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Short course

Practical Data Analytics and Machine Learning

PD Hours
Short course

Practical Data Analytics and Machine Learning

PD Hours

Get more value from your operation through data analytics, machine learning, simulation and optimisation.


Quick facts

Duration Delivery Course Type Next Intake PD Hours Language
20 hours
5 weeks
100% online
Short course
TBC 2024
Up to 20

Course overview

With the resources sector committed to gaining more value from data collected across mining operations and the broader business, AusIMM’s Practical Data Analytics and Machine Learning short course is designed to share practical insight into the fundamentals of data analytics, machine learning, simulation and optimisation. Designed and delivered by industry experts, the program uses real world examples and hands-on access to industry leading tools. Participants will also learn about mechanisms for keeping data private whilst participating in collaborative projects for industry-wide learning.

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Course pricing

A discount of 10% is available for groups of 3 or more participants from the same organisation who enrol for the same course intake in a single booking. This discount will be automatically applied at checkout.
Price inclusive of 10% GST
Associate membership and enrolment bundle
Price is inclusive of 10% GST
Price inclusive of 10% GST
Group training
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Your badge links to a detailed outline of the course, showcasing and validating your new skills in a format that employers and colleagues can easily access and trust.

Who should attend?

Plant Engineers and Operators 
Learn how to use data to inform business-critical activities using data already available within the business.  

Operational Improvement Specialists 
Audit and analyse operations to inform business cases for change.

Senior Operations and Plant Management 
Learn how to use your operation’s routinely collected data to understand historical performance and inform predictions with insights for practical improvements.

Learning objectives

  • Recognise the key terminology used in Data Analytics (DA) and Machine Learning (ML) 
  • Identify potential applications for DA and ML in mining 
  • Describe the characteristics and applications of mining related measurements and interpolated data, i.e. variability, uncertainty and error and when to use mining related professional judgment 
  • Explain the difference between ML models and traditional models for equipment process models 
  • Describe good practices of ML
  • Develop an ML model for a processing plant operation based on a Process Historian data sample 
  • Utilise an ML model in a mineral processing flowsheet simulator 
  • Develop an understanding of use cases for simulations of minerals processing using ML models 
  • Discuss when ML models should be retrained with more or different data

Course structure

The five-week course is a collaborative, hands-on online learning experience, taking learners approximately 20 hours to complete. Please note the live sessions are not mandatory, the virtual classrooms will be recorded and available on the same day for participants who are unable to attend the live event. Course content includes:

  • Live virtual classrooms
  • Additional resources and readings (case studies, videos, articles)
  • Access to hands-on learning activities using Orica’s cloud-based software
  • Group discussion forums
  • Learning activities in the form of short test questions

Module 1

It’s all about the data

Module 2

Turning data into a model

Module 3

Turning models into predictions

Module 4

Closing the loop – reconciliation and next steps

Download the course brochure

Practical Data Analytics and Machine Learning


Greg Shapland

Manager – Technology (IES), Orica
Greg Shapland is responsible for Data Analytics and Software Development of the Integrated Extraction Simulator (IES), a web application that simulates end-to-end processes from Blast to Concentrate. IES uses a broad range of ore body, mining and minerals processing data to provide a digital representation of the value chain for users to interrogate and gather insights.

Greg brings over 25 years' experience in managing and implementing systems and process improvements using mining technologies, data analytics and IT systems.

He holds a B.E. Civil (Hons) and an M.B.A. in Strategy and Finance and is an accredited Project Management Professional.

Edwin Koh

Technical Specialist, Orica
Edwin Koh graduated with a Degree in Chemical Engineering in 2018 where he won the undergraduate thesis of the year in the Engineering Faculty for applied mathematical modelling. Edwin brings modelling experience from various fields including rheology, environmental, computational fluid dynamics and medical diagnosis. Edwin had built up four years of experience coding in MATLAB for various research teams at The University of Queensland (UQ).

Edwin joined the IES team in summer 2018/19 to investigate the feasibility of machine learning models for minerals processing. Following success with the IES summer vacation project Edwin decided to further his studies in this area towards a PhD at UQ under Prof Geoff McLachlan and Dr Eiman Amini. In his PhD, Edwin applies state-of-the-art machine learning models in the IES platform for industry using Tensorflow. These novel methodologies have been published in various journals and conferences, establishing IES at the forefront of utilising machine learning in the minerals processing.

Eiman Amini

Manager – Technical Services, Orica
Dr Eiman Amini is a mineral processing specialist with experience in process optimisations and scale-up which includes process model development, project management, geometallurgy, applied research and innovation, value chain diagnostics, control data analysis and plant surveys.

Eiman has worked in several countries with extensive exposure to comminution and flotation process modelling, simulation, forecasting and optimisation. Eiman also has history of working effectively in cross-functional and cross-cultural environments with consistent success in research and innovation, coaching laboratory teams and process performance improvement in research intuitions and companies such as JKMRC, SGS and Rio Tinto.

Robert Watkins

Senior Developer – Technology (IES), Orica
Robert is responsible for the design and implementation of the Integrated Extraction Simulator (IES) project; leading the IES development team to deliver IES and working with researchers to implement their process models in the IES framework. Robert has worked on the IES project since late 2012 and is a key member of the team responsible for delivering IES and enabling the next generation of simulation technology for the mining industry.

He has 20 years of experience in the software development industry, working with companies such as Mincom, Suncorp and to develop high-performing software solutions.

As the development lead, Robert has overseen and supported the development of the ML capabilities within IES, including the integration of ML models into the IES platform.

Nick Beaton

Senior Manager – Integrated Extraction Simulator, Orica
Nick Beaton is the senior manager within Orica responsible for IES. Nick has enjoyed a 30-year career in the development and commercialisation of technology for the global mining industry.

Nick has professional experience that spans senior appointments with Datamine, Mincom, KPMG and CAE Mining, with postings to the UK, South Africa, Germany, the USA and Switzerland. In addition to his extensive mining industry experience, Nick also worked in management consultancy in Europe, leading pan-European projects in business restructuring and ERP implementations for major manufacturing companies.

Frequently asked questions

The course will be run entirely online via a cloud-based Learning Management System (LMS) which can be accessed via computer, tablet or phone. Participants will simply need to have a working Internet connection and a computer, tablet or phone with sound to access the course.

Participants will require access to Microsoft Excel and some of its add-ons in order to complete one of the learning activities for Module 1. For other modules participants will be granted temporary access to Orica's cloud-based application.

The full course is estimated to take about 20 hours of learning. Participants will have access to the course platform for five weeks to complete all modules. 

We aim to run two intakes each year. 

No, all virtual classrooms are recorded and made available on the learning platform for participants who are unable to attend the live session.

At the moment, no, but we will be looking at delivering the course in other languages in the near future. 

Participants can earn professional development (PD) hours for undertaking the course. One contact hour of technical content is equivalent to one PD hour. 

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