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January2025
Diploma in Business
DPBS 1190-MGT1390
DATA, INSIGHTS AND
DECISIONS
Assessment Guide
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ASSESSMENT SUMMARY
Assessment task Weighting Due Date* Learning
Outcomes
Assessment 1 : Tutorial Portfolio
15% Weekly: during the Tutorial Class from
Week 2 to 12
CLO 1, 2, 3, 4, 5, 6
Assessment 2 :
Individual Project Report
Written report, 1000 words
25% 4:00 pm Friday, 28th February 2025
(AEST/AEDT)
CLO 1, 2, 3 ,4,6
Assessment 3 : Group Project Report
Written report, 2000 words
30% 4:00 pm Friday, 11th April 2025
(AEST/AEDT)
CLO 1,2 ,3, 4 ,5, 6,
Assessment 4 : Individual Presentation: audio- video presentation.
Each member of the group individually will
present their findings/observations clearly
relating with the context of the project. The
presentation must include both audio and video
clearly showing the face of the student.
30% 4:00 pm Friday, 18th April 2025
(AEST/AEDT)
CLO 1, 2, 3 ,4, 5, 6
* Due dates are set at Australian Eastern Standard/Daylight Time (AEST/AEDT
1.1 COURSE LEARNING OUTCOMES (CLO)
CLO 1 Explain how an organisation uses analytical and statistical tools to gain valuable insights.
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.
CLO3 Visualize and analyse data to support arguments that increase comprehension of information,
insights and problem solving.
CLO4 Effectively communicate data insights and recommendations to a range of stakeholders.
CLO5 Evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society.
CLO6 Critically evaluate the suitability of data and data sources to identify and analyse business
problems.
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ASSESSMENT DETAILS
Due Date Weighting Format Length/Duration Submission
Each of these icons are used for the various assessment tasks. Details are provided below.
TURNITIN
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 to understand and clarify concepts; however, your
answers must not have AI generated answers.
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. Remember, Turnitin will generate the degree of
similarity and AI generated answers.
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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.
LATE SUBMISSION
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.
EXTENSION
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
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:
• 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.
ACADEMIC INTEGRITY
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.
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ASSESSMENT 1: TUTORIAL PORTFOLIO
Week 2 -12
15%
Pre-Tutorial and In-class activities
Tutorial class duration
During the Tutorial Class
3.1 DESCRIPTION OF THE ASSESSMENT TASK
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 need to be present in class, attempt
the pre-tutorial tasks and the in-class tutorial exercises provided and demonstrate your work. Your
tutorial portfolio marks will be awarded based on your level of engagement/ participation in the class.
It is expected that you participate responding through answering questions, sharing your computer
screen and whiteboard; or other appropriate means, as determined by the course convenor.
Each biweekly 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.
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For this assessment task, you will be marked according to the criteria provided below.
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ASSESSMENT 2: INDIVIDUAL PROJECT REPORT
4:00 pm Friday, 28th February 2025 (AEST/AEDT)
25%
Writing task based on a project
Maximum word limit 1000 excluding references and R codes (maximum of 10% variation is acceptable)
Via Moodle course site through Turnitin
4.1 ASSESSMENT OVERVIEW
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 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.
• critically evaluate the suitability of data and data sources to identify and analyse business
problems.
This assessment task is geared to:
• examine your conceptual understanding how visualization and descriptive statistics can be used in
improving business decisions; and
• test your understanding about data visualization and descriptive statistics through R (software) and
the application of visualization in generating insights.
4.1.1 Assessment tasks and focus
This assessment task focuses on data visualization and analysis using a dataset on food delivery.
The following variables are included in the dataset and explanation for each variable is provided below:
• hour_of_day (0-23): Hour when order was placed
• is_weekend (0/1): Whether order was placed on weekend, if the order is placed on weekend is 1,
otherwise 0
• is_rush_hour (0/1): Whether order was during rush hour, if the order is placed in rush hour is 1,
otherwise 0
• rain_intensity: Amount of rainfall during delivery
• temperature: Temperature in Celsius
• order_value: Cost of the order
• items_count: Number of items ordered
• distance_km: Delivery distance in kilometers
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• restaurant_load: Restaurant capacity utilization (%)
• driver_experience: Driver's experience in months
• delay_minutes: Delivery delay in minutes
• is_cancelled: Whether order was cancelled, if order is cancelled is 1, otherwise 0
As a Junior Data Analyst at Doorstep Food, your task is to develop strategies to reduce order cancellations
and delivery delays to boost business performance and customer satisfaction. Your manager has requested
insights on these strategies, including how cancellations and delays affect customer satisfaction.
Your role involves using R for exploratory data analysis to identify patterns and relationships that influence
order cancellations and delivery delays. The primary goal is to enhance business operations, improve
customer satisfaction, and position the company as a market leader in food delivery.
You will prepare a detailed report (maximum 1,000 words) to guide management decisions. The report should
include:
1. Goal: Define the goal of your exploratory data analysis. Apply your broader understanding of factors
influencing delivery delays and cancellations from online research to generate insights in your
analysis. No specific number of articles is required for your research.
2. Descriptive Statistics: Present descriptive statistics of relevant variables using the 'moments'
package, focusing on tools covered in the course. Discuss patterns in cancellations and delivery
delays, their acceptability, and what they reveal about service quality.
3. Visual Data Analysis: Use bar plots and line charts to show relationships among variables. Identify
patterns in delivery delays and cancellations, peak cancellation hours, and trends between distance
and delivery rate, and in cancellation rates.
4. Outlier Impact: Analyse outliers using box plots with relevant variables in line with your project goal,
and identifying the exact number of outliers with appropriate R code.
5. Findings and Insights: Interpret your findings and provide actionable insights from your visualizations
and descriptive statistics geared to reducing the delivery delays and cancellations and improve
customer satisfaction.
6. Appendix: You must include the R codes that you have used for your analysis.
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4.2 SUPPORTING RESOURCES AND LINKS
The assessment dataset is provided in the Moodle
4.3
TIPS FOR ANALYSIG THE DATA
You may consider the following advice on exploring the dataset:
1. 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, that you
systematically explore the different variables in the dataset.
2. 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, and
descriptive statistical analysis in a thorough manner in terms of factors responsible for delays in food
delivery and cancellation of orders. 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.
3.
To help focus your analysis and insights, think of potential factors that could drive delivery
cancellations and delays; and how these could impact on customer satisfaction? This can help provide
greater structure for your analysis.
4. Highlight the relevant factors responsible for your analysis and place detailed arguments
appropriately. Relate the findings of visualization and descriptive statistical tools to your analysis.
5. Although you may create many graphs for your assessment as you deem appropriate to better
understand the data and you only want to include figures that support your main findings. These graphs
should summarize the relationships that you are reporting on or analysing. You are expected to do
appropriate number of bar plot, and line chart, to support your analysis. And
6. Also look for potential outliers in the dataset through box plot. What can you infer from these outliers?
Should the outliers be included in your analysis of the data? Any decisions made about including or not
including outliers should be justified in the report.
7. Remember that your conclusions should be well supported by the undertaken data exploration,
descriptive statistics, and created visualisations. You should also outline any key assumptions in your
data-driven conclusions and acknowledge limitations.
8. To ensure the rigour of your visualization and subsequent analysis, apply the frameworks and R codes
discussed in class. We are not expecting the use of analytical methods beyond the scope of this course.
9. Academic integrity must be maintained. Please note your answer and submission must be your
original work. Your report must not have any AI generated answer. Any deviation from this requirement
will attract heavy penalty and among others, can lead to failing the course. Remember, Turnitin can
generate the degree of similarity and AI generated answers.
10. You must sign an academic integrity declaration confirming that it is your original work (word count
will not apply for this). This declaration should be in the cover page of your report and include the
statement with your signature:
“I declare that the work I have submitted relating to this assessment task is completely of my own
and I confirm that I have complied with all requirements of UNSW Academic Honesty and plagiarism
policy”.
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10. 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 STRUCTURE OF REPORT
You are required to submit your report using the following format:
• The cover page includes the title of your report, your name and zID; and academic integrity
declaration.
• The body of your report will include:
o Setting Goal. This section must have a clear statement about your goal of your project.
o Descriptive Statistical analysis. This section you should focus on the descriptive statistical
tools, covered in the course to provide a comprehensive analysis in line with your goal.
o Visual data analysis. In this section, you must provide a well-grounded analysis in line with
your goal using visualization tools, such as, bar plot, and line chart.
o Outlier analysis. In this section, you are required to look for potential outliers in the dataset
through box plot. You are expected to clearly articulate what you can infer from the
outliers, and how the outliers have impacted your analysis
o Interpretation of findings and actionable insights. This section should clearly outline key
messages from your analysis integrating your analysis based on descriptive statistics and
visualization.
• Reference. This section should include references.
• Appendix: This section must include all R codes used in the report. The appendix should be
the last section of your report.
• You should adhere to the word limit of 1000 words. A 10% variation will be acceptable.
However, your cover page, reference and appendix section will not be included in the word
count.
4.5 SUBMISSION INSTRUCTION
Submit a word document of your report and include all R codes used for this assessment in the appendix
and references at the end of your report. You submit your report via the Turnitin assessment submission
link on Moodle. Your submission must Include your name, zID, and the word count. The appendix must
have all relevant R code.
You must submit your work by 4:00pm on 28th February 2025 (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.
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5 ASSESSMENT 3: GROUP PROJECT REPORT
4:00 pm Friday, 11 th April 2025 (AEST/AEDT)
30%
Writing task, based on analysis of big data set
Maximum word count of 2000, excluding references and R codes (maximum of 10% variation is acceptable)
Via Moodle course site, through Turnitin
5.1 ASSESSMENT OVERVIEW
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.
• analyse data to support arguments that increase comprehension of information, insights, and
problem solving by using predictive modelling.
• apply statistics and data analysis skills to real data sets from a variety of organisations and domains
to generate insights 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 using data visualisation, 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, and;
• communicate key message and develop skills of presentation to a broad group of stakeholders,
including non-technical audience.
5.2 SELECTION OF GROUP
Students will need to select their own groups. The maximum number of students in each group should
be 4. To select their groups; students will need to click the link available in the Moodle under the Section
Assessment 3: Group Project Report. This link for group selection will be available for students in week
3. 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 latest by the week 6 of the term. Please note
the group selection is not limited to any tutorial group. You can select group members from the
DPBS1190 class, irrespective of any tutorial group.
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5.3 TEAM CONTRACT
Each group must develop a team contract.
It must be signed and dated by the group members. The team
contract should be handed over to the course convenor via email by the beginning of week 8.
The following information as per the below format should be included in the Team contract.
We, the members of (group name) agree to the following plan of action regarding our work toward the
group assignment tasks. (The following is a list of items you may wish to include in your contract).
5.3.1 MEETINGS AND COMMUNICATION
• Number of weekly 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.
5.3.2 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?
5.3.4 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 latest by the end of Week 10 via email with the evidence.
5.4 ASSESSMENT TASK AND FOCUS
In this assessment, you will continue to work with the same dataset used in your individual project.
As a member of the data analytics team at Doorstep Food, your task is to conduct predictive
analytics using R on the company's food delivery data. This group project builds on the analysis
from your individual assessment, with the goal of developing insights that will help improve the
business operations of Doorstep Food leading to improved customer satisfaction.
1. Define a clear Project Goal
• Clearly articulate the objective of your project
2. Design and Agile Thinking Approach
• Apply 5 steps of design and agile thinking approach
• Cite examples how you have used these steps in developing your project
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3. Develop and evaluate the following two Predictive Models
a) Delay Prediction Model
b) Cancellation Prediction Model
In predictive Models, focus on the following:
Use linear regression with best subset selection
Interpret the significant variables
Compare training and test MSE
Compare logistic regression and decision tree models
Analyse confusion matrices for both models
• Which model performs better and why?
4. Actionable Insights and Recommendations
• Derive actionable insights from your predictive analysis
• What factors most strongly influence delays and cancellations?
• provide three specific recommendations to:
• reduce cancellation rates
• improve on-time delivery performance
• optimize delivery operations
• business recommendations based on the model results
5. Ethical Issues and Group Reflection
• Do you see any potential ethical issues in managing the data in your project and how you
address these issues?
• How your learning from this course has helped you in dealing with this project?
Your findings should be presented in a well-structured written report of 2,000 words, supported by
appropriate predictive analytics. However, 10% variation of this word limit is accepted.
5.5 Supporting resources and links
The assessment dataset is provided in the Moodle.
5.6 Tips for analysing the data
You should consider the following advice regarding this assessment task:
1.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; it is a business issue.
2.To ensure the rigour of the model development and subsequent analysis, apply the frameworks
discussed in class. We are not expecting the use of analytical methods beyond the scope of this course.
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3. Remember that your conclusions should be well supported by your created models and analysis. You
should also outline any key assumptions in your data-driven conclusions and acknowledge limitations.
4. In terms of factors responsible for improving customer satisfaction by reducing delivery delays and
cancellations, you should use knowledge and insights gained through undertaking online research
and making intuitive assumptions.
There is no limit set how many articles you should include in
your online research.
Remember, business analysts should work like designers, exploring
possible alternatives through understanding specific business requirements.
5. Where appropriate, connect findings from your individual report to your group report.
6. The report must follow the following format sequence:
The cover page of the report must include your project title, your name, zID, and the following
academic integrity statement signed by all members of the group. 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). The cover page will not be included in the word count.
“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”.
The body of the report should include the following:
• Executive summary (150 words). Executive summary must provide a good overview of your project
so that the reader should have a clear understanding of your report without going into main report.
• Introduction (150 words) outlining the rationale for using big data in predictive analysis in
management decision making with a particular reference to increasing customer satisfaction by
reducing delays and cancellation in food delivery.
• Project goals (50 words). Key questions to be addressed in the project. What would you like to have
the major focus of your project?
• Design and agile thinking approach (150 words). An outline of ‘design and agile thinking’
concept in developing your project. You should explain precisely how you have applied this
concept in your project. 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.
• Analysis of data (850 words). This section will include, among others:
Developing and analysing predictive models, as outlined in the Section 5.4 of this
Assessment guide. It is expected that your analysis should be detailed and
comprehensively cover all aspects of predictive models in line with your project goal.
Highlighting your findings presented in a logical manner 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.
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• Actionable insights and recommendation (400 words) Actionable insights from your analysis and
recommendations in the context of project goals and outlining key message(s) to a range of
stakeholders, primarily targeted to the senior management of Doorstep Food, including non-technical
audiences how to improve the customer satisfaction by reducing order cancellations and delivery
delays.
• Ethical issues (150 words). This section should highlight potential ethical issues in the project and
how to address these issues.
• Your group reflection (100 words) as to what you have learnt from the DPBS1190 - BMGT1390 and
how such learning has helped you in undertaking this project.
• Reference. This section should include all references that you have used (word count will not apply).
• The Appendix should include R codes used to analyse and interpret the data. The appendix should
be at the end of your report (word count will not apply). It is expected that you will use R codes
discussed in the class and integrate your analysis using these codes.
• Team Contribution: You should provide each member’s participation in their respective allocated
work based on the team contract (word count will not apply). The format for the team contribution is
available in the Moodle under the Section – Assessment 3: Group Project Report.
Please note:
o Academic integrity must be maintained. Please note your answer and submission must be
your original work. Your report must not have any AI generated answer. Any deviation from
this requirement will attract heavy penalty and among others, can lead to failing the course.
Remember, Turnitin can generate the degree of similarity and AI generated answers.
o 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 in line with your project goals using
knowledge gained in the course. The project report should be written clearly and concisely within a 2000- word limit (excluding cover page, references, tables, appendices, and R code) for the understanding of
non-technical audience. A 10% variation in the word count will be acceptable.
5.7 SUBMISSION INSTRUCTIONS
Submit a word document of your report and include all R codes used for this assessment in the appendix
and references at the end of your report. You submit your report via the Turnitin assessment submission
link on Moodle.
You must submit your work by 4 pm on Friday 11th April 2025 (AEST/AEDT).
Your submission must Include your group number including members name, their zID, and the word
count.
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.
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5.8 SUPPORTING RESOURCES AND LINKS
You should get guidance on group work through visiting https://student.unsw.edu.au/groupwork
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6 ASSESSMENT 4: INDIVIDUAL PRESENTATION
4:00 pm Friday, 18th April 2025 (AEST/AEDT)
30%
Individual presentation recording through voice over power point presentation (VOPP) or recording via Zoom. The presentation must include both audio and video recording clearly
showing your face.
Maximum 4 Power point slides, including the title slide
Via Moodle course site, through Assignment
6.1 DESCRIPTION OF ASSESSMENT TASK
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.
• 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. Before starting your presentation, you
must clearly display your UNSW Student Card.
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6.2 SUBMISSION INSTRUCTIONS
Audio and video presentation either through voice over power point (VOPP) presentation or via Zoom
should be submitted individually in course Moodle site via Assignment tool embedded in the course
Moodle site. The file must not exceed 200MB. If it exceeds this limit; Assignment tool 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.
You must submit your individual presentation by Friday, by 4 pm, 18th April 2025 (AEST/AEDT).
6.3 SUPPORTING RESOURCES AND LINKS
The following link will help you to understand the process how to submit your presentation through
Assignment https://student.unsw.edu.au/how-submit-moodle-assignment-file-upload
7 ASSESSMENT MARKING RUBRICS
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.
[END OF ASSESSMENT GUIDE]
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