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August 2023
Diploma in Business
Assessment Guide
* 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.
2. CLO 1 Explain how an organisation uses analytical and statistical tools to gain valuable insights.
3. CLO 2 Apply statistics and data analysis skills to real data sets from a variety of organisations and
domains to generate insights in order to make informed decisions.
4. CLO3 Visualize and analyse data to support arguments that increase comprehension of
information, insights and problem solving.
5. CLO4 Effectively communicate data insights and recommendations to a range of stakeholders.
6. CLO5 Evaluate ethical implications of organisational use of big data and analytics on stakeholders
and society.
7. CLO6 Critically evaluate the suitability of data and data sources to identify and analyse business
problems.
Due Date Weighting Format Length/Duration Submission
Each of these icons are used for the various assessment tasks. Details are provided below.
Turnitin is an originality and plagiarism prevention tool that enables the assessment of submitted
written work for improper citation or misappropriated content. Each Turnitin task is checked against
other students' work, the internet and key resources selected by the course convenor.
An artificial intelligence (AI) writing detection tool is embedded within Turnitin and is able to identify
the extent to which a student’s response has been generated using technology such as Chat GPT, Bard
(Google), Bing and/or others. Whilst this tool will not in itself be used as an indication that a student
has engaged in academic misconduct the tool can be used with other information to investigate the
situation more fully.
UNSW College teaches and assesses in English, except in language courses. Using digital translators
is also not permitted. One of the reasons is because language is not merely words, but also based in
particular contexts. A pure translation of words will not necessarily reflect its context.
The best way to produce English language work is to write in English. The use of generative AI
including translators is likely to appear in a Turnitin report. If your response is not written in English you
cannot assume the marker can read your work to verify that you understood the question being
assessed.
Further, language editing will likely be identified as AI generated writing. This course does not permit
the use of these tools. If, however you inadvertently make use of AI translators such as Google
translate, Bing Microsoft Translate, Grammarly and/or others please ensure that you record it and keep
drafts of your work. You may be required to provide previous drafts of your work if you are asked about
how you developed it.
In this course it is possible to make use of generative AI such as those identified above, in the planning
process only for any submitted assessment work. For example, using the software to generate initial
ideas that may direct your response.
It is essential that students edit the information they gather using the AI to such an extent that only
their own work is submitted. It is recommended (as mentioned previously) that students keep
evidence of this formative work including drafts should there be concerns about the originality of
their response.
Please be advised that in the event there is evidence of use of generative AI (beyond that outlined
above) to form any significant part or all of a submitted response it will be regarded as serious
academic misconduct and subjected to an investigation to determine the appropriate penalty. Please
refer to the Student Handbook for more information.
Additional information about acceptable use of AI can be found at
https://www.student.unsw.edu.au/assessment/ai. Please note that this information is general in nature
and that the specifics of the expectations in this course are identified above.
If you submit your (written) assessment after the due date, you may incur penalties for late submission.
The final examination response however can only be submitted on the day of the examination in
accordance with the information provided beforehand. Refer to the course outline for details. Late
submission of quiz responses is not possible unless prior permission has been obtained by the course
convenor.
You are expected to manage your time to meet assessment requirements including submission by the due
date. If you do however require an extension for the submission of a task, it is important that you contact
the course convenor/your tutor in the first instance to discuss the request as soon as you are aware of why
you need this consideration. Note that an extension will only be granted in certain circumstances.
Special consideration is the process for assessing the impact of short-term events beyond your control
(exceptional circumstances), that may impact your performance in an assessment task. Always seek advice
from the course convenor/your tutor first, before applying for any special consideration. As a guide an
exceptional circumstance generally:
• Prevents you from completing a course requirement
• Keeps you from attending or submitting an assessment
• Significantly affects your assessment performance
You can find more details in the course outline.
As a student at UNSW you are expected to display academic integrity in your work and interactions. Where
a student breaches the UNSW Student Code with respect to academic integrity, the University may take
disciplinary action under the Student Misconduct Procedure. To assure academic integrity, you may be
required to demonstrate reasoning, research and the process of constructing work submitted for
assessment.
To assist you in understanding what academic integrity means, and how to ensure that you do comply with
the UNSW Student Code, it is strongly recommended that you complete the Working with Academic
Integrity module before submitting your first assessment task. It is a free, online self-paced Moodle
module that should take about one hour to complete.
Week 2 -12
15%
Pre-Tutorial and In-class activities
Tutorial class duration
During the Tutorial Class
The purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• visualize and analyse data to support arguments that increase comprehension of information,
insights, and problem solving
• apply statistics and data analysis skills to real data sets from a variety of organisations and domains
to generate insights in order to make informed decisions
• effectively communicate data insights and recommendations to a range of stakeholders
• evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society
• critically evaluate the suitability of data and data sources to identify and analyse business problems
There will be ten (10) sets of pre- tutorial and in-class tutorial activities, each consisting of a variety of short
response questions and application of data analytics concepts. These questions relate to the lecture
content from the previous week(s).
Pre-tutorial and in class activities will be assessed in Weeks 2-6 and 8-12 inclusive in bi-weekly tutorials.
Each week’s pre-tutorial and in-class tutorial activities are worth of ten (10) marks for a total of 100 marks.
Please note that each week has 2 tutorials and each tutorial will have pre-tutorial and in class activities.
Students will be assessed on their completed pre-tutorial task and in-class activities each week during the
tutorial classes relating to preselected questions provided by the course convenor.
Please note that there is no mark awarded only for attendance. You have to be present in class, attempt the
pre- tutorial tasks and the in-class tutorial exercises provided and demonstrate your work.
It is expected that you participate responding through microphone, sharing your computer screen and
whiteboard; or other appropriate means, as determined by the course convenor.
Each bi weekly tutorial classwork is marked out of 5 giving a total raw mark of (5 x 2 x 10) = 100 which is
then scaled to a 15% weighting.
For this assessment task, you will be marked according to the criteria provided below.
4:00pm Friday, 6th October, 2023 (AEST/AEDT)
25%
Writing task based on a project
Maximum word limit 1000 excluding references and R codes
Via Moodle course site through Turnitin
The purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• apply statistics and data analysis skills to real data sets from a variety of organisations and
domains to generate insights in order to make informed decisions
• visualize and analyse data to support arguments that increase comprehension of information,
insights and problem solving
• effectively communicate data insights and recommendations to a range of stakeholders
This assessment task is geared to test your understanding about data visualization through R (software) and
the application of visualization in generating insights. You are required to present your findings, supported
by data visualization in the form of a written report. You are expected to provide references as appropriate.
4.1.1 Description of assessment task
This is an individual assessment. The assessment task focuses on data visualization using a dataset
on start-up companies across different cities in Australia. A start-up is a new company, generally
established by one or more entrepreneurs with an objective of bringing innovation and unique style
of product and services. Leading examples of start-up includes: Facebook, Google, Airbnb, Uber,
DoorDash, and Instagram. You can have a brief conceptual overview on start-up companies here:
https://www.investopedia.com/terms/s/startup.asp
The assessment dataset on start-up companies is available in the Moodle and it consists of a
number of variables. The following variables are included in the dataset and explanation for each
variable is provided below.
R&D = Research and Development expenses
Administration= Administrative expenses
Marketing= Marketing expenses
SeedFunding= The amount of seed funding received by each company. It means the equity
contribution by the private investors in the start-up companies. Generally, seed funding comes
from sources close to founders of start-ups; including friends, and families. This is generally the first
stage of financing of stat-up companies.
City = Different locations where the start-up companies are established
Equipment= Cost of equipment incurred
Website = Cost of developing company website
Office Furnitures and Supplies = Cost of office furniture and supplies
Payroll= Payroll expenses for each year
Professional Consultants= Fees paid to professional consultants
Profit= Profit earned by companies
StartYear = The year in which companies started
IsSuccessful = 1 indicates successful and 0 is for not-successful
Ownership = 1 indicates sole trader, 2 is for partnership and 3 is for large company
Size = 1 indicates small, 2 is for medium and 3 is for large enterprise
You are a junior data analyst working for an Australian market research company - MarketGo. Your
manager has asked you to undertake an exploratory data analysis using R to investigate the pattern
and relationship among different variables in regards to a number of start-up companies and prepare
a report.
You must present your findings, supported by data visualisations, in the form of a written report
(maximum of 1000 words) that should include:
➢ Descriptive statistics of relevant variables in the dataset using the ‘moments’ package
and explain why such statistics are relevant to your analysis. There should be a clear
analysis what these statistics mean in the context of analysing the data that you are
dealing with?
➢ Data sub-setting that you deem necessary to conduct your analysis.
➢ A visual data analysis (including bar plot, histogram, line chart, box plot and bubble plot) to
demonstrate
o attributes between successful and not-successful start-ups;
o trend of variables that you deem important for the success of companies. You
are free to choose these variables and provide justification why these variables
are considered important for your analysis; and,
o outlier analysis using box plot focusing on two different variables along with
clearly explaining the implications of outliers, if any, in your data visualization.
You are free to choose these variables.
➢ You are expected to apply your broader understanding about the operation of start-up
companies and interpret your findings and actionable insights from your visualization
exercise. In order to support your analysis, you are strongly advised to undertake online
research appropriately to review the articles/research papers on start-up business.
4..2 Supporting resources and links
The assessment dataset is available in the Moodle under the section – Assessment 2: Individual Project
Report
4.3 Tips for analysing the data
You may consider the following advice on exploring the dataset:
• It is important to emphasize that there is not only one correct answer to the assignment. There
are number of different dimensions of the data to explore, and some aspects and dimensions
of the data are likely to be more useful than others. Thus, it is important that prior to starting
your assignment, you systematically explore the different variables in the dataset.
• Remember, it is important to highlight the relevant factors responsible for your analysis and it
is critical to place detailed arguments appropriately. This should be the key focus of your
analysis. Just providing commentary on visualization is not enough. You need to relate the
findings of visualization to your analysis in a thorough manner. Always remember, the ability to
relate analytics to the business issue is fundamental. It is not just a technical issue, it is a business
issue.
• To help focus your analysis and insights, think of the general nature of start-up companies and
the potential factors that may contribute to the success of these companies. This can help
provide greater structure for your analysis. You are strongly advised to undertake online
research as to have an overview about the nature of the start-up companies and understand the
attributes which may be important for the success of start-up companies.
• You are expected to do multiple bar plots, line charts, histograms, box plots and bubble plots, for your
assessment as you deem appropriate to better understand the data, and provide an insightful
discussion. These graphs should summarize the relationships that you are reporting on or
analysing. You also need to perform descriptive analysis using the ‘moments’ package and
explain implications in the context of the dataset.
• To ensure the rigour of analysis, apply the frameworks/R codes discussed and practised in
class. This is important as we are not expecting the use of analytical methods beyond the
scope of this course.
• Also look for potential outliers in the dataset using box plot. What can you infer from these
outliers? How these outliers affect your analysis? Should the outliers be included in the
analysis of the data? Any decisions made about including or not including outliers should be
justified in the report.
• Remember that your conclusions should be well supported by the undertaken data exploration
and created visualisations. You should also outline any key assumptions in your data-driven
conclusions and acknowledge limitations.
• A declaration signed by you confirming that it is the original work of you (word count will not apply
for this). This declaration should include the statement with your signature: “I declare that the work
that I have submitted relating to this assessment is completely of my own and I confirm that I have
complied with all requirements of UNSW Academic Honesty and plagiarism policy” .
• You must comply with all applicable requirements of UNSW Academic Honesty and Plagiarism Policy.
Should you need further clarification, consult the Academic Honesty and Plagiarism Policy available
in the course Moodle page.
• You are required to provide appropriate references (done via Harvard in-text reference). This
do not count towards the assessment’s word count. Consult the link for further information
about referencing https://www.student.unsw.edu.au/harvard-referencing
4.4 Submission instruction
Submit a word document with all relevant R code and references in the appendix to Turnitin assessment
submission link on Moodle. You must submit your work by 4:00pm Friday, 6th October, 2023 (AEST/AEDT).
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day, including
the weekend and public holidays.
4:00pm Friday, 17th November, 2023 (AEST/AEDT)
30%
Writing task, based on analysis of big data set
Maximum word count of 2000, excluding references and R code
Via Moodle course site, through Turnitin
The purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• visualize and analyse data to support arguments that increase comprehension of information,
insights, and problem solving
• apply statistics and data analysis skills to real data sets from a variety of organisations and domains
to generate insights in order to make informed decisions
• effectively communicate data insights and recommendations to a range of stakeholders
• evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society
• critically evaluate the suitability of data and data sources to identify and analyse business problems
The group project will help the students to:
• make individual contribution to shape the idea of the group
• learn successfully work in teams and reflect on strategies in achieving group objectives
• design experimentation, undertake data analysis and building predictive models
• apply wide range of perspectives in solving organisational problems for achieving the best possible
solutions including to understand and resolve contextual limitations that an organisation may face in
real-world
• deliver an effective and well justified analytic solution
• communicate key message and develop skills of presentation to a broad group of stakeholders,
including non-technical audience
5.1.1 Selection of group
Students will need to select their own groups. The maximum number of students in each group should be 4.
In order to select the group; students will need to click the Group selection for Assessment 3: Group Project
in the Moodle and join the group that they want. Self-selection of group will offer flexibility and allow students
to choose their own peers with whom they like to work.
The group selection should be completed by week 5.
5.1.2 Team contract
Each group should develop a team contract as per the following format. It must be signed and dated by the
group members. The team contract should be handed over to the course convenor by week 8 via email.
The following is a list of items you may wish to include in your team contract.
We, the members of (group name) agree to the following plan of action regarding our work toward the group
assignment tasks.
5.1.3 Meetings and communication
• Number of weekly online/face to face meetings.
• Person coordinating the meeting for each. Each member will take their turn.
• Who will summarise decisions, when will he/she make them available to all members. Each
member will take their turn.
5.1.4 Work and deadlines
• How will the group come to agreement on a topic (what research are members expected to do
before you meet / go online to discuss the topic)?
• When will you make a final decision on a topic?
• Allocation of tasks among group members including the deadline set.
• Who will collate the draft submissions and then circulate it for the group to comment on?
• Who will prepare and submit the final submission in Turnitin?
5.1.5 Penalties
• What happens if members don’t meet agreed-to deadlines?
• What happens if members do not contribute / come to meetings?
• If any member does not participate as per the team contract, this should be reported to the course
Convenor by the end of Week 10 via email with the evidence.
5.2 Supporting resources and links
The assessment dataset is available in the Moodle under the section – Assessment 3: Group Project
Report.
5.2.1 Project topic, tasks and Report
This is a group assessment. In this assessment, you will continue to explore the assessment dataset,
available in the Moodle. You have joined MarketGo as a member of the data analytics team. You are
now required to work with your team members to conduct predictive analytics using R particularly
focusing on the following:
➢ Defining clear project goals
➢ develop predictive models using regression R codes and splitting the data into train and test
data with a ratio of 80:20;
➢ using ‘leaps’ package, develop best subset regression model to identify relevant variables
having implications in your predictive analytics;
➢ performance of start-up companies based on the selected variables, outlining a comparative
analysis based on three different cities;
➢ using information from the dataset, provide a well-focused analysis as to which factors
contribute to the successful start-up;
➢ based on the above predictive exercise; you will be required to derive actionable insights and
make recommendations for the start-ups to improve their performance. In this regard, you
should use your broader knowledge on the operation of start-up companies and make intuitive
assumptions together with analysis of the dataset given;
➢ You must present your findings, supported by appropriate predictive analytics in the form of a
written report (approx. 2000 words).
5.3 Tips for analysing the data
Here is advice on developing models and recommendations:
• It is important to emphasise that there is not only one correct answer to the assignment. There
are many different models that can be put forward to effectively address your project goals.
Thus, it is important that you clearly identify the analytics methods and set out a systematic,
comprehensive plan in line with your project goals. Always remember, the ability to relate
analytics to the business issue is fundamental. It is not just a technical issue alone, it is a
business issue.
• You need to split the dataset into train and test data with 80:20 ratio together with doing data
subsets in line with your project goals.
• You are expected to use applicable R codes and packages that are discussed in the course to
develop your models.
• To ensure the rigour of the model development and subsequent analysis, apply the
frameworks/R codes discussed and practised in class. We are not expecting the use of
analytical methods beyond the scope of this course.
• Remember that your conclusions should be well supported by the created models. You should
also outline any key assumptions in your data-driven conclusions and acknowledge
limitations.
• In terms of factors responsible for successful operations of start-up companies, you should
use knowledge and insights gained through undertaking online research and making intuitive
assumptions. Remember, business analytics should work like designers, exploring possible
alternatives and through understanding specific business requirements.
• Where appropriate, connect findings or questions from your individual reports to your team
report.
• The report should have the following sections and must be presented as per the sequence below:
✓ Executive summary (150 words). It must provide a solid overview of your project so that the reader
should have a clear understanding of your report without going into main report.
✓ Introduction outlining the rationale for using the big data in predictive analysis in management
decision making. In this section you must relate this with managing the operations of start-up
business. (150 words).
✓ Project goals: key questions to be addressed in the project. What you would like to have the major
focus of your project and how these goals could be related with the dataset given to you? (50
words).
✓ An outline of ‘design and agile thinking concept’ in developing the project. You should explain how
you have applied this concept in developing your project goals (150 words). Please note just
explaining the design and agile thinking concept alone will not attract any marks. This section
must highlight how your group have practically approached and used this concept in developing
your project showing specific examples.
✓ Methods used to analyse and interpret the data. You need to focus on predictive analytics to
derive actionable insights that can be used to assist with business decisions in the context of
your project goals. You need to use R in this regard; together with justification why you are using
this particular software together with selected predictive analytics methods (100 words).
✓ Findings of the project. This should include actionable insights from the data in the context of
project goals together with your research findings. Findings should be presented in a logical
manner outlining key message(s) to a range of stakeholders, primarily targeted to the senior
management of MarketGo. These should be presented under headings and sub-headings. You
need to clearly put arguments in favour of your findings demonstrating your conceptual
understanding and application in the context of real-life business scenario. You should also
outline any intuitive assumptions that you may have made while working on this project. In this
section, you may outline about the limitations of the dataset, if any (900 words).
✓ Recommended actions and conclusion in line with your findings including the strategies for
improved operations of start-up companies (400 words).
✓ Your group reflection as to what you have learnt from the DPBS 1190 and how such learning has
helped you in undertaking this project (100 words).
✓ R codes used to analyse and interpret the data should be given in the appendix (word count will
not apply).
✓ Table demonstrating each member’s participation in their respective allocated work based on the
team contract (word count will not apply).
✓ A declaration signed by all members of the group confirming that it is the original work of group
members (word count will not apply for this). This declaration should include the statement with
signature from all members of the group: “We declare that the work we have submitted relating to
this assessment task is completely of our own and we confirm that we have complied with all
requirements of UNSW Academic Honesty and plagiarism policy”.
✓ You must comply with all applicable requirements of UNSW Academic Honesty and Plagiarism
Policy. Should you need further clarification, consult the Academic Honesty and Plagiarism Policy
available in the course Moodle page.
✓ You are required to provide appropriate references (done via Harvard in-text reference). This do
not count towards the assessment’s word count. Consult the link for further information about
referencing https://www.student.unsw.edu.au/harvard-referencing
✓ Your report should demonstrate a thorough analysis of relevant data set in the context of your
project goals using knowledge gained in the course. The project report should be written clearly
and concisely within a 2000-word limit (excluding references, appendices and R code) for the
understanding of non-technical audience.
5.4 Submission instructions
Submit a word document with all relevant R codes and references to the Turnitin assessment submission
link on Moodle. You must submit your work by 4 pm on Friday 17th November, 2023 (AEST/AEDT).
One member from each group should submit this assessment on behalf of their respective groups.
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day, including
the weekend and public holidays.
5.5 Supporting resources and links
You should get guidance on group work through visiting https://student.unsw.edu.au/groupwork
4:00 pm Friday, 24th November, 2023 (AEST/AEDT)
30%
Individual presentation in audio-video format clearly showing the face either by voice over
power point presentation (VOPP) or recording via Zoom
Maximum 4 Power point slides, including the title slide
Via Moodle course site, through Assignment
This is an individual assessment task.
This purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• visualize and analyse data to support arguments that increase comprehension of information,
insights, and problem solving
• apply statistics and data analysis skills to real data sets from a variety of organisations and
domains to generate insights in order to make informed decisions
• effectively communicate data insights and recommendations to a range of stakeholders
• evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society
• critically evaluate the suitability of data and data sources to identify and analyse business
problems
Each member of the group will present their individual findings/observations either through the voice
over power point (VOPP) presentation or via Zoom recording. Students are required to present their
allocated section of the group report individually clearly linking with the context of the project. Each
member needs to demonstrate how they have contributed to their respective group project and present
their findings/observations succinctly together with proper explanation.
Each presentation should not have more than 4 power point slides, including the title slide. Your title
slide should have the topic of your presentation, your name and zID. You should not spend more than 5
- 6 minutes for your presentation.
You need to record your presentation in audio-video format clearly showing your face. Any deviation
from this requirement will attract significant reduction in marks.
6.2 Submission instructions
Audio-Video Presentation clearly showing the student’s face either through voice over power point
(VOPP) presentation or via Zoom should be recorded and submitted individually in the course Moodle
site by Friday, 4 pm, 24th November, 2023 (AEST/AEDT).
The file must not exceed 200MB. If it exceeds this limit; Assignment will not accept your presentation.
In the course Moodle page, your assignment name will be Individual Presentation, where you need to
click to start the process of submitting your presentation.
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day,
including the weekend and public holidays.
6.3 Supporting resources and links
The following link will help you to understand the process how to submit your presentation. The
submission of your presentation should very straightforward. Should you need assistance, you can
consult this link: https://student.unsw.edu.au/how-submit-moodle-assignment-file-upload
The marking Rubrics for Assessment 1 (Tutorial Portfolio) are included in the Assessment Guide.
The Marking rubrics for other assessment items (Assessment 2, 3, and 4) are available in the respective
assessment sections in the Moodle.
T: +61 (2) 8936 2222 | E: enquiries@unswglobal.unsw.edu.au | W: unswglobal.unsw.edu.au/programs-courses/
UNSW Global Pty Limited (ABN 62 086 418 582) is a not-for-profit provider of education services and a wholly owned subsidiary of UNSW Sydney. UNSW Global delivers
English language programs under its CRICOS Provider Code 01020K | UNSW Global delivers Diploma and Foundation Studies programs under UNSW Sydney’s CRICOS
Provider Code 00098G | See UNSW Global CRICOS Course Codes at unswglobal.unsw.edu.au/esos Copyright © 2021 UNSW Global Pty Limited.
August 2023
Diploma in Business
Assessment Guide
* 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.
2. CLO 1 Explain how an organisation uses analytical and statistical tools to gain valuable insights.
3. CLO 2 Apply statistics and data analysis skills to real data sets from a variety of organisations and
domains to generate insights in order to make informed decisions.
4. CLO3 Visualize and analyse data to support arguments that increase comprehension of
information, insights and problem solving.
5. CLO4 Effectively communicate data insights and recommendations to a range of stakeholders.
6. CLO5 Evaluate ethical implications of organisational use of big data and analytics on stakeholders
and society.
7. CLO6 Critically evaluate the suitability of data and data sources to identify and analyse business
problems.
Due Date Weighting Format Length/Duration Submission
Each of these icons are used for the various assessment tasks. Details are provided below.
Turnitin is an originality and plagiarism prevention tool that enables the assessment of submitted
written work for improper citation or misappropriated content. Each Turnitin task is checked against
other students' work, the internet and key resources selected by the course convenor.
An artificial intelligence (AI) writing detection tool is embedded within Turnitin and is able to identify
the extent to which a student’s response has been generated using technology such as Chat GPT, Bard
(Google), Bing and/or others. Whilst this tool will not in itself be used as an indication that a student
has engaged in academic misconduct the tool can be used with other information to investigate the
situation more fully.
UNSW College teaches and assesses in English, except in language courses. Using digital translators
is also not permitted. One of the reasons is because language is not merely words, but also based in
particular contexts. A pure translation of words will not necessarily reflect its context.
The best way to produce English language work is to write in English. The use of generative AI
including translators is likely to appear in a Turnitin report. If your response is not written in English you
cannot assume the marker can read your work to verify that you understood the question being
assessed.
Further, language editing will likely be identified as AI generated writing. This course does not permit
the use of these tools. If, however you inadvertently make use of AI translators such as Google
translate, Bing Microsoft Translate, Grammarly and/or others please ensure that you record it and keep
drafts of your work. You may be required to provide previous drafts of your work if you are asked about
how you developed it.
In this course it is possible to make use of generative AI such as those identified above, in the planning
process only for any submitted assessment work. For example, using the software to generate initial
ideas that may direct your response.
It is essential that students edit the information they gather using the AI to such an extent that only
their own work is submitted. It is recommended (as mentioned previously) that students keep
evidence of this formative work including drafts should there be concerns about the originality of
their response.
Please be advised that in the event there is evidence of use of generative AI (beyond that outlined
above) to form any significant part or all of a submitted response it will be regarded as serious
academic misconduct and subjected to an investigation to determine the appropriate penalty. Please
refer to the Student Handbook for more information.
Additional information about acceptable use of AI can be found at
https://www.student.unsw.edu.au/assessment/ai. Please note that this information is general in nature
and that the specifics of the expectations in this course are identified above.
If you submit your (written) assessment after the due date, you may incur penalties for late submission.
The final examination response however can only be submitted on the day of the examination in
accordance with the information provided beforehand. Refer to the course outline for details. Late
submission of quiz responses is not possible unless prior permission has been obtained by the course
convenor.
You are expected to manage your time to meet assessment requirements including submission by the due
date. If you do however require an extension for the submission of a task, it is important that you contact
the course convenor/your tutor in the first instance to discuss the request as soon as you are aware of why
you need this consideration. Note that an extension will only be granted in certain circumstances.
Special consideration is the process for assessing the impact of short-term events beyond your control
(exceptional circumstances), that may impact your performance in an assessment task. Always seek advice
from the course convenor/your tutor first, before applying for any special consideration. As a guide an
exceptional circumstance generally:
• Prevents you from completing a course requirement
• Keeps you from attending or submitting an assessment
• Significantly affects your assessment performance
You can find more details in the course outline.
As a student at UNSW you are expected to display academic integrity in your work and interactions. Where
a student breaches the UNSW Student Code with respect to academic integrity, the University may take
disciplinary action under the Student Misconduct Procedure. To assure academic integrity, you may be
required to demonstrate reasoning, research and the process of constructing work submitted for
assessment.
To assist you in understanding what academic integrity means, and how to ensure that you do comply with
the UNSW Student Code, it is strongly recommended that you complete the Working with Academic
Integrity module before submitting your first assessment task. It is a free, online self-paced Moodle
module that should take about one hour to complete.
Week 2 -12
15%
Pre-Tutorial and In-class activities
Tutorial class duration
During the Tutorial Class
The purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• visualize and analyse data to support arguments that increase comprehension of information,
insights, and problem solving
• apply statistics and data analysis skills to real data sets from a variety of organisations and domains
to generate insights in order to make informed decisions
• effectively communicate data insights and recommendations to a range of stakeholders
• evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society
• critically evaluate the suitability of data and data sources to identify and analyse business problems
There will be ten (10) sets of pre- tutorial and in-class tutorial activities, each consisting of a variety of short
response questions and application of data analytics concepts. These questions relate to the lecture
content from the previous week(s).
Pre-tutorial and in class activities will be assessed in Weeks 2-6 and 8-12 inclusive in bi-weekly tutorials.
Each week’s pre-tutorial and in-class tutorial activities are worth of ten (10) marks for a total of 100 marks.
Please note that each week has 2 tutorials and each tutorial will have pre-tutorial and in class activities.
Students will be assessed on their completed pre-tutorial task and in-class activities each week during the
tutorial classes relating to preselected questions provided by the course convenor.
Please note that there is no mark awarded only for attendance. You have to be present in class, attempt the
pre- tutorial tasks and the in-class tutorial exercises provided and demonstrate your work.
It is expected that you participate responding through microphone, sharing your computer screen and
whiteboard; or other appropriate means, as determined by the course convenor.
Each bi weekly tutorial classwork is marked out of 5 giving a total raw mark of (5 x 2 x 10) = 100 which is
then scaled to a 15% weighting.
For this assessment task, you will be marked according to the criteria provided below.
4:00pm Friday, 6th October, 2023 (AEST/AEDT)
25%
Writing task based on a project
Maximum word limit 1000 excluding references and R codes
Via Moodle course site through Turnitin
The purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• apply statistics and data analysis skills to real data sets from a variety of organisations and
domains to generate insights in order to make informed decisions
• visualize and analyse data to support arguments that increase comprehension of information,
insights and problem solving
• effectively communicate data insights and recommendations to a range of stakeholders
This assessment task is geared to test your understanding about data visualization through R (software) and
the application of visualization in generating insights. You are required to present your findings, supported
by data visualization in the form of a written report. You are expected to provide references as appropriate.
4.1.1 Description of assessment task
This is an individual assessment. The assessment task focuses on data visualization using a dataset
on start-up companies across different cities in Australia. A start-up is a new company, generally
established by one or more entrepreneurs with an objective of bringing innovation and unique style
of product and services. Leading examples of start-up includes: Facebook, Google, Airbnb, Uber,
DoorDash, and Instagram. You can have a brief conceptual overview on start-up companies here:
https://www.investopedia.com/terms/s/startup.asp
The assessment dataset on start-up companies is available in the Moodle and it consists of a
number of variables. The following variables are included in the dataset and explanation for each
variable is provided below.
R&D = Research and Development expenses
Administration= Administrative expenses
Marketing= Marketing expenses
SeedFunding= The amount of seed funding received by each company. It means the equity
contribution by the private investors in the start-up companies. Generally, seed funding comes
from sources close to founders of start-ups; including friends, and families. This is generally the first
stage of financing of stat-up companies.
City = Different locations where the start-up companies are established
Equipment= Cost of equipment incurred
Website = Cost of developing company website
Office Furnitures and Supplies = Cost of office furniture and supplies
Payroll= Payroll expenses for each year
Professional Consultants= Fees paid to professional consultants
Profit= Profit earned by companies
StartYear = The year in which companies started
IsSuccessful = 1 indicates successful and 0 is for not-successful
Ownership = 1 indicates sole trader, 2 is for partnership and 3 is for large company
Size = 1 indicates small, 2 is for medium and 3 is for large enterprise
You are a junior data analyst working for an Australian market research company - MarketGo. Your
manager has asked you to undertake an exploratory data analysis using R to investigate the pattern
and relationship among different variables in regards to a number of start-up companies and prepare
a report.
You must present your findings, supported by data visualisations, in the form of a written report
(maximum of 1000 words) that should include:
➢ Descriptive statistics of relevant variables in the dataset using the ‘moments’ package
and explain why such statistics are relevant to your analysis. There should be a clear
analysis what these statistics mean in the context of analysing the data that you are
dealing with?
➢ Data sub-setting that you deem necessary to conduct your analysis.
➢ A visual data analysis (including bar plot, histogram, line chart, box plot and bubble plot) to
demonstrate
o attributes between successful and not-successful start-ups;
o trend of variables that you deem important for the success of companies. You
are free to choose these variables and provide justification why these variables
are considered important for your analysis; and,
o outlier analysis using box plot focusing on two different variables along with
clearly explaining the implications of outliers, if any, in your data visualization.
You are free to choose these variables.
➢ You are expected to apply your broader understanding about the operation of start-up
companies and interpret your findings and actionable insights from your visualization
exercise. In order to support your analysis, you are strongly advised to undertake online
research appropriately to review the articles/research papers on start-up business.
4..2 Supporting resources and links
The assessment dataset is available in the Moodle under the section – Assessment 2: Individual Project
Report
4.3 Tips for analysing the data
You may consider the following advice on exploring the dataset:
• It is important to emphasize that there is not only one correct answer to the assignment. There
are number of different dimensions of the data to explore, and some aspects and dimensions
of the data are likely to be more useful than others. Thus, it is important that prior to starting
your assignment, you systematically explore the different variables in the dataset.
• Remember, it is important to highlight the relevant factors responsible for your analysis and it
is critical to place detailed arguments appropriately. This should be the key focus of your
analysis. Just providing commentary on visualization is not enough. You need to relate the
findings of visualization to your analysis in a thorough manner. Always remember, the ability to
relate analytics to the business issue is fundamental. It is not just a technical issue, it is a business
issue.
• To help focus your analysis and insights, think of the general nature of start-up companies and
the potential factors that may contribute to the success of these companies. This can help
provide greater structure for your analysis. You are strongly advised to undertake online
research as to have an overview about the nature of the start-up companies and understand the
attributes which may be important for the success of start-up companies.
• You are expected to do multiple bar plots, line charts, histograms, box plots and bubble plots, for your
assessment as you deem appropriate to better understand the data, and provide an insightful
discussion. These graphs should summarize the relationships that you are reporting on or
analysing. You also need to perform descriptive analysis using the ‘moments’ package and
explain implications in the context of the dataset.
• To ensure the rigour of analysis, apply the frameworks/R codes discussed and practised in
class. This is important as we are not expecting the use of analytical methods beyond the
scope of this course.
• Also look for potential outliers in the dataset using box plot. What can you infer from these
outliers? How these outliers affect your analysis? Should the outliers be included in the
analysis of the data? Any decisions made about including or not including outliers should be
justified in the report.
• Remember that your conclusions should be well supported by the undertaken data exploration
and created visualisations. You should also outline any key assumptions in your data-driven
conclusions and acknowledge limitations.
• A declaration signed by you confirming that it is the original work of you (word count will not apply
for this). This declaration should include the statement with your signature: “I declare that the work
that I have submitted relating to this assessment is completely of my own and I confirm that I have
complied with all requirements of UNSW Academic Honesty and plagiarism policy” .
• You must comply with all applicable requirements of UNSW Academic Honesty and Plagiarism Policy.
Should you need further clarification, consult the Academic Honesty and Plagiarism Policy available
in the course Moodle page.
• You are required to provide appropriate references (done via Harvard in-text reference). This
do not count towards the assessment’s word count. Consult the link for further information
about referencing https://www.student.unsw.edu.au/harvard-referencing
4.4 Submission instruction
Submit a word document with all relevant R code and references in the appendix to Turnitin assessment
submission link on Moodle. You must submit your work by 4:00pm Friday, 6th October, 2023 (AEST/AEDT).
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day, including
the weekend and public holidays.
4:00pm Friday, 17th November, 2023 (AEST/AEDT)
30%
Writing task, based on analysis of big data set
Maximum word count of 2000, excluding references and R code
Via Moodle course site, through Turnitin
The purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• visualize and analyse data to support arguments that increase comprehension of information,
insights, and problem solving
• apply statistics and data analysis skills to real data sets from a variety of organisations and domains
to generate insights in order to make informed decisions
• effectively communicate data insights and recommendations to a range of stakeholders
• evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society
• critically evaluate the suitability of data and data sources to identify and analyse business problems
The group project will help the students to:
• make individual contribution to shape the idea of the group
• learn successfully work in teams and reflect on strategies in achieving group objectives
• design experimentation, undertake data analysis and building predictive models
• apply wide range of perspectives in solving organisational problems for achieving the best possible
solutions including to understand and resolve contextual limitations that an organisation may face in
real-world
• deliver an effective and well justified analytic solution
• communicate key message and develop skills of presentation to a broad group of stakeholders,
including non-technical audience
5.1.1 Selection of group
Students will need to select their own groups. The maximum number of students in each group should be 4.
In order to select the group; students will need to click the Group selection for Assessment 3: Group Project
in the Moodle and join the group that they want. Self-selection of group will offer flexibility and allow students
to choose their own peers with whom they like to work.
The group selection should be completed by week 5.
5.1.2 Team contract
Each group should develop a team contract as per the following format. It must be signed and dated by the
group members. The team contract should be handed over to the course convenor by week 8 via email.
The following is a list of items you may wish to include in your team contract.
We, the members of (group name) agree to the following plan of action regarding our work toward the group
assignment tasks.
5.1.3 Meetings and communication
• Number of weekly online/face to face meetings.
• Person coordinating the meeting for each. Each member will take their turn.
• Who will summarise decisions, when will he/she make them available to all members. Each
member will take their turn.
5.1.4 Work and deadlines
• How will the group come to agreement on a topic (what research are members expected to do
before you meet / go online to discuss the topic)?
• When will you make a final decision on a topic?
• Allocation of tasks among group members including the deadline set.
• Who will collate the draft submissions and then circulate it for the group to comment on?
• Who will prepare and submit the final submission in Turnitin?
5.1.5 Penalties
• What happens if members don’t meet agreed-to deadlines?
• What happens if members do not contribute / come to meetings?
• If any member does not participate as per the team contract, this should be reported to the course
Convenor by the end of Week 10 via email with the evidence.
5.2 Supporting resources and links
The assessment dataset is available in the Moodle under the section – Assessment 3: Group Project
Report.
5.2.1 Project topic, tasks and Report
This is a group assessment. In this assessment, you will continue to explore the assessment dataset,
available in the Moodle. You have joined MarketGo as a member of the data analytics team. You are
now required to work with your team members to conduct predictive analytics using R particularly
focusing on the following:
➢ Defining clear project goals
➢ develop predictive models using regression R codes and splitting the data into train and test
data with a ratio of 80:20;
➢ using ‘leaps’ package, develop best subset regression model to identify relevant variables
having implications in your predictive analytics;
➢ performance of start-up companies based on the selected variables, outlining a comparative
analysis based on three different cities;
➢ using information from the dataset, provide a well-focused analysis as to which factors
contribute to the successful start-up;
➢ based on the above predictive exercise; you will be required to derive actionable insights and
make recommendations for the start-ups to improve their performance. In this regard, you
should use your broader knowledge on the operation of start-up companies and make intuitive
assumptions together with analysis of the dataset given;
➢ You must present your findings, supported by appropriate predictive analytics in the form of a
written report (approx. 2000 words).
5.3 Tips for analysing the data
Here is advice on developing models and recommendations:
• It is important to emphasise that there is not only one correct answer to the assignment. There
are many different models that can be put forward to effectively address your project goals.
Thus, it is important that you clearly identify the analytics methods and set out a systematic,
comprehensive plan in line with your project goals. Always remember, the ability to relate
analytics to the business issue is fundamental. It is not just a technical issue alone, it is a
business issue.
• You need to split the dataset into train and test data with 80:20 ratio together with doing data
subsets in line with your project goals.
• You are expected to use applicable R codes and packages that are discussed in the course to
develop your models.
• To ensure the rigour of the model development and subsequent analysis, apply the
frameworks/R codes discussed and practised in class. We are not expecting the use of
analytical methods beyond the scope of this course.
• Remember that your conclusions should be well supported by the created models. You should
also outline any key assumptions in your data-driven conclusions and acknowledge
limitations.
• In terms of factors responsible for successful operations of start-up companies, you should
use knowledge and insights gained through undertaking online research and making intuitive
assumptions. Remember, business analytics should work like designers, exploring possible
alternatives and through understanding specific business requirements.
• Where appropriate, connect findings or questions from your individual reports to your team
report.
• The report should have the following sections and must be presented as per the sequence below:
✓ Executive summary (150 words). It must provide a solid overview of your project so that the reader
should have a clear understanding of your report without going into main report.
✓ Introduction outlining the rationale for using the big data in predictive analysis in management
decision making. In this section you must relate this with managing the operations of start-up
business. (150 words).
✓ Project goals: key questions to be addressed in the project. What you would like to have the major
focus of your project and how these goals could be related with the dataset given to you? (50
words).
✓ An outline of ‘design and agile thinking concept’ in developing the project. You should explain how
you have applied this concept in developing your project goals (150 words). Please note just
explaining the design and agile thinking concept alone will not attract any marks. This section
must highlight how your group have practically approached and used this concept in developing
your project showing specific examples.
✓ Methods used to analyse and interpret the data. You need to focus on predictive analytics to
derive actionable insights that can be used to assist with business decisions in the context of
your project goals. You need to use R in this regard; together with justification why you are using
this particular software together with selected predictive analytics methods (100 words).
✓ Findings of the project. This should include actionable insights from the data in the context of
project goals together with your research findings. Findings should be presented in a logical
manner outlining key message(s) to a range of stakeholders, primarily targeted to the senior
management of MarketGo. These should be presented under headings and sub-headings. You
need to clearly put arguments in favour of your findings demonstrating your conceptual
understanding and application in the context of real-life business scenario. You should also
outline any intuitive assumptions that you may have made while working on this project. In this
section, you may outline about the limitations of the dataset, if any (900 words).
✓ Recommended actions and conclusion in line with your findings including the strategies for
improved operations of start-up companies (400 words).
✓ Your group reflection as to what you have learnt from the DPBS 1190 and how such learning has
helped you in undertaking this project (100 words).
✓ R codes used to analyse and interpret the data should be given in the appendix (word count will
not apply).
✓ Table demonstrating each member’s participation in their respective allocated work based on the
team contract (word count will not apply).
✓ A declaration signed by all members of the group confirming that it is the original work of group
members (word count will not apply for this). This declaration should include the statement with
signature from all members of the group: “We declare that the work we have submitted relating to
this assessment task is completely of our own and we confirm that we have complied with all
requirements of UNSW Academic Honesty and plagiarism policy”.
✓ You must comply with all applicable requirements of UNSW Academic Honesty and Plagiarism
Policy. Should you need further clarification, consult the Academic Honesty and Plagiarism Policy
available in the course Moodle page.
✓ You are required to provide appropriate references (done via Harvard in-text reference). This do
not count towards the assessment’s word count. Consult the link for further information about
referencing https://www.student.unsw.edu.au/harvard-referencing
✓ Your report should demonstrate a thorough analysis of relevant data set in the context of your
project goals using knowledge gained in the course. The project report should be written clearly
and concisely within a 2000-word limit (excluding references, appendices and R code) for the
understanding of non-technical audience.
5.4 Submission instructions
Submit a word document with all relevant R codes and references to the Turnitin assessment submission
link on Moodle. You must submit your work by 4 pm on Friday 17th November, 2023 (AEST/AEDT).
One member from each group should submit this assessment on behalf of their respective groups.
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day, including
the weekend and public holidays.
5.5 Supporting resources and links
You should get guidance on group work through visiting https://student.unsw.edu.au/groupwork
4:00 pm Friday, 24th November, 2023 (AEST/AEDT)
30%
Individual presentation in audio-video format clearly showing the face either by voice over
power point presentation (VOPP) or recording via Zoom
Maximum 4 Power point slides, including the title slide
Via Moodle course site, through Assignment
This is an individual assessment task.
This purpose of this assessment task is to assess the following learning outcomes:
• explain how an organisation uses analytical and statistical tools to gain valuable insights
• visualize and analyse data to support arguments that increase comprehension of information,
insights, and problem solving
• apply statistics and data analysis skills to real data sets from a variety of organisations and
domains to generate insights in order to make informed decisions
• effectively communicate data insights and recommendations to a range of stakeholders
• evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society
• critically evaluate the suitability of data and data sources to identify and analyse business
problems
Each member of the group will present their individual findings/observations either through the voice
over power point (VOPP) presentation or via Zoom recording. Students are required to present their
allocated section of the group report individually clearly linking with the context of the project. Each
member needs to demonstrate how they have contributed to their respective group project and present
their findings/observations succinctly together with proper explanation.
Each presentation should not have more than 4 power point slides, including the title slide. Your title
slide should have the topic of your presentation, your name and zID. You should not spend more than 5
- 6 minutes for your presentation.
You need to record your presentation in audio-video format clearly showing your face. Any deviation
from this requirement will attract significant reduction in marks.
6.2 Submission instructions
Audio-Video Presentation clearly showing the student’s face either through voice over power point
(VOPP) presentation or via Zoom should be recorded and submitted individually in the course Moodle
site by Friday, 4 pm, 24th November, 2023 (AEST/AEDT).
The file must not exceed 200MB. If it exceeds this limit; Assignment will not accept your presentation.
In the course Moodle page, your assignment name will be Individual Presentation, where you need to
click to start the process of submitting your presentation.
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day,
including the weekend and public holidays.
6.3 Supporting resources and links
The following link will help you to understand the process how to submit your presentation. The
submission of your presentation should very straightforward. Should you need assistance, you can
consult this link: https://student.unsw.edu.au/how-submit-moodle-assignment-file-upload
The marking Rubrics for Assessment 1 (Tutorial Portfolio) are included in the Assessment Guide.
The Marking rubrics for other assessment items (Assessment 2, 3, and 4) are available in the respective
assessment sections in the Moodle.
