company
  • November 23, 2022
    Second progress report
  • December 3, 2022
    Experience end
Project Examples
  • Applying machine learning techniques in different domains including Healthcare, Education, and Business
  • Clustering products of a company by different clustering algorithms
  • Predicting the salary of new employees based on previous historical data

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

What are your expectation from the students during the course of the internship (3 months)?

Do you know that this internship is virtual and will be done remotely by students?

Could you please write down the list of software tools that you expect the students to work on for your project?