COMM1190 Assessment 1: Individual Report
Week 4: 3:00 pm Friday, June 24 (AEDT)
20%
A written report
Maximum word count of 750, excluding references, figures, tables, and Appendix
Via Turnitin on Moodle course site

Objective
The objective of this individual assessment is to test your ability in conceptualizing and
solving analytics problems, your skills in R programming, and your ability in providing
business recommendations based on analytics results. In this assignment, you will
undertake an exploratory data analysis in the role of a data analyst. You are expected
to analyze data using statistics and visualisation techniques. These learning content
has been covered in the course up until the end of week 3.
Description
A manufacturing company MechTech is launching an initiative of improving its
workforce performance. The leadership team aims to explore the factors that are
associated with the job performance of their employees. MechTech has contracted
you as a data analyst to investigate these factors. The company provides you with the
data including attributes such as employees’ demographic information (e.g., gender,
age, education) and job information (e.g., department, years at the company,
mentoring, training). A detailed description of each attribute of the dataset is presented
in Appendix A: Data Dictionary.
MechTech requires you to:
1. conduct descriptive analytics to identify the factors that are associated with the
job performance of their employees. Note that Descriptive Analytics refers to
statistics and visualization techniques. As an example, a box plot and a bar
chart are considered as two different techniques.
2. provide recommendations to its leadership team about how to improve job
performance based on the descriptive analytics results.
How to Download Dataset
Use the following R-script to directly download the data in R-studio by typing the last
7 digits of your zID (e.g., zID is 3241403) as highlighted in yellow below:
Note that students will have a personalized data set, thereby different results and
recommendations even using the same analytics technique.
Guidance on Data Analysis
1. There is not a single correct answer to the assignment. The dataset includes
many attributes for you to explore, and some attributes are likely to be more
useful than others. Therefore, it is important that you systematically explore
different variables in the dataset to facilitate your analysis.
2. Consider potential, key factors that are associated with low performance across
the workforce by relating them to real-world scenarios. You can look for industry
examples and/or academic references to justify your selection of variables.
3. Although you may create many graphs for your assignment, you only want to
include figures that support your main findings. Those graphs should
summarize the associations that you are reporting.
4. Your recommendations to the leadership team at MechTech should be well
supported by your visualizations and/or statistics summaries.
5. You should explicitly state any key assumptions that impact your data analysis.
Requirements:
1. Problem Identification (10%)
• Formulate a business problem.
• State the purpose of the analytics tasks.
2. Data Analysis (50%)
• Justify the selection of techniques and variables. More than 3 variables
are recommended for your analysis.
• Apply appropriate descriptive analytics techniques to analyze data.
• Use visualization graphs to explore associations between variables.
Visualization graphs include histograms, bar charts, scatter plots, box
plots, etc.
• Interpret analytics results.

library(RCurl)
x <- read.csv("https://raw.githubusercontent.com/dat-analytics/data_assess_1_t2_2022/main/z3241403.csv")

3. Recommendations (20%)
• Provide recommendations based on analytics results.
• Support recommendations use state-of-the-art industry practices and/or
academic references.
• Use supplementary readings to this assessment and self-researched
materials to develop recommendations.
4. Communication (10%)
• Demonstrate proficiency in reading and writing in English.
• Uses language, figures, and/or tables to convey qualitative and
quantitative information effectively and accurately.
• Attach the codes of your R programming (not a screenshot) in the
Appendix of your report.
5. Organization and structure of the report (10%)
• Develop a logical structure to organize the sections of your report.
• Use academic referencing in Harvard style. Refer to UNSW guideline:
https://www.student.unsw.edu.au/harvard-referencing
• An example for structuring and developing your report is provided in
Appendix B
Submission Instructions
• A written report with all relevant codes in an appendix.
• A cover sheet with signature.
• Word limit is 750 words with 10% leeway.
Please note:
1) the codes do not count towards the word limit;
2) 10% penalty applies to missing a signed cover sheet in the submission;
3) 5% penalty applied to exceeding the word limit.
Late Submission Penalties
1. Late submission will incur a penalty of 5% per day or part thereof (including
weekends) from the due date and time. An assessment will not be accepted
after 5 days (120 hours) of the original deadline unless special consideration
has been approved. An assignment is considered late if the requested format,
such as hard copy or electronic copy, has not been submitted on time or where
the ‘wrong’ assignment has been submitted.
2. No extensions will be granted except in the case of serious illness,
misadventure, or bereavement, which must be supported with documentary
evidence. Requests for extensions must be made to the Lecturer-in-charge by
email and be accompanied by the appropriate documentation no later than 24
hours before the due date of the assignment. In circumstances where this is not
possible, students must apply for Special Consideration.
3. The Lecturer in Charge is the only person who can approve a request for an
extension. If you do make a request for an extension, the Lecturer in Charge
will email you and the course convener with the decision. Note: A request for
an extension does not guarantee that you will be granted one.
Smarthinking English Support
“… an online writing support platform officially sanctioned by UNSW. Students can
submit drafts of their writing to a Smarthinking tutor or connect to a Smarthinking
tutor in a real-time session and receive comprehensive feedback on a variety of
writing areas”. https://www.student.unsw.edu.au/smarthinking

Smarthinking is available on the COMM1190 Moodle Site.
Using the service, you can:
• Submit your drafts to a Smarthinking tutor for comprehensive feedback on
your writing typically within 24 hours; or
• Connect to a Smarthinking tutor in a live one-on-one session about writing.
• Receive comments on a variety of writing areas including clarity of your
ideas, grammar, organisation etc.
• Use up to 2 hours on Smarthinking reviews.


Marking Rubric for Individual Assessment
Weight
%
Criteria Fail
(0% - 49%)
Pass
(50% - 74%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(75% - 100%)
A
N
A
LY
SI
S
(8
0%
)

10 Problem
Identification
 Does not identify a
business problem
or a task objective.
 Identify a business
problem but misses
many details.
 Identify a business
problem but misses
a few details.
 Identify a business
problem and task
objectives.
 Identify a business
problem and task
objectives with
clarity and depth,
supported by
industry examples.
50 Data
Analysis
 No relevant
analytical
technique was
identified.
 No specific
variable was
identified.
 No logic between
business issues,
analytical
techniques, and
variable selection.
 No statistics
summary or
visualization is
presented.
 The results are
mostly incorrectly
interpreted.
 No R codes are
included.
 Identify 1 analytical
technique to be
used for solving the
problem.
 Identify variables for
each technique to
be deployed.
 Attempt to present a
logic between
business issues,
analytical
techniques, and
variable selection,
but the logic is not
coherent or clear.
 Attempt to analyze
data but conduct
inadequate data
analysis in some
aspects.
 The results are
somewhat correctly
examined and
interpreted.
 Identify and explain
2 analytical
techniques to be
used for solving the
problem.
 Identify and explain
variables for each
technique to be
deployed.
 Attempt to present a
logic between
business issues,
analytical
techniques, and
variable selection.
 Analyze data but
explanations of
analysis results are
insufficient.
 The results are
mostly correctly
examined and
interpreted.
 Identify, explain,
and justify 3
analytical
techniques to be
used for solving
the problem.
 Identify, explain,
and justify
variables for each
technique to be
deployed.
 Explicitly present a
logic between
business issues,
analytical
techniques, and
variable selection.
 Analyze data
adequately with
sufficient
explanations of the
issues identified,
but the solutions to
solving the issues
 Identify, explain,
and justify more
than 3 analytical
techniques to be
used for solving the
problem with clarity.
 Identify, explain,
and justify variables
for each technique
to be deployed. The
justifications are
sound and
convincing.
 Explicitly present a
coherent and clear
logic between
business issues,
analytical
techniques, and
variable selection.
The logic is
coherent and clear.
 Analyze data
adequately with
sufficient
Weight
%
Criteria Fail
(0% - 49%)
Pass
(50% - 74%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(75% - 100%)
 R codes are
included but
extensive errors are
identified.
 R codes are
included but some
errors are identified.
identified are
insufficient.
 The results of
each model are
mostly correctly
interpreted and
examined
supported by
academic
references.
Results
interpretation is
relevant and
meaningful in the
case context.
 R codes attached
are mostly correct.

explanations of the
issues identified,
and with adequate
solutions to the
issues identified
using statistics and
visualization.
 The results of each
model performance
and findings are
correctly interpreted
and critically
examined supported
by academic
references. Results
interpretation is
relevant and
meaningful in the
case context.
 R codes attached
are thoroughly
correct.
20 Recommenda
tions
 Inadequate or no
recommendations
of the
analysis/evidence
are provided.

 Recommendations
are somewhat
inconsistently tied to
some of the issues
discussed and
inconsistently linked
back to variables
analyzed.
 Recommendations
are consistently tied
to each issue
discussed and
linked back to
variables analyzed.

 Recommendations
are logically and
consistently tied to
each issue
discussed and
linked back to
variables
analyzed.

 Recommendations
are logically and
consistently tied to
each issue
discussed, linked
back to variables
analyzed, and
developed with
critical thinking.
Weight
%
Criteria Fail
(0% - 49%)
Pass
(50% - 74%)
Credit
(65%-74%)
Distinction
(75%-84%)
High Distinction
(75% - 100%)
C
O
M
M
U
N
IC
A
TI
O
N
(2
0%
)
10 Communicati
on
 Your writing is not
professional in
tone and there are
major spelling and
grammatical errors
throughout.
 Your written
expression does
not indicate a
logic/flow between
each section of the
essay.
 Some attempt has
been made to use a
professional tone
and presentation in
your writing, but
there are some
spelling and
grammatical errors.
 You have
endeavoured to
provide logic/flow
between each
section of the essay.
 Your writing is
mostly professional
in tone and
presentation, but
there are occasional
spelling and/or
grammatical errors.
 Your written
expression provides
an adequate
indication of the
logic/flow between
each section of the
essay.
 Your writing is
professional in
tone and
presentation with a
few very minor
spellings and/or
grammatical
errors.
 Your written
expression
provides a strong
indication of the
logic/flow between
each section of the
essay.
 Your writing is
professional in tone
and presented in an
outstanding manner
with no spelling or
grammatical errors.
 Your written
expression provides
a strong and
coherent indication
of the logic/flow
between each
section of the essay
that has enabled
key arguments to
fully develop.
10 Organisation
and structure
of the report

 Poor or unclear
structure.
 Your sources have
not been
referenced and/or
there are
excessive errors in
referencing in the
essay.
 The word limit has
not been adhered
to.
 Attempt to a good
structure but lack
coherent flow
between sections.
 Some sources are
referenced
throughout the
essay, but there are
errors in your
referencing of
sources.
 Good structure with
organized headings.
 Most sources are
referenced
throughout the
essay, with only
minor errors in
referencing.
 Good structure
with organized
headings and
coherent follow
between sections.
 All sources are
referenced
throughout the
essay with only
minor errors in
referencing.
 Good structure with
organized headings
and coherent follow
between sections.
 All sources are
referenced
throughout the
essay and the
sources are used
very well, with no
significant errors in
referencing.


Appendix A. Data Dictionary
Variables Description
Age Age in years (18-60)
BusinessTravel Business travel frequency (Non-Travel, Travel_Rarely, Travel_Frequently)
Department Human Resources, IT Support, Production, Research and Development,
Finance, Sales
DistanceFromHome Number of kms from home to workplace (ranging from 1 to 25 kms)
Education Level of education:
1=High School, 2=Diploma, 3=Bachelor, 4=Masters, 5=PhD
EnvironmentSatisfaction Satisfaction with work environment:
1= Low, 2= Medium, 3=High, 4=Very High
FeedbackFromManager L =Low feedback; H = high feedback
Gender Male/Female
JobLevel Seniority level in organizational hierarchy:
1= Very Low, 2= Low, 3= Medium, 4=High, 5=Very High
JobInvolvement Engagement level at a job (1=Not engaged, 2= Somewhat not engaged, 3=
Somewhat engaged, 4= Engaged)
JobRole Job roles as specified such as Accountant, Machinists, Manufacturing
Director, Research Director, etc.
JobRotation Number of times employee has rotated within the organization:
1= low, 4=high
Mentoring Y = yes for involving in a mentoring relationship;
N = no for involving in a mentoring relationship
MonthlyIncome Monthly net pay ($)
MonthlyRate HR’s internal calculation of an employee’s cost to a company. It can be
higher or lower than a monthly income.
NumCompaniesWorked Number of other companies an employee has worked for
OverTime Whether work overtime (Yes/No)
Performance Rating of an employee’s job performance, ranging on a scale from 0 to 100
PercentSalaryHike Salary increase compared with last round (%)
RelationshipSatisfaction Satisfaction with the relationships with colleagues:
1=Low, 2=Medium, 3=High, 4=Very High
StockOptionLevel Stock option levels in the company ranging from 0 to 3: 0=None, 3=Highest
TotalWorkingYears Number of years at work (including companies other than MechTech)
Training “Y” = training assigned to the employees and “N” =no training
WorkLifeBalance Level of work-life balance:
1=Bad, 2=Moderate, 3= Good, 4=Very Good
YearsAtCompany Number of years at MechTech
YearsInCurrentRole Number of years in the current role
YearsSinceLastPromotion Number of years since last promotion
YearsWithCurrManager Number of years with the current manager

Appendix B. An Example of Report Template
Content page
Include:
• Page numbers from this page onwards (Insert à Page Number)
• A header from this page onwards, including your ZID and course code (Insert
à Header)
• All key sections and sub-sections of your report listed in the contents page
If you are unsure how to format a report contents page, select “References” in the
menu above, then “Table of Contents”. Examples:







Key sections to include in your report
1. Introduction
 Have you provided the purpose of your report?
 Have you given a brief outline of the contents of your report?

2. Summary Statistics
 Have you included relevant data in the form that best communicates it,
e.g. tables, figures, etc?
 Have you divided this data into clear sections or themes for readability?
 Have you clearly linked this data to the subject matter and how it is
relevant to the question and problem at hand?
 Have you referenced any literature or recent events that you
researched, if it provides useful insights into or justification for the
problem analysis?

3. Visualization Analysis
 Have you identified and explained the variables based on the data?
 Have you included relevant graphs which are appropriate for the type
of data you’re presenting?
 Have you used the data to clearly justify why the variables and
visualization techniques are related to the problem, drawing
conclusions about the variables that are the most relevant?

4. Recommendations
 Have you made at least one clear, actionable recommendation?
 Is/are your recommendation/s based on the conclusions/data-
supported variables above?

5. Reference List
 Have you included at least X references in your report?
 Are your references in alphabetical order?
 Do your referencing follow Harvard style as required?

6. Appendix
 Have you included your R code in the Appendix?
 Have you included any other supporting tables or figures in the
Appendix, as relevant?