Work Integrated Learning in Data Analytics
General
- Graduate
- 50 learners; teams of 4
- 120 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 4/4 project matches
- Anywhere
- Academic experience
- Any
- Any
Categories
Project timeline
-
March 18, 2022Experience start
-
August 29, 2021Problem Statement
-
September 19, 2021Project Plan
-
September 26, 2021Progress report
-
March 25, 2022Problem Statement
-
April 15, 2022Project Plan
-
June 5, 2022Experience end
Timeline
-
March 18, 2022Experience start
-
August 29, 2021Problem Statement
The problem statement should identify the question/problem, and a brief description of the data set(s). This describes what the problem is, who the problem affects, why research is needed, and how your project will contribute to solving it. A problem statement serve as the basis for your project pr
-
September 19, 2021Project Plan
Aims and objectives;literature review and research plan.
-
September 26, 2021Progress report
A clear presentation of the progress and stage achievements.
-
March 25, 2022Problem Statement
The problem statement should identify the question/problem, and a brief description of the data set(s). This describes what the problem is, who the problem affects, why research is needed, and how your project will contribute to solving it. A problem statement serve as the basis for your project pr
-
April 15, 2022Project Plan
Meeting between students and company to confirm: project scope, communication styles, and important dates.
-
May 6, 2022Project progress presentation
Aims and objectives;literature review and research plan.
-
June 4, 2022Progress report
A clear presentation of the progress and stage achievements.
-
June 5, 2022Experience end
Overview
- Details
-
The project addresses the application of analytics and statistics in a real world situation and is a capstone project for final year Master students. Our students have extensive knowledge in data extraction and preprocessing, data wrangling and exploration, data visualization, machine learning, forecasting, multivariate analysis, quality control and experimental design. Computing skills include querying language (SQL), scripting language (R, Python) & statistical language (R, SAS).
- Learner skills
- Programming languages, Statistical analysis, Machine learning, Data modeling, Data analytics
- Deliverables
-
A well documented final report and a final video presentations from our students.
Project Examples
In this course, students apply a wide range of data analytical methods and tools covered in the whole program. This includes in particular time series analysis, multivariate analysis, predictive modelling, quality control, regression, machine learning, data visualisation, experimental design and optimisation. Computation tools include in particular querying language (SQL), R, Python, Matlab, SAS and SPSS.
Example 1: Data Visualisation project. A train link service was interested in how the on-time running of the trainlink 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 , 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 Manhole gas check maintenance program is an annual program. The industry was interested in finding out how effective these programs are, that is, how often these reticulation lines and manholes report a blockage after cleaning, 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 will be used by sales to segment their fresh produce customers based on behaviour, types of products and amounts of products purchased. The project aims to understand customer behaviour, and therefore help Coles for future promotions at their target market.
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
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.
Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.
Be able to set up regular meetings with students to answer their domain questions
Get involved in the assessments of students' progress and final report
Our students' expertise are on data analytics, including data wrangling, data preprocessing, data visualisation and data modelling (time series, multivariate analysis, forecast, machine learning etc). We request industry to have data sets ready for students to access and analyze, or know exactly where students can access the relevant datasets for their analyses without difficulties.