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Work Integrated Learning in Data Analytics

Open Closing on June 26, 2023
RMIT University
Melbourne, Victoria, Australia
Yan Wang
WIL Course Coordinator and Program Manager 5
  • Post-graduate
  • 70 learners; teams of 4
  • 120 hours per learner
  • Academic credit
Preferred companies
  • Anywhere
  • Any
  • Any
  • 2/7 project matches
Data Data visualization Data analysis Data modelling Customer segmentation Machine learning
Project timeline
  • July 16, 2023
    Experience start
  • March 28, 2023
    Problem Statement
  • May 2, 2023
    Progress presentation
  • August 6, 2023
    Project Scope Meeting
  • August 13, 2023
    Project Plan
  • November 9, 2023
    Final report and final presentation
  • November 10, 2023
    Experience end
What can our learners do for your company?

This work integrated learning course addresses the application of analytics and statistics in a real world situation for final year Master of Analytics, Master of Statistics and Operations Research and Bachelor of Analytics students. The projects include data pre-processing and cleaning, data wrangling and exploration, data visualisation (such as dashboard), time series analysis, multivariate analysis, predictive modelling, quality control, regression, machine learning, , experimental design and optimisation. Computation tools include in particular querying language (SQL), R, Python, SAS, and Matlab.

Our WIL projects have helped big and small companies improving their service and efficiency using current analytics and data science techniques. The WIL also provides a pathway for industry to recruit excellent graduates. Some employers report that our students bring fresh ideas and approaches to the workplace, sharing the latest research and thinking in the field they study.

Learner skills
Programming languages, Statistical analysis, Machine learning, Data analytics, Data modeling
What would your company get at the end of this experience?

A well documented final report and final presentation from our students.

Project Examples

Industry projects can be relatively small in scope and (at a minimum) only require the affiliated organisation to provide a data set, or research problem that has been encountered and would be suitable for the students to solve.

In this course, students apply a wide range of data analytical methods and tools covered in the whole program.

Example 1: Data Visualisation project. A train link company was interested in how the on-time running of the networks can be best visualised, and where the pinch points are in the networks. Our students utilised general data visualisation and geospatial data visualisation tools to help industry partners locating the worst performing services and if some services have to be removed.

Example 2: Water Utility project. One of the largest water supply companies in Melbourne, has a yearly maintenance program for sewer reticulation cleaning including key customers and key events. The company was interested in finding out how effective these programs are, and if the frequency of blockages in these assets has come down as a result of preventative maintenance programs. Our students deciphered whether prevention programs reduce the need for responses by making use of multivariate analysis of variance techniques. Time-to-failure analyses highlighted whether prevention programs can extend the time before a failure is seen.

Example 3: Customer Segmentation for Supermarkets. One of the largest supermarket chains in Australia. Our students have built a customer segmentation model that has been used by sales to segment their fresh produce customers based on behaviour, types of products and amounts of products purchased.

Example 4: High Education Enrolment Forecast for RMIT University.

Example 5: Customer Service Improvement Using Natural Language Processing.

Required questions to apply

Companies must answer the following custom questions in order to apply to this experience:

Get involved in the assessments of students' progress and final report

Be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.

Be able to set up regular meetings with students to answer their domain questions

Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.