BUS105 Introduction To Business Analytics :
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Assessment 3 Information
| Subject Code: | BUS105 | ||
| Subject Name: | Introduction to Business Analytics | ||
| Assessment Title: | AI and unstructured data | ||
| Assessment Type: | Written report | ||
| Word Count: | 1000 | Words | (+/-10%) |
| Weighting: | 30 % | ||
| Total Marks: | 30 | ||
| Submission: | Turnitin | ||
| 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.
Background:
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.
Sources:
https://medium.com/@Jam3/how-to-make-ai-generated-sneakers-3a39464ec58d https://towardsdatascience.com/generating-shoe-designs-with-deep-learning-5dde432a23b8
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:
https://theconversation.com/what-is-an-algorithm-how-computers-know-what-to-do-with-data- 146665
https://www.accenture.com/au-en/insights/artificial-intelligence-summary- 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: http://www.kbs.edu.au/current-students/student-policies/.
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 relevant | Introduction is relevant and in the student’s own words | 7 |
| The student has introduced another | The student has introduced another specific | 3 |
| vague application to which the | application to which the team will adapt the | |
| team will adapt the algorithm | algorithm | |
| The student has not explained the | The student has explained in their own words | 7 |
| types of data needed and | exactly what data they require for the | |
| differences between them, OR not | algorithm, and state what type of data it is. | |
| explained in their own words | They 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 customers | from potential customers and the next steps | 3 |
| and the next steps is missing or not | are feasible, relevant and practical | |
| relevant | ||
| Suggestions for storage of data are | Suggestions for storage of all data is relevant | 3 |
| too general. | ||
| The student has not explained the | The student has explained the main limitations | 3 |
| main limitations of Artificial | of 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 Harvard | style are included | |
| referencing style | ||
| The report is engaging and shows originality of | 2 | |
| Work has no originality | the 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 days | Penalty |
| 1* – 9 days | 5% per day for each calendar day late deducted from the student’s total Marks. |
| 10 – 14 days | 50% deducted from the student’s total marks. |
| After 14 days | Assignments 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). |
| Note | Notwithstanding 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:
https://www.kbs.edu.au/wp-content/uploads/2016/07/KBS_FORM_Assessment- Policy_MAR2018_FA.pdf
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