Decision-making for Engineers and Scientists - 2023 entry
MODULE TITLE | Decision-making for Engineers and Scientists | CREDIT VALUE | 15 |
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MODULE CODE | CSMM222 | MODULE CONVENER | Prof Hylke J Glass (Coordinator) |
DURATION: TERM | 1 | 2 | 3 |
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DURATION: WEEKS | 11 |
Number of Students Taking Module (anticipated) | 50 |
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DESCRIPTION - summary of the module content
Decision-making is an integral part of our daily life and profoundly influences our professional mastery. This module focusses on decision-making for engineers and scientists who are active in studies of mineral exploration, resource estimation, mining engineering, mineral processing, and tailings management.
This module imparts valuable knowledge of essential techniques for understanding the design of sampling campaigns and information contained in data in order to make decisions.
Technical decision-making skills are useful during individual research projects which follow the taught modules of the CSM MSc programmes and in later life.
As a prerequisite to engage fully with the module, you are expected to have a basic appreciation of statistics and mathematics. Guidance on appropriate self-study to improve knowledge in these areas can be provided as required.
AIMS - intentions of the module
The module seeks to provide:
- insight into the sampling of solid or particulate materials.
- enhanced insight into decision-making about physicochemical properties or processing behaviour of a metal- or mineral-bearing materials.
- understanding of the effect of risk, uncertainty, and bias on decision-making.
- improving confidence in data through a process of reconciliation.
- solutions for practical applications:
- analysis of sample collection in the field and in a processing plant.
- determination of a fit-for-purpose sample size.
- simulation of data variability.
- design of experimental programmes in the laboratory.
- interpretation of experimental results under consideration of uncertainty.
- building models through regression.
- identification of outliers and trends in datasets.
- adjustment of data under consideration of process constraints.
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)
On successful completion of this module, the learner should be able to:
Module Specific Skills and Knowledge
1. select suitable techniques for sample collection.
2. calculate sampling variance and required sample size.
3. distinguish between outlier and spatial or temporal trends in data.
4. analyse sample variability with bootstrapping and jackknifing.
5. create factorial experiments.
6. develop models with regression.
7. create factorial experiments.
8. determine parameter significance with Analysis of Variance.
9. apply classic parametric and non-parametric tests.
10. understand Bayesian updating.
11. perform data reconciliation.
Discipline Specific Skills and Knowledge
12. select appropriate methods for the analysis, modelling, and solution of practical engineering problems.
13. apply computer-based decision-making for applications across the mining value chain.
Personal and Key Transferable / Employment Skills and Knowledge
14. analyse and present data in a way that facilitates effective decision-making.
15. communicate effectively and persuasively using the full range of currently available methods.
SYLLABUS PLAN - summary of the structure and academic content of the module
Topics:
- Field sampling techniques
- Plant sampling techniques
- Variability, uncertainty, and bias
- Statistical distributions
- Sampling variance
- Sample size optimisation
- Inference from sample analyses
- Bootstrapping and jackknifing
- Classic parametric and non-parametric hypothesis testing
- Bayesian updating
- Experimental design
- Regression models
- Outlier detection
- Mass balance closure
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities | 30 | Guided Independent Study | 120 | Placement / Study Abroad |
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DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category | Hours of study time | Description |
Scheduled learning & teaching activities | 12 | Lectures |
Scheduled learning & teaching activities | 18 | Computer Tutorials |
Guided independent study | 120 | Private Study |
ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Computer exercises | Duration: 18 hours | Verbal, or electronic if required | |
SUMMATIVE ASSESSMENT (% of credit)
Coursework | 100 | Written Exams | 0 | Practical Exams |
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DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment | % of Credit | Size of Assessment (e.g. duration/length) | ILOs Assessed | Feedback Method |
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Decision-making coursework | 75% | Length 3000 word equivalent | 1-15 | Electronic or written feedback |
In-class test | 25% | Duration 1 hour | 1-11 | Electronic or written feedback |
DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment | Form of Re-assessment | ILOs Re-assessed | Time Scale for Re-assessment |
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Decision-making coursework | Decision-making coursework | 1-15 | Aug Ref/Def period |
In-class test | In-class test | 1-11 | Aug Ref/Def period |
RE-ASSESSMENT NOTES
If a student is referred or deferred, the failed / non-completed component(s) will be re-assessed at the same weighting as the original assessment.
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener
information that you are expected to consult. Further guidance will be provided by the Module Convener
Web based and Electronic Resources:
Reading list for this module:
Type | Author | Title | Edition | Publisher | Year | ISBN |
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Set | Davis, John C. | Statistics and Data Analysis in Geology | 3rd | 2002 | ||
Set | Hair, Joseph F. | Multivariate Data Analysis | 1998 | |||
Set | Kachigan, Sam | Statistical Analysis: An interdisciplinary introduction to univariate and multivariate methods | 1986 | |||
Set | Little, Roderick J. A. and Donald B. Rubin | Statistical Analysis with Missing Data | 3rd | Wiley | 2020 |
CREDIT VALUE | 15 | ECTS VALUE | 7.5 |
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PRE-REQUISITE MODULES | None |
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CO-REQUISITE MODULES | None |
NQF LEVEL (FHEQ) | 7 | AVAILABLE AS DISTANCE LEARNING | No |
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ORIGIN DATE | Monday 24th January 2022 | LAST REVISION DATE | Thursday 2nd February 2023 |
KEY WORDS SEARCH | Sampling theory; sampling statistics; data analysis; statistical distributions; bias; uncertainty; error; sampling variance theory; sample size optimisation; sampling techniques; data reconciliation; regression analysis; ANOVA; principle components... |
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Please note that all modules are subject to change, please get in touch if you have any questions about this module.