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时间：2024-02-27
ASSESSMENT GUIDE
COMM5000
Data Literacy
Sandbox PwC Distribution Project
Milestone 1 Information
Term 1, 2024
UNSW Business School 1
Table of Contents
Assessment Summary ................................................................................................................................................................................... 2
Assessment Administrative Details ............................................................................................................................................................. 3
Turnitin .................................................................................................................................................................................................................................................. 3
Late Submissions .................................................................................................................................................................................................................................. 3
Extensions ............................................................................................................................................................................................................................................ 3
Special Consideration ........................................................................................................................................................................................................................... 3
CASE STUDY INFORMATION ........................................................................................................................................................................ 4
PricewaterhouseCoopers (PwC) Distribution Project Statement .............................................................................................................. 4
The context of COMM5000 ............................................................................................................................................................................ 4
PwC schedule of engagement ...................................................................................................................................................................... 5
Sandbox Project Showcase at PwC ............................................................................................................................................................. 5
MILESTONE 1: Preliminary Insight Development ....................................................................................................................................... 6
.............................................................................................................................................................................................................................................................. 6
Description of assessment task ............................................................................................................................................................................................................ 6
Approach to the assessment task ......................................................................................................................................................................................................... 6
Structure of the report ........................................................................................................................................................................................................................... 7
Submission instructions ........................................................................................................................................................................................................................ 8
Supporting resources and links ............................................................................................................................................................................................................. 8
Peer Marking/Feedback ........................................................................................................................................................................................................................ 8
Marking Rubrics ........................................................................................................................................................................................... 10
UNSW Business School
2
Assessment Summary
Assessment Task Weighting Due Date* Course Learning
Outcomes
Milestone 1: Case Study Preliminary Insight Development (due in
Week 4 20%)
20% Week 4 (Sunday 11:59 PM) 1, 2
Milestone 2: Case study project proposal 20% Week 7 (Friday 5PM) 1, 2, 3, 4
Case Study business report 60% Week 11 (Friday 5PM) 2, 3, 4, 5
* Due dates are set at Australian Eastern Standard/Daylight Time (AEST/AEDT). If you are located in a different time-zone, you can use the time and date converter.
UNSW Business School 3
Assessment Administrative Details
Turnitin
Turnitin is an originality checking and plagiarism prevention tool that enables checking of submitted written work for
improper citation or misappropriated content. Each Turnitin assignment is checked against other students' work, the
Internet and key resources selected by your Course Coordinator.
If you are instructed to submit your assessment via Turnitin, you will find the link to the Turnitin submission in your
Moodle course site. You can submit your assessment well before the deadline and use the Similarity Report to
improve your academic writing skills before submitting your final version.
You can find out more information on the Turnitin information site for students.
Late Submissions
The parameters for late submissions are outlined in the UNSW Assessment Implementation Procedure. For
COMM5000, if you submit your assessments after the due date, you will incur penalties for late submission unless you
have Special Consideration (see below). Late submission is 5% per day (including weekends), calculated from the
marks allocated to that assessment (not your grade). Assessments will not be accepted more than 5 days late.
Extensions
You are expected to manage your time to meet assessment due dates. If you do require an extension to your
assessment, please make a request as early as possible before the due date via the special consideration portal on
myUNSW (My Student profile > Special Consideration). You can find more information on Special Consideration and
the application process below. Lecturers and tutors do not have the ability to grant extensions.
Special Consideration
Special consideration is the process for assessing the impact of short-term events beyond your control (exceptional
circumstances), on your performance in a specific assessment task.
What are circumstances beyond my control?
These are exceptional circumstances or situations that may:
• Prevent you from completing a course requirement,
• Keep you from attending an assessment,
• Stop you from submitting an assessment,
• Significantly affect your assessment performance.
Available here is a list of circumstances that may be beyond your control. This is only a list of examples, and your
exact circumstances may not be listed.
You can find more detail and the application form on the Special Consideration site, or in the UNSW Special
Consideration Application and Assessment Information for Students.
UNSW Business School 4
CASE STUDY INFORMATION
PricewaterhouseCoopers (PwC) Distribution Project Statement
Wholesale distribution companies typically purchase products from manufacturers/suppliers and then sell them to retail
stores, making them available for consumers. Typically, wholesale distributors deal in large quantities of
goods and are set up to have warehouses, distribution centres, and logistic functions to manage and deliver
inventory to retail stores. We are interested in better understanding the profitability of wholesale distribution
companies.
By examining the profitability of wholesale distribution companies globally over the past five years (PwC to
provide an Excel file containing raw data), we aim to determine whether there is a correlation (positive or
negative) between their profitability and their local jurisdiction's GDP, along with other key economic metrics
or events (e.g., the COVID-19 pandemic, Ukraine war, Chinese property sector crisis, global inflation, interest
rate rises, etc.). If such correlations exist, what might be the reasons? Please provide both quantitative and
qualitative analysis to support your findings.
Additionally, considering their straightforward business model, wholesale distributors are not typically
involved in other key business functions such as manufacturing, R&D, retail trade, etc. Can researchers review
publicly available information of major global distribution companies and validate their key functions, assets,
and risks across various jurisdictions (e.g., comparing the activities performed, assets held, and risks borne by
wholesale distributors based in the US vs. China)? This comparison may reveal other drivers of profitability.
Please also provide any supporting analysis for these additional considerations.
The key jurisdictions we are interested in are the US, UK, China, Japan, South Korea, Australia, and New
Zealand.
The context of COMM5000
This project presents a genuine business inquiry that PwC is exploring. As a consultant engaged by PwC, you are tasked
with employing the COMM5000 data analysis toolkit, encompassing both descriptive and inferential statistical methods,
to dissect and interpret complex datasets.
This Sandbox project tackles a real-world data problem and provides a rare opportunity to acquire and hone analytical
skills that are highly sought after in the workplace. Engaging with actual data from a leading global firm allows you to
bridge the gap between theoretical knowledge and practical application, cultivating competencies that are quintessential
for business graduates. This hands-on experience aligns perfectly with the UNSW program learning outcomes. It aims
to instil a deep understanding of data interpretation and strategic decision-making, ensuring that graduates are well-
equipped to meet the evolving demands of the global business.
The work will be scaffolded into two milestones (M1 and M2, each worth 20%) and a final project report (60%). Each
milestone will require you to apply what you have learned to address specific aspects of the data. Typically, M1 consists
of exploratory data analysis, while M2 focuses on identifying hypotheses and formulating key inferential questions. The
final project report will utilize insights from M1 and M2 to model the data and answer the project's questions.
UNSW Business School
5
PwC schedule of engagement
It is imperative that you attend these sessions, where PwC delegates will conduct live synchronous sessions to discuss
the importance of analysing the factors affecting the profitability of distribution companies to their operations. You are
encouraged to ask questions and discuss any project aspects during these sessions. Please note, these sessions will not
be recorded.
1) Week 3, Monday 26th February (Stream1 at 12:00pm and Stream 2 at 4:00pm): A 30-minute presentation followed
by a 15-minute Q&A.
2) Week 8, Monday 8th April, at 12:00pm (Stream 1) and 4:00pm (Stream 2):: A 30-minute Q&A and mentoring session.
Sandbox Project Showcase at PwC
Students who express interest in their assessment submission may be considered for the Project Showcase at PwC. High-
Of the students who express interest, the highest performers in the assessment will be placed into teams (based on
common countries, industries etc.) and invited to present their analysis and insights of the PwC distribution project
problem in person at PwC’s Sydney office.
Each team will present for up to 15 minutes followed by 5 minutes of Q&A. In addition to their data literacy, students
are invited to showcase their sense making capabilities in the presentation.
To facilitate this, teams may choose to investigate additional sources of information (e.g. IBISWorld Industry Reports,
economics journals etc.) to assist them in developing a narrative of their choice of data analysis approach, the reasons
behind the identified data trends / correlations (or apparent lack thereof) and what it may mean for businesses in the
industry making decisions for the future.
Note that this presentation is not part of COMM5000 assessments. This is an important career opportunity to be invited
to showcase your work to an industry leader. A selection of the five best reports in Week 11. Those selected are invited
to present their analysis of the PwC distribution project problem in person.
UNSW Business School 6
MILESTONE 1: Preliminary Insight Development
Report details
Week 4, Sunday 11:59PM
20%
Report: This is individual work. Reports will be checked for plagiarism.
1500 words (not including tables, graphs, and references)
Via Moodle course site
Description of assessment task
This first milestone aims to give you a better understanding of the datasets, variables, and questions investigated by
PwC. This exploratory data analysis seeks to get the necessary insights so that a development plan can be formulated
to address the following key points of the distribution project:
(1) Correlation analysis: a cross-sectional analysis of correlation patterns between the profitability proxy variables and
the local jurisdiction’s key economic factors like GDP.
(2) Covid-19 effect analysis of cross-sectional patterns across the years may capture the effect of Covid on profitability
(by country).
You must submit a written development plan summarising the finding from the data explorations, describing any patterns
from comparing summary statistics of the variable of interest between countries, and providing a plan on how you may
address the key questions (1) and (2). The report should be concise and to the point.
As a style guide, you may include some or all tables/graphs as an appendix and refer to them as appropriate in your
report. You should only include graphics and tables to support your analysis, conclusions, and findings. While preparing
your paper, you will encounter numerous tables and graphs, which are irrelevant to the analysis. So be very selective
and make good use of the page limit!
Approach to the assessment task
In week 1, we learnt how to represent the data using graphical tools, as well as numerical summaries. All these tools
are meant to give us an idea of what the data is ‘trying to tell us’. Can we make sense of the large numbers of
observations and tell a simple story or pick up a trend? This is what you will do in this milestone: understand the data
and what PwC is trying to find out from the data.
(A) Expected Tasks
(i) Attend the presentation session by PwC. This will give you important information about the importance of this
analysis for PwC and will inform your formulation of the development plan for the analysis. You must understand
the problem, PwC’s expectations to inform your analysis.
(ii) Download your allocated country's spreadsheets. This is individual work, and you have been randomly assigned
companies for Milestone 1 analysis. Please check the file ‘Dataset Allocation to find out which companies you must
analyse.
(iii) Data preparation. Please check the document,‘Data Preparation and Data Cleaning’ to prepare your data for
analysis. The main data issue here is the missing points. These appear as ‘n.a.’ but sometimes also as ‘0’. In the
latter case, you should double-check whether the variable in question can have a ‘real’ 0 value or whether ‘0’, in
UNSW Business School
7
fact, represents an ‘n.a.’ missing value. Some strategies for dealing with missing points appear in the document and
will be discussed in your weekly SEM. You must explain any data manipulations you perform and provide a rationale
for them.
(iv) Variables of interest. While preparing the data, think about (1) and (2) above and ask what information you need
to be able to address these points. For instance, the columns in the data files have many variables that may proxy
for profitability, like:
1) Operating Revenue (Turnover) US$
2) Gross Profits US$
3) Gross Profits Mark-up (%)
4) Gross Profit / Total Operating Costs (%)
5) Gross Profit / Total Assets (%)
You must decide which variable(s) to address PwC profitability questions.
(v) Data structure. An essential aspect of the analysis is understanding the dataset's structure. The dataset provided
by PwC has three dimensions: country, company and year. For each country, we have a panel dataset. Each
panel is a cross-section of companies observed over six years. In this first milestone, we will fix the country
dimension and analyse the profitability variation across companies and years.
(vi) You may not have all the information needed in the data file to address (1) and (2). For example, PwC is interested
in understanding the effect of a country's key economic indicators (like GDP) on a company’s profitability. At this
point, you may want to ask how to use additional data to answer this question.
(B) The expected outcomes
The written work must provide a brief description of PwC business problem and a clear plan of how the dataset provided
will address the key questions raised in the project description. You will have the opportunity to adjust, revise and review
this plan as we progress throughout the term. M1 analysis is based on COMM5000 content covered in weeks 1 and 2.
(i) Numerical summaries of the key variables of interest: present the means, median, and standard deviations of the
variables in the data. You may represent these results in the form of tables. For example, for each country/Year
Mean Median Mode SD Min Max
Variable name 1
…
(ii) Graphical representations of some variables if you deem it important for to capture a trend or some interesting
patterns in the data. For instance, if you plot a bar chart of average profitability by country and by year, there
may be a pattern emerging in relation to jurisdiction and year effect.
(iii) What conclusions can you make from the inspection of these data summaries in the form of tables and graphs?
For example, is there a pattern in the tables when comparing the countries?
(iv) Your analysis should inform your development plan to address points (1) and (2) in Milestone 2 and in the final
report. This plan may be revised later during your work on Milestone 2.
Structure of the report
* The introduction You should briefly introduce the industry client, PwC, and its business operations and summarise
the purpose and importance of this project for PwC. Then outline how this preliminary insight development plan will be
structured. It is important to provide some background information on this topic, which is to say, what is the profitability
of the distribution company.
* Data Summaries and Descriptive Statistics: Provide the necessary analysis to explore the variables in each country.
Describe the trends and stories that emerge from the data summaries. Are any patterns emerging from the graphs or
tables you have constructed so far? Note: now that you have completed the first stage of data summaries, you have
some basic insight into the dataset. You can use this information to develop some plans of action to address points (1)
and (2) in Milestone 2 and in the final report.
* Conclusion: This should summarise the findings of your investigation and any concluding comments. The conclusion
should also provide your plan for the next step of the analysis.
UNSW Business School
8
Submission instructions
• Via Moodle course site.
Supporting resources and links
- The client: About PwC: https://www.pwc.com.au/about-us.html
- The Business problem: Importance of the distribution project to PwC: Week 3 at 12:00pm and 4:00 pm PwC
presentation
- Dataset files: PwC provided spreadsheets with information about distribution companies and their characteristics
for the following countries:
Australia (1,716 companies)
China (5,228 companies)
New Zealand (312 companies)
UK (6,298 companies)
US (199 companies)
The columns in each country dataset are variables observed over six years (2017-2022). The dataset has the
following dimensions: distribution company, country of jurisdiction, and year.
The spreadsheets are available on Moodle. You only need to analyse the companies allocated to you. Check the
‘Company allocation’ file.
- Check the ‘Data Preparation and Data Cleaning’ document for details on preparing your data for analysis in Excel.
- Weekly seminar: the seminar coordinator will cover aspects of data manipulation using Excel during the SEM
session
Peer Marking/Feedback
This first Milestone will be assessed following a ‘Marking Rubric’ (attached below). We will use anonymous peer
review and feedback to evaluate your analysis of M1 tasks.
More details on the process will be available on Moodle in Week 3.
UNSW Business School 9
UNSW Business School 10
Marking Rubrics
Milestone 1: Rubric doc file here.
UNSW Business School
COMM5000
Data Literacy
Sandbox PwC Distribution Project
Milestone 1 Information
Term 1, 2024
UNSW Business School 1
Table of Contents
Assessment Summary ................................................................................................................................................................................... 2
Assessment Administrative Details ............................................................................................................................................................. 3
Turnitin .................................................................................................................................................................................................................................................. 3
Late Submissions .................................................................................................................................................................................................................................. 3
Extensions ............................................................................................................................................................................................................................................ 3
Special Consideration ........................................................................................................................................................................................................................... 3
CASE STUDY INFORMATION ........................................................................................................................................................................ 4
PricewaterhouseCoopers (PwC) Distribution Project Statement .............................................................................................................. 4
The context of COMM5000 ............................................................................................................................................................................ 4
PwC schedule of engagement ...................................................................................................................................................................... 5
Sandbox Project Showcase at PwC ............................................................................................................................................................. 5
MILESTONE 1: Preliminary Insight Development ....................................................................................................................................... 6
.............................................................................................................................................................................................................................................................. 6
Description of assessment task ............................................................................................................................................................................................................ 6
Approach to the assessment task ......................................................................................................................................................................................................... 6
Structure of the report ........................................................................................................................................................................................................................... 7
Submission instructions ........................................................................................................................................................................................................................ 8
Supporting resources and links ............................................................................................................................................................................................................. 8
Peer Marking/Feedback ........................................................................................................................................................................................................................ 8
Marking Rubrics ........................................................................................................................................................................................... 10
UNSW Business School
2
Assessment Summary
Assessment Task Weighting Due Date* Course Learning
Outcomes
Milestone 1: Case Study Preliminary Insight Development (due in
Week 4 20%)
20% Week 4 (Sunday 11:59 PM) 1, 2
Milestone 2: Case study project proposal 20% Week 7 (Friday 5PM) 1, 2, 3, 4
Case Study business report 60% Week 11 (Friday 5PM) 2, 3, 4, 5
* Due dates are set at Australian Eastern Standard/Daylight Time (AEST/AEDT). If you are located in a different time-zone, you can use the time and date converter.
UNSW Business School 3
Assessment Administrative Details
Turnitin
Turnitin is an originality checking and plagiarism prevention tool that enables checking of submitted written work for
improper citation or misappropriated content. Each Turnitin assignment is checked against other students' work, the
Internet and key resources selected by your Course Coordinator.
If you are instructed to submit your assessment via Turnitin, you will find the link to the Turnitin submission in your
Moodle course site. You can submit your assessment well before the deadline and use the Similarity Report to
improve your academic writing skills before submitting your final version.
You can find out more information on the Turnitin information site for students.
Late Submissions
The parameters for late submissions are outlined in the UNSW Assessment Implementation Procedure. For
COMM5000, if you submit your assessments after the due date, you will incur penalties for late submission unless you
have Special Consideration (see below). Late submission is 5% per day (including weekends), calculated from the
marks allocated to that assessment (not your grade). Assessments will not be accepted more than 5 days late.
Extensions
You are expected to manage your time to meet assessment due dates. If you do require an extension to your
assessment, please make a request as early as possible before the due date via the special consideration portal on
myUNSW (My Student profile > Special Consideration). You can find more information on Special Consideration and
the application process below. Lecturers and tutors do not have the ability to grant extensions.
Special Consideration
Special consideration is the process for assessing the impact of short-term events beyond your control (exceptional
circumstances), on your performance in a specific assessment task.
What are circumstances beyond my control?
These are exceptional circumstances or situations that may:
• Prevent you from completing a course requirement,
• Keep you from attending an assessment,
• Stop you from submitting an assessment,
• Significantly affect your assessment performance.
Available here is a list of circumstances that may be beyond your control. This is only a list of examples, and your
exact circumstances may not be listed.
You can find more detail and the application form on the Special Consideration site, or in the UNSW Special
Consideration Application and Assessment Information for Students.
UNSW Business School 4
CASE STUDY INFORMATION
PricewaterhouseCoopers (PwC) Distribution Project Statement
Wholesale distribution companies typically purchase products from manufacturers/suppliers and then sell them to retail
stores, making them available for consumers. Typically, wholesale distributors deal in large quantities of
goods and are set up to have warehouses, distribution centres, and logistic functions to manage and deliver
inventory to retail stores. We are interested in better understanding the profitability of wholesale distribution
companies.
By examining the profitability of wholesale distribution companies globally over the past five years (PwC to
provide an Excel file containing raw data), we aim to determine whether there is a correlation (positive or
negative) between their profitability and their local jurisdiction's GDP, along with other key economic metrics
or events (e.g., the COVID-19 pandemic, Ukraine war, Chinese property sector crisis, global inflation, interest
rate rises, etc.). If such correlations exist, what might be the reasons? Please provide both quantitative and
qualitative analysis to support your findings.
Additionally, considering their straightforward business model, wholesale distributors are not typically
involved in other key business functions such as manufacturing, R&D, retail trade, etc. Can researchers review
publicly available information of major global distribution companies and validate their key functions, assets,
and risks across various jurisdictions (e.g., comparing the activities performed, assets held, and risks borne by
wholesale distributors based in the US vs. China)? This comparison may reveal other drivers of profitability.
Please also provide any supporting analysis for these additional considerations.
The key jurisdictions we are interested in are the US, UK, China, Japan, South Korea, Australia, and New
Zealand.
The context of COMM5000
This project presents a genuine business inquiry that PwC is exploring. As a consultant engaged by PwC, you are tasked
with employing the COMM5000 data analysis toolkit, encompassing both descriptive and inferential statistical methods,
to dissect and interpret complex datasets.
This Sandbox project tackles a real-world data problem and provides a rare opportunity to acquire and hone analytical
skills that are highly sought after in the workplace. Engaging with actual data from a leading global firm allows you to
bridge the gap between theoretical knowledge and practical application, cultivating competencies that are quintessential
for business graduates. This hands-on experience aligns perfectly with the UNSW program learning outcomes. It aims
to instil a deep understanding of data interpretation and strategic decision-making, ensuring that graduates are well-
equipped to meet the evolving demands of the global business.
The work will be scaffolded into two milestones (M1 and M2, each worth 20%) and a final project report (60%). Each
milestone will require you to apply what you have learned to address specific aspects of the data. Typically, M1 consists
of exploratory data analysis, while M2 focuses on identifying hypotheses and formulating key inferential questions. The
final project report will utilize insights from M1 and M2 to model the data and answer the project's questions.
UNSW Business School
5
PwC schedule of engagement
It is imperative that you attend these sessions, where PwC delegates will conduct live synchronous sessions to discuss
the importance of analysing the factors affecting the profitability of distribution companies to their operations. You are
encouraged to ask questions and discuss any project aspects during these sessions. Please note, these sessions will not
be recorded.
1) Week 3, Monday 26th February (Stream1 at 12:00pm and Stream 2 at 4:00pm): A 30-minute presentation followed
by a 15-minute Q&A.
2) Week 8, Monday 8th April, at 12:00pm (Stream 1) and 4:00pm (Stream 2):: A 30-minute Q&A and mentoring session.
Sandbox Project Showcase at PwC
Students who express interest in their assessment submission may be considered for the Project Showcase at PwC. High-
Of the students who express interest, the highest performers in the assessment will be placed into teams (based on
common countries, industries etc.) and invited to present their analysis and insights of the PwC distribution project
problem in person at PwC’s Sydney office.
Each team will present for up to 15 minutes followed by 5 minutes of Q&A. In addition to their data literacy, students
are invited to showcase their sense making capabilities in the presentation.
To facilitate this, teams may choose to investigate additional sources of information (e.g. IBISWorld Industry Reports,
economics journals etc.) to assist them in developing a narrative of their choice of data analysis approach, the reasons
behind the identified data trends / correlations (or apparent lack thereof) and what it may mean for businesses in the
industry making decisions for the future.
Note that this presentation is not part of COMM5000 assessments. This is an important career opportunity to be invited
to showcase your work to an industry leader. A selection of the five best reports in Week 11. Those selected are invited
to present their analysis of the PwC distribution project problem in person.
UNSW Business School 6
MILESTONE 1: Preliminary Insight Development
Report details
Week 4, Sunday 11:59PM
20%
Report: This is individual work. Reports will be checked for plagiarism.
1500 words (not including tables, graphs, and references)
Via Moodle course site
Description of assessment task
This first milestone aims to give you a better understanding of the datasets, variables, and questions investigated by
PwC. This exploratory data analysis seeks to get the necessary insights so that a development plan can be formulated
to address the following key points of the distribution project:
(1) Correlation analysis: a cross-sectional analysis of correlation patterns between the profitability proxy variables and
the local jurisdiction’s key economic factors like GDP.
(2) Covid-19 effect analysis of cross-sectional patterns across the years may capture the effect of Covid on profitability
(by country).
You must submit a written development plan summarising the finding from the data explorations, describing any patterns
from comparing summary statistics of the variable of interest between countries, and providing a plan on how you may
address the key questions (1) and (2). The report should be concise and to the point.
As a style guide, you may include some or all tables/graphs as an appendix and refer to them as appropriate in your
report. You should only include graphics and tables to support your analysis, conclusions, and findings. While preparing
your paper, you will encounter numerous tables and graphs, which are irrelevant to the analysis. So be very selective
and make good use of the page limit!
Approach to the assessment task
In week 1, we learnt how to represent the data using graphical tools, as well as numerical summaries. All these tools
are meant to give us an idea of what the data is ‘trying to tell us’. Can we make sense of the large numbers of
observations and tell a simple story or pick up a trend? This is what you will do in this milestone: understand the data
and what PwC is trying to find out from the data.
(A) Expected Tasks
(i) Attend the presentation session by PwC. This will give you important information about the importance of this
analysis for PwC and will inform your formulation of the development plan for the analysis. You must understand
the problem, PwC’s expectations to inform your analysis.
(ii) Download your allocated country's spreadsheets. This is individual work, and you have been randomly assigned
companies for Milestone 1 analysis. Please check the file ‘Dataset Allocation to find out which companies you must
analyse.
(iii) Data preparation. Please check the document,‘Data Preparation and Data Cleaning’ to prepare your data for
analysis. The main data issue here is the missing points. These appear as ‘n.a.’ but sometimes also as ‘0’. In the
latter case, you should double-check whether the variable in question can have a ‘real’ 0 value or whether ‘0’, in
UNSW Business School
7
fact, represents an ‘n.a.’ missing value. Some strategies for dealing with missing points appear in the document and
will be discussed in your weekly SEM. You must explain any data manipulations you perform and provide a rationale
for them.
(iv) Variables of interest. While preparing the data, think about (1) and (2) above and ask what information you need
to be able to address these points. For instance, the columns in the data files have many variables that may proxy
for profitability, like:
1) Operating Revenue (Turnover) US$
2) Gross Profits US$
3) Gross Profits Mark-up (%)
4) Gross Profit / Total Operating Costs (%)
5) Gross Profit / Total Assets (%)
You must decide which variable(s) to address PwC profitability questions.
(v) Data structure. An essential aspect of the analysis is understanding the dataset's structure. The dataset provided
by PwC has three dimensions: country, company and year. For each country, we have a panel dataset. Each
panel is a cross-section of companies observed over six years. In this first milestone, we will fix the country
dimension and analyse the profitability variation across companies and years.
(vi) You may not have all the information needed in the data file to address (1) and (2). For example, PwC is interested
in understanding the effect of a country's key economic indicators (like GDP) on a company’s profitability. At this
point, you may want to ask how to use additional data to answer this question.
(B) The expected outcomes
The written work must provide a brief description of PwC business problem and a clear plan of how the dataset provided
will address the key questions raised in the project description. You will have the opportunity to adjust, revise and review
this plan as we progress throughout the term. M1 analysis is based on COMM5000 content covered in weeks 1 and 2.
(i) Numerical summaries of the key variables of interest: present the means, median, and standard deviations of the
variables in the data. You may represent these results in the form of tables. For example, for each country/Year
Mean Median Mode SD Min Max
Variable name 1
…
(ii) Graphical representations of some variables if you deem it important for to capture a trend or some interesting
patterns in the data. For instance, if you plot a bar chart of average profitability by country and by year, there
may be a pattern emerging in relation to jurisdiction and year effect.
(iii) What conclusions can you make from the inspection of these data summaries in the form of tables and graphs?
For example, is there a pattern in the tables when comparing the countries?
(iv) Your analysis should inform your development plan to address points (1) and (2) in Milestone 2 and in the final
report. This plan may be revised later during your work on Milestone 2.
Structure of the report
* The introduction You should briefly introduce the industry client, PwC, and its business operations and summarise
the purpose and importance of this project for PwC. Then outline how this preliminary insight development plan will be
structured. It is important to provide some background information on this topic, which is to say, what is the profitability
of the distribution company.
* Data Summaries and Descriptive Statistics: Provide the necessary analysis to explore the variables in each country.
Describe the trends and stories that emerge from the data summaries. Are any patterns emerging from the graphs or
tables you have constructed so far? Note: now that you have completed the first stage of data summaries, you have
some basic insight into the dataset. You can use this information to develop some plans of action to address points (1)
and (2) in Milestone 2 and in the final report.
* Conclusion: This should summarise the findings of your investigation and any concluding comments. The conclusion
should also provide your plan for the next step of the analysis.
UNSW Business School
8
Submission instructions
• Via Moodle course site.
Supporting resources and links
- The client: About PwC: https://www.pwc.com.au/about-us.html
- The Business problem: Importance of the distribution project to PwC: Week 3 at 12:00pm and 4:00 pm PwC
presentation
- Dataset files: PwC provided spreadsheets with information about distribution companies and their characteristics
for the following countries:
Australia (1,716 companies)
China (5,228 companies)
New Zealand (312 companies)
UK (6,298 companies)
US (199 companies)
The columns in each country dataset are variables observed over six years (2017-2022). The dataset has the
following dimensions: distribution company, country of jurisdiction, and year.
The spreadsheets are available on Moodle. You only need to analyse the companies allocated to you. Check the
‘Company allocation’ file.
- Check the ‘Data Preparation and Data Cleaning’ document for details on preparing your data for analysis in Excel.
- Weekly seminar: the seminar coordinator will cover aspects of data manipulation using Excel during the SEM
session
Peer Marking/Feedback
This first Milestone will be assessed following a ‘Marking Rubric’ (attached below). We will use anonymous peer
review and feedback to evaluate your analysis of M1 tasks.
More details on the process will be available on Moodle in Week 3.
UNSW Business School 9
UNSW Business School 10
Marking Rubrics
Milestone 1: Rubric doc file here.
UNSW Business School
