Finance and Management

BUS105 Introduction To Business Analytics

10 April 2023 09:06 AM | UPDATED 8 months ago

BUS105 Introduction To Business Analytics :

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BUS105  Introduction To Business Analytics
BUS105 Introduction To Business Analytics

Assessment 3 Information

Subject Code:BUS105
Subject Name:Introduction to Business Analytics
Assessment Title:AI and unstructured data
Assessment Type:Written report
Word Count:1000Words(+/-10%)
Weighting:30 %
Total Marks:30
Due Date:Friday Week 10 at 23:55pm (11:55pm AEST)

Your Task

  • Write an individual report on Artificial intelligence and unstructured data

Assessment Description

  • You will
  • This covers LO3.
A collage of different shoes  Description automatically generated with medium confidenceA shoe on a surface  Description automatically generated with low confidence


Jam3 is a design and experience company. One of the aims of Jam3 is to generate new ideas. Those ideas are researched and developed at Jam3 Labs, thus, today’s ideas become tomorrow’s brands!

One of the latest ideas is to use an algorithm to generate modifications of adidas sneakers. The type of algorithm is a “generative adversarial network (GAN)”. GANS actually make use of two different networks, one that generates new images of what is supposed to be sneakers (sport shoes), and the other tries to work out which are fake and which are real sneakers by being trained to look at real adidas sneakers. The detail of the algorithm is not important here. What is important is that the algorithm can create new trendy designs for sneakers!

The visuals of the new designs of the sneakers can then be used in real campaigns and analytics can be done on the feedback (surveys, tweets, Instagram comments…..) to see which modified

designs are the most popular. From those, the popular designs, real new types of sneakers can be manufactured and sold.


Suppose that you are in charge of a team of analysts and data scientists and you want them to adapt the algorithm (generating new sports shoes) to another specific area of fashion (e.g. dance shoes, high heal shoes, coloured shirts, summer dresses, leather coats).

Assessment Instructions

  • In your own words, introduce the idea of algorithms and artificial intelligence (AI) from your workshop notes and the references below. Also explain what AI can do in a general sense. [250 words, 7 marks]
  • Introduce the application area of fashion to which the team will adapt the algorithm. [100 words, 3 marks]
  • Explain exactly what data you will need to gather for the algorithm, e.g. images of men’s white shorts, tap dancing shoes, race day hats, lengths, widths of items, etc. Describe what type of data this is and how that differs from other types of data, i.e. is it structured, semi- structured, unstructured? How do these types differ? [250, 7 marks]
  • Describe how you will get feedback from potential customers about your new designs, e.g. tweets, and what the next steps will be once you have feedback. [200 words, 3 marks]
  • Explain how you might store all of your data. [100 words, 3 marks]
  • Explain the limitations of Artificial intelligence. [100 words, 3 marks]
  • Include at least five references which can include one or two from your workshop notes. Use Harvard referencing. In the Academic Success Centre on MyKBS see STUDY RESOURCES

– ACADEMIC SKILLS. [2 marks]

  • Originality of the work. [2 marks]

Starting references: 146665 index?c=acn_glb_brandexpressiongoogle_12786625&n=psgs_0122&gclid=CjwKCAjwjZmTB hB4EiwAynRmD2r9XSunm8JG8NtvHnCVSi0zx3wHdm0Rpcyesd- pmYvG9vhqyWxdNBoCynsQAvD_BwE

Important Study Information

Academic Integrity Policy

KBS values academic integrity. All students must understand the meaning and consequences of cheating, plagiarism and other academic offences under the Academic Integrity and Conduct Policy.

What is academic integrity and misconduct? What are the penalties for academic misconduct? What are the late penalties?

How can I appeal my grade?

Click here for answers to these questions:

Word Limits for Written Assessments

Submissions that exceed the word limit by more than 10% will cease to be marked from the point at which that limit is exceeded.

Study Assistance

Students may seek study assistance from their local Academic Learning Advisor or refer to the resources on the MyKBS Academic Success Centre page. Click here for this information.

Assessment Marking Guide

PART A: BUS105 Assessment 3   Rubric /30
Individual Assessment
Has demonstrated limited achievement:Has demonstrated all or most of:Max Marks
  Introduction is vague, too general or not relevantIntroduction is relevant and in the student’s own words  7
  The student has introduced anotherThe student has introduced another specific  3
vague application to which theapplication to which the team will adapt the 
team will adapt the algorithmalgorithm 
  The student has not explained theThe student has explained in their own words  7
types of data needed andexactly what data they require for the 
differences between them, OR notalgorithm, and state what type of data it is. 
explained in their own wordsThey can distinguish between data types, 
 structured, semi-structured, unstructured, etc. 
  The description of how they will get  The description of how they will get feedback 
feedback from potential customersfrom potential customers and the next steps3
and the next steps is missing or notare feasible, relevant and practical 
  Suggestions for storage of data areSuggestions for storage of all data is relevant  3
too general.  
  The student has not explained theThe student has explained the main limitations  3
main limitations of Artificialof Artificial intelligence in their own words 
intelligence in their own words or it  
is missing from the report  
The References are not related to the topics in each paragraph,  At least five references in Harvard referencing  2
missing or not in Harvardstyle are included 
referencing style  
 The report is engaging and shows originality of2
Work has no originalitythe work (for the best students) 

Assignment Submission

Students must submit their individual analysis via Turnitin on Friday of Week 10 at 23:55pm AEST.

Students are also encouraged to submit their work well in advance of the time deadline to avoid any possible delay with Turnitin similarity report generation or any other technical difficulties.

Late assignment submission penalties

Penalties will be imposed on late assignment submissions in accordance with Kaplan Business School’s Assessment Policy.

Number of daysPenalty
1* – 9 days5% per day for each calendar day late deducted from the student’s total Marks.
10 – 14 days50% deducted from the student’s total marks.
After 14 daysAssignments that are submitted more than 14 calendar days after the due date will not be accepted and the student will receive a mark of zero for the assignment(s).
NoteNotwithstanding the above penalty rules, assignments will also be given a mark of zero if they are submitted after assignments have been returned to students.

*Assignments submitted at any stage within the first 24 hours after deadline will be considered to be one day late and therefore subject to the associated penalty.

If you are unable to complete this assessment by the due date/time, please refer to the Special Consideration Application Form, which is available at the BUS105 Introduction To Business Analytics end of the KBS Assessment Policy: Policy_MAR2018_FA.pdf


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