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MODULE TITLE	Sampling Theory and Data Analysis	CREDIT VALUE	15
MODULE CODE	CSMM433	MODULE CONVENER	Prof Hylke J Glass (Coordinator), Dr Robert Fitzpatrick

DURATION: TERM	1	2	3
DURATION: WEEKS	3	0	0

Number of Students Taking Module (anticipated)	10

DESCRIPTION - summary of the module content

The module gives graduates from a range of disciplines the opportunity to study sampling theory and statistics and data analysis. This module will provide valuable knowledge of techniques which are applicable across a range of disciplines and are of vital importance for designing experiments, understanding and interpreting experimental data and accurately assessing the performance of mineral processing plants. This will be invaluable for the individual research project undertaken as a part of the MSc Mineral Processing programme and in later life.

When linked to module CSMM434, it forms part of the specialist training for the MSc in Minerals Processing.

Students are expected to have a basic knowledge of statistics and mathematics to fully engage with this module. Guidance on appropriate self-study to improve knowledge in these areas can be given if desired.

AIMS - intentions of the module

This module has been designed to develop an understanding of sampling and data analysis. The aim is to give students an appreciation of the importance of careful sampling and teach techniques and approaches to assess whether samples are representative of a whole bulk and maximise this representivity. Further to this the module aims to teach a range of tools for data analysis and instil confidence in applying these to experimental data. These are vital skills for use in future employment.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Understand basic statistical concepts such as statistical distributions, bias, uncertainty and error.

2. Understand the link between sample variance and sample size and how appropriate sample masses can be estimated to minimise error.

3. Describe suitable procedures and techniques for sampling material in the context of the minerals processing industry.

4. Apply data analysis tools to the interpretation of experimental data.

Discipline Specific Skills and Knowledge

5. Apply appropriate mathematical methods, scientific principles and computer based methods to the modelling, analysis and solution of practical engineering problems.

6. Work safely in laboratory, workshop environments etc., and promote safe practice.

Personal and Key Transferable / Employment Skills and Knowledge

7. Sort, manipulate and present data in a way that facilitates effective analysis and decision making.

8. Communicate effectively and persuasively using the full range of currently available methods.

SYLLABUS PLAN - summary of the structure and academic content of the module

Sampling theory topics

Statistical distributions: types, moments, and statistics

Concepts: uncertainty and bias

Derivation of sampling variance equation

Variance in the sampling value chain

Inference based on sample analysis

Sample size determination

Optimisation of sample size

Types of sampling techniques

Data analysis topics

Reconciliation: obtaining a closed mass balance

Regression modelling: least squares estimation

Analysis of variance (ANOVA)

Clustering: principle component analysis, dendrogram

Bayesian prediction

Fuzzy logic

Neural networks

Health and Safety engagement

Health and safety implications related to sampling of different types of minerals in laboratory and industrial settings are discussed

LEARNING AND TEACHING

LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)

Scheduled Learning & Teaching Activities	30.00	Guided Independent Study	114.00	Placement / Study Abroad	0.00

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	114	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
Prior knowledge and skills e-assessment	100 word equivalent		Electronic supported by verbal if required

SUMMATIVE ASSESSMENT (% of credit)

Coursework	100	Written Exams	0	Practical Exams	0

DETAILS OF SUMMATIVE ASSESSMENT

Form of Assessment	% of Credit	Size of Assessment (e.g. duration/length)	ILOs Assessed	Feedback Method
Sampling exercises and calculations	50	2,000 word equivalent	1-2, 5-7	Electronic or written feedback
Data analysis exercises and calculations	50	2,000 word equivalent	3-7	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
Coursework	New assignment	1-7	Ref/def period

RE-ASSESSMENT NOTES

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

Basic reading:

ELE: http://vle.exeter.ac.uk/

Web based and Electronic Resources:

http://app.knovel.com

Other Resources:

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE	15	ECTS VALUE	7.5

PRE-REQUISITE MODULES	None
CO-REQUISITE MODULES	None

NQF LEVEL (FHEQ)	7	AVAILABLE AS DISTANCE LEARNING	No
ORIGIN DATE	Monday 06 February 2023	LAST REVISION DATE	Monday 06 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...

CSMM433 - Sampling Theory and Data Analysis (2023)