FACULTY OF LAW & BUSINESS

1
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.
2
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)
4
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:

5
Rubric – Assessment Task 3 – Business Report Project (50%)

Elements CriteriaStandards
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)

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