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FACULTY OF LAW & BUSINESS
Peter Faber Business School
DATA201: Data Analytics and Decision Making, Semester 2, 2019
Assessment 3 [Weight 50%]
Due Date: 13/11/2019 5:00PM
Individual Work
Why this assignment?
• Opportunity to apply theory into practice
• Exposure to real-life scenario
• Develop meta-cognitive skills by reflecting on feedback
What are the types of skills that I will acquire upon completion of this assignment?
• Written communication skills
• Think critically and reflectively.
• Practical skills using Tableau and Excel
• Practical skills using Knime Analytics Platform
• Application knowledge in solving problems.
• Self-management skills.
Assessment Overview:
This assessment is designed to develop students’ skills in the correct usage of analytical techniques and
interpreting data for making managerial decisions. The main task is to analyse business data and to prepare a
report for management based on an analysis of the data. The focus is on understanding the use of data
analytical tools in a business context and develop written communication skills.
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Assignment-3 tasks and description
| Tasks | Steps/Description | Which tools to use to complete? | How to Submit your Work |
| Construct classification tool | Construct classification tool to effectively assist the buyer in identification of cars likely to be Kicks. • Experiment different configurations of the decision tree tool in Knime to find the best one you can. (NB. The error rate should be less than 15%). • It is expected that while exploring this tool, you may need to keep coming back to explore the dataset to find the best set of inputs for your classification problem#. | Use Knime software | Data file (Excel), Knime file (of your decision tree) on ePortfolio** |
| Create Dashboard | Create dashboard • When you are happy with your classification tool, create a dashboard in Tableau or Excel to present these inputs and how they affect IsBadBuy (Kicks). • Be mindful to choose appropriate visuals for your dashboard. | Use Tableau or Excel | Dashboard (Tableau or Excel) on ePortfolio** |
| Write a 1000-word report | What to include in report? Once successfully creating a classification tool, describe the tool’s functionality with respect to input contributions to the Kick classification. • Evaluate your classification tool and explain how it may assist the buyer to reduce the Kicks rate. • Using the data analytic methods you have learnt in the whole semester, explain your analysis, interpretation in the experiment to support decision makers. | Use Word | Combine item 3 and 4 and submit it on Turnitin |
| Self-reflection on feedback from assignment-2 | Reflection Proforma: Use the reflection proforma included in this document to complete your self-reflection and attach it at the end of your report (item-3 above) | Use Word | |
| Note: # use Excel clean dataset and Data Dictionary from Assignment-2 **Refer to LEO on instructions on how to submit files on ePortfolio (Same process as assignment -2) Refer to case study (page-6) – the same case study as before. Refer to rubric for weight allocation and marking schema (Page-5 in this document) |
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Case Study: Don’t Get Kicked (Same as assignment-2)
One of the biggest challenges of an auto dealership purchasing a used car at an auto auction is the risk that the
vehicle might have serious issues that prevent it from being sold to customers. The auto community calls these
unfortunate purchases “kicks”.
Kicked cars often result when there are tampered odometers, mechanical issues the dealer is not able to
address, issues with getting the vehicle title from the seller, or some other unforeseen problem. Kicked cars can
be very costly to dealers after transportation cost, throw-away repair work, and market losses in reselling the
vehicle.
Data analysts who can figure out which cars have a higher risk of being kick can provide real value to dealerships
trying to provide the best inventory selection possible to their customers.
The challenge of this case study is to predict if a car purchased at an Auction is a Kick (bad buy).
The data dictionary, Carvana_Data_Dictionary.txt, and the data files can be downloaded from LEO under
Assessment tab. The data dictionary describes the 34 attributes: RefId, in the first column, contains the ID
number for each record. IsBadBuy, in the second column, is the binary dependent variable, where a 1 (one)
means “is Kick” and 0 (zero) “is not Kick”. The remaining columns (3 through 30) are independent variables. The
dataset contains records for 72,561 vehicles, of which 12.3% are Kick. (Adapted from Kaggle competition)
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Student Reflection on Feedback
How did you act on the feedback?
Reflecting on your assessment feedback is an important component of learning, and an essential part of
developing your on-going skills and ultimately, adding to your skillset.
Please consider the feedback that you received from this assignment (assessment 2) and provide a short
reflection on how you acted on, or intend to act upon, that feedback, and how it has informed this assignment
task.
Please submit this self-reflection proforma along with written report for this assignment.
| Questions | Feedback Received (Instructor Comments) | Student’s Action Plan |
| On reflection, what area(s) of your work needed improvement in this assignment, based on the feedback? | ||
| What are the key feedback elements that you can use to improve your work for this assignment? | ||
| Provide at least 1 example of practices you intend to improve on as a result of acting on the feedback from your teacher. | ||
| Is there a concept or a topic that you find difficult to understand? State here: |
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Rubric – Assessment Task 3 – Business Report Project (50%)
| Elements | Criteria | Standards | ||||
| Below expectations | Meets expectations | Exceeds expectations | ||||
| NN (0-49) | PA (50-64) | CR (65-74) | DI (75-84) | HD (85-100) | ||
| GA4 ULO3 Weight: 35 marks TL=5 | Dashboard / business report (35 points) | Dashboard does not work properly; not easily digestible, cluttered, incomplete, incorrect, too complicated | Satisfactory dashboard, but can still be improved in three or four aspects of clarity, brevity, completeness and correctness | Fair dashboard, can be improved in two or three aspects of clarity, brevity, completeness and correctness | Good dashboard, but can still be improved in one or two aspect of clarity, brevity, completeness and correctness | Highly effective dashboard, very high degree of clarity, brevity, completeness, correctness |
| GA5 ULO3 Weight: 35 marks TL=5 | Knime workflow (25 points) | Workflow does not work or is not correct | Workflow works but half of the nodes need to be improved | Workflow works fairly well, some nodes can still be improved | Workflow works well but few nodes can be improved | Workflow works excellent. |
| GA5 ULO5 Weight: 25 marks TL=2 | Data analysis and interpretation (25 points) | Fail to carry out most data analysis tasks required; failed to interpret them | Can carry out more than half of data analysis tasks but can interpret only about half of them | Can carry out most of data analysis tasks and can interpret most of them; fair interpretation | Can carry out all of data analysis tasks and can interpret most of them; good interpretation | Can carry out all of data analysis tasks and can interpret all of them; excellent interpretation |
| GA9 ULO5 Weight: 15 marks TL=2 | Written communication skills to support decision makers (15 marks) | Inferior demonstration of effective written communication skills in preparing reports using data analytics and interpretation skills for management to support decision makers. | Moderate demonstration of effective written communication skills in preparing reports using data analytics and interpretation skills for management to support decision makers. | Proficient demonstration of effective written communication skills in preparing reports using data analytics and interpretation skills for management to support decision makers. | Accomplished demonstration of effective written communication skills in preparing reports using data analytics and interpretation skills for management to support decision makers. | Outstanding demonstration of effective written communication skills in preparing reports using data analytics and interpretation skills for management to support decision makers. |
| Note: This rubric is an indicative of the rubric to be used in the assessments 1. GA – Graduate Attribute 2. ULO – Unit Learning outcome 3. TL – Taxonomy Level (or level of complexity) (see Bloom’s Taxonomy) |