DATA4400 Data-driven Decision Making and Forecasting Assignment Help

 

Subject Code: 

DATA4400

Subject Name: 

Data-driven Decision Making and Forecasting 

Assessment Title: 

A Forecasting Project

Assessment Type: 

In Class Presentation and Written Report 

Word Count: 500 Words (+/-10%) 

Weighting:

30 % (20% individual 10% group presentation) 

Total Marks: 

30 

Submission: 

In Class (group), Turnitin (individual) 

Due Date: 

Group: Group work to be done in class during Week 5.  Individual: Individual report via Turnitin, no more than 48 hours after  class in Week 5.


Your Task  

Apply forecasting techniques to a given dataset and provide a business application of the  forecasts. The assessment is worth 30 marks (see rubric for allocation of these marks).  

Assessment Description  

  

The data provided for the assessment are time series of monthly sales revenue for two stores:  TheDon which sells cricket equipment; and Work Out which sells equipment for a wide range of  sports.’ 

The objective of the assessment is to use Exploratory, Tableau and Excel software to: describe the  time series; to develop Prophet and Holt-Winters demand forecast models for the two stores; and  to compare forecasts from these models. 

Assessment Instructions  

  

In class: You will be presented with a dataset in class. As a group, analyse the dataset using  Tableau and Exploratory.io.  

You will provide an oral presentation of the group work in parts A to D during the third hour of the  workshop.  

The data set will be posted or emailed to you at the beginning of class in Week 5.  After Class: Individually write a 500-word report, which briefly summarises the analysis and  provides suggestions for further analysis. This component of the assessment is to be submitted  via Turnitin, within 48 hours of midnight on the day of your class in Week 5.  

  

No marks will be awarded for the assessment unless this report is submitted. Hint: take notes during the group assessment to use as prompts for your report. 

 

As a group:  

Part A  

– Use Tableau to compare time series plots for the two stores by: plotting one under the other;  and by plotting both on the same graph. Explain how you implement these plots in Tableau. – Comment on features of the two time series. Use descriptive analytics to compare the two  stores in terms of sales revenue, and explain how these statistics can be obtained using  Tableau. 

Part B  

– Use Tableau to investigate trend and seasonal variability in the time series for the two stores.  Explain how to implement relevant graphics in Tableau. Comment on the trends and seasonal  effects.  

Part C  

– Use Tableau to obtain Holt-Winters forecasts for TheDon for the next 12 months, and to  provide a suitable graphical display. Explain the various summaries provided by Tableau, in  the context of TheDon. Explain how to find the point forecast three months ahead and its 95%  Prediction Interval in Tableau.  

Part D  

– Use Exploratory to obtain Prophet forecasts for TheDon for the next 12 months, and to  provide a suitable graphical display. Explain the various summaries provided by Exploratory,  in the context of TheDon. Explain how to find the point forecast three months ahead and its  95% Prediction Interval in Exploratory. 

Part E 

Prepare a PowerPoint presentation with your answers to A, B, C, and D:  

• Include screenshots showing how you use the software, and of the information provided  by the software. 

• Discuss the suitability of HW and Prophet forecasting for TheDon. 

Note: All members of the group should be involved in the presentation. The allocated time for the  presentation will be decided by your lecturer. 

As individuals:  

Take the most recent year as a test series. Use the time series, less the final year, as a training  series. 

• a) Plot training series plus HW forecasts for test year for TheDon in Tableau. • b) Plot training series plus Prophet forecasts for test year for TheDon in Exploratory. • c) Selecting from statistics given in Tableau and Exploratory, construct a table of RMSE  within training series by method by store. 

• d) Make calculations in Excel, or otherwise, construct a table of RMSE in the test series by  method by store. 

• e) Explain why the RMSE is expected to be larger in the test series. Will the RMSE  necessarily be larger in the test series? 

• f) Make calculations in Excel, or otherwise, find the MASE using Prophet in the test series  for WorkOut.  

Note: You must submit your individual report before midnight on the second day after your class in  Week 5

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?  

  

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.  

Late assignment submission penalties  

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

Page 4 Kaplan Business School Assessment Outline  

*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 end of the KBS Assessment Policy:  https://www.kbs.edu.au/wp 

content/uploads/2016/07/KBS_FORM_AssessmentPolicy_MAR2018_FA.pdf 

 

Generative AI Traffic Lights  

Please see the level of Generative AI that this assessment has been designed to accept: 

Traffic 

Light

Amount of Generative Artificial Intelligence  (AI) usage

Evidence  

Required

This  

assessment (

 

This  

assessment  is level 3

Level  

1

This assessment fully integrates Generative AI, encouraging you to harness the technology’s  full potential in collaboration with your own  expertise.  

It will highlight your ability to demonstrate how  effectively you can work alongside AI to achieve  sophisticated outcomes, blending human intellect  and artificial intelligence.

Your  

collaboration with  AI must be  

clearly  

referenced and  documented in  the appendix of  

your submission,  including all  

prompts and  

responses used  for the  

assessment.

Level  

2

This assessment invites you to engage with  Generative AI as a means of expanding your  creativity and idea generation.  

It will highlight your ability to complement your  original thinking with the capabilities of AI. For  example, through brainstorming and preliminary  concept development.

Your  

collaboration with  AI must be  

clearly  

referenced and  documented in  the appendix of  

your submission,  including all  

prompts and  

responses used  for the  

assessment.

Level  

3

This assessment showcases your individual  knowledge and skills in the absence of  Generative AI support.  

It will highlight your personal abilities. For  example, to analyse, synthesise, and create based on your own understanding and learning.

Use of generative AI is prohibited  and may  

potentially result  in penalties for  academic  

misconduct,  

including but not  limited to a mark  of zero for the  

assessment.

 

Assessment Marking Guide 

  

Standards for this Task 

Points 

Feedback 

Group Activity 

Data loaded into Tableau and Exploratory.io.  

Appropriate plots developed to characterise data.  Forecasting models are developed and used.  

Well-structured presentation with involvement of all group  members. 

/10 

 

Individual Report  

a) Training series plus HW forecasts for the test year for  TheDon plotted in Tableau. 

b) Training series plus Prophet forecasts for test year for  TheDon plotted in Exploratory. 

c) A table constructed of RMSE within training series by method  by store. 

d) A table constructed of RMSE in the test series by method by  store using Excel or other methods. 

e) Explained well why the RMSE is expected to be larger in the  test series. 

f) Found the MASE using Prophet in the test series for WorkOut  using Excel or other methods. 

For a higher grade: 

A clear interpretation of the metrics with valid justification  should be provided.

/20

 
 

/30