Description
Summary
Large volumes of data is often generated and captured in mining. However, it is uncommon for the value that this data contains to be fully uncovered and utilised to improve processes. The report “How digital innovation can improve mining productivity” by McKinsey & Company (2015) found that less than 1% of operational data obtained by mining companies is appropriately used.
This webinar will enable participants to apply theoretical knowledge of statistics to resolve applied mining problems. Participants will gain an understanding of what data to collect, how to measure it, and with what frequency? The data cleansing, validation, exploration, visualisation and integration phases will also be presented. In doing so, participants will be exposed to excel spreadsheets in order to appropriately find patterns, gain insights and communicate results. A number of case studies will be presented which demonstrate how data driven decision making can be unitised to improve mining processes.
This course requires participants to undertake numerous calculation based exercises to effectively demonstrate the impact of each of the key levers that will be addressed. All participants will be provided with template/pre-filled excel spreadsheets to minimize time spent on data entry. Good use of excel is therefore required.
Duration: 5 Hours
Access: 90 Days
Price: $399 USD
Category: Mining
Who This Course is For
This course is designed for participants with mining industry exposure that are genuinely interested in using data to find patterns and gain insights into how these can potentially improve mining processes. It is also recommended for mining professionals who want to refresh their knowledge on data analytics.
This may include but not limited to the following:
- Mine planners/schedulers: To make better assumptions relating to future productivity, machine and operator performance.
- Maintenance planners: Useful for understanding trends in machine failure and downtime data.
- Environmental personal: Making sense and correlating sample data, dust monitoring information, vibration data, and other environment monitoring data.
- Health and Safety representatives: Understanding trends in health and safety reporting of incidents and near-misses.
- Process plant operators/engineers: May assist in understanding process bottlenecks.
Presenter
An edumine author with 14 years experience in the Mining Industry as an Engineer, Consultant and Academic
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Objectives
Course Objectives
After successfully completing this course students will be able to:
- Analyse, cleanse and validate the data associated with a mining problem.
- Apply statistics to gain insights into a practical mining problem.
- Apply a working knowledge of spreadsheet programs to solve practical mining problems.
Communicate results of mining studies at an appropriate level.
Course Content
This course is structured in three modules. For each module, you will view a video and then complete a short quiz to practice what you’ve learned. You will need to achieve a score of at least 60% on each quiz to move forward to the next module and you will need an average score of 75% on all quizzes to mark the course as complete and in order to receive a certificate of completion.
Course Outline
Module 1 - Introduction to Data Analytics
- What is Data Analytics?
- Why Data Analytics?
- The Opportunity
- Types of Data
- Terminology
- Skills of Data Specialists
- Uses of Data Analytics
- Data in Mining
- Types of Data Analytics
- Key Questions Addressed by Analytics
- Life Cycle of an Analytics Project
Module 2 - Basic Statistics
- Range (Truck cycle time example)
- Mean, median, mode (Truck cycle time example)
- Distribution/Skew
- Variance (Gold price example)
- Standard deviation (Gold price example)
- Covariance (Rock strength vs grind time example)
- Correlation (Rock strength vs grind time example)
Module 3 - Advanced Statistics
- Regression (Haul road condition vs tyre life example)
- R squared (Haul road condition vs tyre life example)
- Standard error of the estimate (Haul road condition vs tyre life example)
- Statistical testing
- t-test (Dust levels example)
- ANOVA: f-ratio (Digger operator and instantaneous dig rate comparison)
Certificate
By completing/passing this course, you will attain the certificate On Demand Course Certificate of Completion
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