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Evidence Guide: ICTDAT503 - Use unsupervised learning for clustering

Student: __________________________________________________

Signature: _________________________________________________

Tips for gathering evidence to demonstrate your skills

The important thing to remember when gathering evidence is that the more evidence the better - that is, the more evidence you gather to demonstrate your skills, the more confident an assessor can be that you have learned the skills not just at one point in time, but are continuing to apply and develop those skills (as opposed to just learning for the test!). Furthermore, one piece of evidence that you collect will not usualy demonstrate all the required criteria for a unit of competency, whereas multiple overlapping pieces of evidence will usually do the trick!

From the Wiki University

 

ICTDAT503 - Use unsupervised learning for clustering

What evidence can you provide to prove your understanding of each of the following citeria?

Determine data clustering requirements

  1. Research organisation’s need for data clustering and define problem, objective and outputs
  2. Determine required machine and input data set according to task requirements
  3. Define evaluation protocol and accepted measure of success
  4. Develop and document required benchmark model
  5. Collect data according to task requirements
  6. Evaluate data quantity, completeness and alignment according to task requirements
  7. Transform and format data according to specifications
  8. Finalise data preparation according to task requirements
  9. Input raw data according to task requirements
  10. Run required algorithm and adhere to required processing time frame
  11. Obtain output reports and determine completeness of task according requirements
Research organisation’s need for data clustering and define problem, objective and outputs

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Determine required machine and input data set according to task requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Define evaluation protocol and accepted measure of success

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Develop and document required benchmark model

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Collect data according to task requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Evaluate data quantity, completeness and alignment according to task requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Transform and format data according to specifications

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Finalise data preparation according to task requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Input raw data according to task requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Run required algorithm and adhere to required processing time frame

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Obtain output reports and determine completeness of task according requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Finalise data clustering tasks

  1. Analyse data report and determine clustering tasks have been completed according to task requirements
  2. Interpret, summarise and document findings
  3. Communicate findings to required personnel and seek and respond to feedback
  4. Lodge documentation according to task requirements and finalise task activities according to organisational requirements
Analyse data report and determine clustering tasks have been completed according to task requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Interpret, summarise and document findings

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Communicate findings to required personnel and seek and respond to feedback

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Lodge documentation according to task requirements and finalise task activities according to organisational requirements

Completed
Date:

Teacher:		 Evidence:

 

 

 

 

 

 

 

Assessed

Teacher: ___________________________________ Date: _________

Signature: ________________________________________________

Comments:

 

 

 

 

 

 

 

 

Instructions to Assessors

Required Skills and Knowledge

The candidate must demonstrate the ability to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including evidence of the ability to:

collect, prepare and cluster data using unsupervised machine learning methodologies and report on the findings on at least two occasions.

In the course of the above, the candidate must:

research industry standard approaches and methodologies for machine learning

evaluate and prepare data.

The candidate must be able to demonstrate knowledge to complete the tasks outlined in the elements, performance criteria and foundation skills of this unit, including knowledge of:

methodologies for data clustering unlabelled data including intra-cluster cohesion and intra-cluster separation

industry standard data clustering methodologies including benchmark modelling techniques for data clustering

report writing methodologies relevant to reporting findings of data clustering activities

industry standard machine learning methodologies relevant to unsupervised learning

methodologies for modelling data relevant to unsupervised learning.