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程序代写案例-DPBS1190
时间：2022-08-03
DPBS1190
Data, Insights and Decisions
Diploma in Business
May 2022
* 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.
1. CLO 1 Explain how an organisation uses analytical and statistical tools to gain valuable insights.
2. 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.
3. CLO3 Visualize and analyse data to support arguments that increase comprehension of information,
insights and problem solving.
4. CLO4 Effectively communicate data insights and recommendations to a range of stakeholders.
5. CLO5 Evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society.
6. CLO6 Critically evaluate the suitability of data and data sources to identify and analyse business
problems.
Due Date Weighting Format Length/Duration Submission
Turnitin is an originality checking and plagiarism prevention tool that enables checking of submitted written
work for improper citation or misappropriated content. Each Turnitin assignment is checked against other
students' work, the Internet and key resources selected by your Course Coordinator.
If you are instructed to submit your assessment via Turnitin, you will find the link to the Turnitin submission in
your Moodle course site. You can find out more information in the Turnitin information site for students.
If you submit your assessment after the due date, you may incur penalties for late submission. Ask your Course
Coordinator or tutor on what these penalties may be or check your course outline. You can read more in the
UNSW Assessment Implementation Procedure.
You are expected to manage your time to meet assessment due dates. If you do require an extension to your
assessment, it is very important that you ask your Course Coordinator or tutor first and request your extension
as early as possible before the due date.
Special consideration is the process for assessing the impact of short-term events beyond your control
(exceptional circumstances), on your performance in a specific assessment task. Always seek advice from your
Course Coordinator or tutor first, before applying for any special consideration.
These are exceptional circumstances or situations that may:
• Prevent you from completing a course requirement,
• Keep you from attending an assessment,
• Stop you from submitting an assessment,
• Significantly affect your assessment performance.
Available here is a list of circumstances that may be beyond your control. This is only a list of examples, and
your exact circumstances may not be listed.
You can find more detail and the application form on the Special Consideration site, or in the UNSW Special
Consideration Application and Assessment Information for Students.
Week 2 -12
10%
Pre-Tutorial and In-class tutorial 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. Marks will be awarded based on your level
of participation and engagement during the class. 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 and engage during the class responding to questions/discussions through microphone, sharing
your computer screen and whiteboard, working through the shared document 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 10% weighting.
For this assessment task, you will be marked according to the criteria provided below.
Week 6: 4:00pm Friday, 17th June, 2022 (AEST/AEDT)
30%
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:
• examine your conceptual understanding how visualization can be used in improving business
decisions; and
• test your understanding about data visualization through R (software) and the application of
visualization in generating insights.
This 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 dataset on start-up companies is available on 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
Payroll= Payroll expenses for each year
Office Furniture and Supplies= Cost of office furniture and supplies
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
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 various 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 ‘moment’ 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, and bubble plot) to demonstrate
o attributes between successful and not-successful start-ups;
o trend of three 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 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 gain this
understanding, you may undertake online research appropriately to review the articles/research
papers on start-up business.
The dataset is provided on Moodle called “Startups”
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, 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 to your analysis
in a thorough manner in terms of explaining the variables that you have chosen. 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 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.
4. Although you may create many graphs for your assessment as you deem appropriate to better understand
the data, however, 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 appropriately
multiple number of barplot, histogram, bubble plot and line chart to support your analysis. You also need to
perform descriptive analysis using the ‘moments’ package and explain the implications.
5. To ensure the rigour of 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.
6. Also look for potential outliers in the dataset. 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.
7. 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.
8. 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
Submit a word document with all relevant R code and references in the appendix to Turnitin assessment
submission link on Moodle. The R codes will not be included in the word count. Not including R codes will
result in substantial reduction of marks. our submission include your name, zID, and the word count. The
appendix must have all relevant R code. You must submit your work by 4:00pm Friday, 17th June, 2022
(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.
Week 12: 4:00pm Friday, Friday, 29th July, 2022 (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 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
• communicate key message and develop skills of presentation to a broad group of stakeholders,
including non-technical audience
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
and join the group that they want. This link is available in the course moodle site in the Section Assessment
3: Group Project Report under the heading of Group self-selection. 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 5 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.
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 6 via email.
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).
• Number of weekly online 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.
• 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?
• What happens if members don’t meet agreed-to deadlines?
• What happens if members do not contribute / come to meetings?
In this assessment, you will continue to explore data related to the individual assessment. 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 focusing on the following:
➢ performance analysis of start-up companies based on the selected variables;
➢ using ‘leaps’ package develop best subset regression model to identify relevant variables having
implications in your predictive analytics;
➢ using information from the dataset, predict whether a start-up is going to be successful or not-
successful;
➢ based on the above predictive exercise; you will be required to derive actionable insights and make
recommendations for the start-ups to improve its 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 relevant data from the dataset given.
You must present your findings, supported by appropriate predictive analytics in the form of a written report
(approx. 2000 words).
The dataset is provided on Moodle as “Startups”.
Here is advice on developing models and recommendations:
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/R codes
discussed and practised in class. We are not expecting the use of analytical methods beyond the scope of
this course.
3. 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.
4. 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.
5. Where appropriate, connect findings or questions from your individual reports to your team report.
6. The report should have the following sections:
➢ 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 start-up business. (250 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).
➢ You should develop FIVE models using different sets of variables for analysing the performance of
start-up companies and provide justification which is the best model. This justification should be
included in the section outlining the findings of the project.
➢ You should split the data into a training dataset and a testing dataset with a split of approximately
80/20 and explain why you need to split the data into a training data set and test data set. This should
be mentioned in your analysis while you will focus on findings of your project.
➢ 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,
including non-technical audiences. 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 operation (250 words).
➢ Any ethical implications, if you find relevant in dealing with this dataset (50 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). 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
6. Your report should demonstrate a thorough analysis of relevant data set in the context of your project
goals using knowledge gained in the course. Among others, your report should highlight the predictive
analysis using R. 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.
Submit a word document with all relevant codes in the appendix and references to the Turnitin assessment
submission link on Moodle. You must submit your work by 4 pm on Friday 29th July, 2022 (AEST/AEDT). Your
submission must include a cover page, with group members names, their zID and the word count.
The appendix must have all relevant R code. Not including appropriate R codes will result in substantial
reduction of marks.
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day, including
the weekend and public holidays.
You should get guidance on group work through visiting https://student.unsw.edu.au/groupwork
Study-break Week: 4:00 pm Friday, 5th August, 2022 (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.
Maximum 4 Power point slides, including the title slide
Via Moodle course site, through Assignment
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 ensuring the presentation includes both audio and
video recording. Students are required to present their allocated section (as per the table submitted in their
group report outlining their respective allocated work) of the group report individually. Having said that in
order to clearly communicating the message, you need to relate your allocated tasks with the goals of the
project and with other parts of the group project to establish the context. Failure to do this will lead to
significant reduction in marks. This will call for your presentation to the different parts of the project to
establish the appropriate context of your presentation. Each member needs to demonstrate how they have
contributed to their respective group project and present their findings/observations succinctly together with
proper explanation relating them to the project context.
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.
Please note submission of power points only without your voice recording and video is not acceptable.
Therefore, you recording should have both audio and video. A step by step guidelines for recording voice
over power points will be available in your course moodle page in the Section Assessment 4: Individual
Presentation.
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, 5th August, 2022 (AEST/AEDT).
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
DPBS 1190 – Assessment Rubrics
Data, Insights and Decisions
Diploma in Business
May 2022
* 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.
1. CLO 1 Explain how an organisation uses analytical and statistical tools to gain valuable insights.
2. 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.
3. CLO3 Visualize and analyse data to support arguments that increase comprehension of information,
insights and problem solving.
4. CLO4 Effectively communicate data insights and recommendations to a range of stakeholders.
5. CLO5 Evaluate ethical implications of organisational use of big data and analytics on stakeholders and
society.
6. CLO6 Critically evaluate the suitability of data and data sources to identify and analyse business
problems.
Due Date Weighting Format Length/Duration Submission
Turnitin is an originality checking and plagiarism prevention tool that enables checking of submitted written
work for improper citation or misappropriated content. Each Turnitin assignment is checked against other
students' work, the Internet and key resources selected by your Course Coordinator.
If you are instructed to submit your assessment via Turnitin, you will find the link to the Turnitin submission in
your Moodle course site. You can find out more information in the Turnitin information site for students.
If you submit your assessment after the due date, you may incur penalties for late submission. Ask your Course
Coordinator or tutor on what these penalties may be or check your course outline. You can read more in the
UNSW Assessment Implementation Procedure.
You are expected to manage your time to meet assessment due dates. If you do require an extension to your
assessment, it is very important that you ask your Course Coordinator or tutor first and request your extension
as early as possible before the due date.
Special consideration is the process for assessing the impact of short-term events beyond your control
(exceptional circumstances), on your performance in a specific assessment task. Always seek advice from your
Course Coordinator or tutor first, before applying for any special consideration.
These are exceptional circumstances or situations that may:
• Prevent you from completing a course requirement,
• Keep you from attending an assessment,
• Stop you from submitting an assessment,
• Significantly affect your assessment performance.
Available here is a list of circumstances that may be beyond your control. This is only a list of examples, and
your exact circumstances may not be listed.
You can find more detail and the application form on the Special Consideration site, or in the UNSW Special
Consideration Application and Assessment Information for Students.
Week 2 -12
10%
Pre-Tutorial and In-class tutorial 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. Marks will be awarded based on your level
of participation and engagement during the class. 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 and engage during the class responding to questions/discussions through microphone, sharing
your computer screen and whiteboard, working through the shared document 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 10% weighting.
For this assessment task, you will be marked according to the criteria provided below.
Week 6: 4:00pm Friday, 17th June, 2022 (AEST/AEDT)
30%
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:
• examine your conceptual understanding how visualization can be used in improving business
decisions; and
• test your understanding about data visualization through R (software) and the application of
visualization in generating insights.
This 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 dataset on start-up companies is available on 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
Payroll= Payroll expenses for each year
Office Furniture and Supplies= Cost of office furniture and supplies
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
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 various 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 ‘moment’ 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, and bubble plot) to demonstrate
o attributes between successful and not-successful start-ups;
o trend of three 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 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 gain this
understanding, you may undertake online research appropriately to review the articles/research
papers on start-up business.
The dataset is provided on Moodle called “Startups”
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, 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 to your analysis
in a thorough manner in terms of explaining the variables that you have chosen. 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 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.
4. Although you may create many graphs for your assessment as you deem appropriate to better understand
the data, however, 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 appropriately
multiple number of barplot, histogram, bubble plot and line chart to support your analysis. You also need to
perform descriptive analysis using the ‘moments’ package and explain the implications.
5. To ensure the rigour of 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.
6. Also look for potential outliers in the dataset. 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.
7. 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.
8. 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
Submit a word document with all relevant R code and references in the appendix to Turnitin assessment
submission link on Moodle. The R codes will not be included in the word count. Not including R codes will
result in substantial reduction of marks. our submission include your name, zID, and the word count. The
appendix must have all relevant R code. You must submit your work by 4:00pm Friday, 17th June, 2022
(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.
Week 12: 4:00pm Friday, Friday, 29th July, 2022 (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 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
• communicate key message and develop skills of presentation to a broad group of stakeholders,
including non-technical audience
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
and join the group that they want. This link is available in the course moodle site in the Section Assessment
3: Group Project Report under the heading of Group self-selection. 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 5 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.
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 6 via email.
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).
• Number of weekly online 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.
• 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?
• What happens if members don’t meet agreed-to deadlines?
• What happens if members do not contribute / come to meetings?
In this assessment, you will continue to explore data related to the individual assessment. 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 focusing on the following:
➢ performance analysis of start-up companies based on the selected variables;
➢ using ‘leaps’ package develop best subset regression model to identify relevant variables having
implications in your predictive analytics;
➢ using information from the dataset, predict whether a start-up is going to be successful or not-
successful;
➢ based on the above predictive exercise; you will be required to derive actionable insights and make
recommendations for the start-ups to improve its 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 relevant data from the dataset given.
You must present your findings, supported by appropriate predictive analytics in the form of a written report
(approx. 2000 words).
The dataset is provided on Moodle as “Startups”.
Here is advice on developing models and recommendations:
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/R codes
discussed and practised in class. We are not expecting the use of analytical methods beyond the scope of
this course.
3. 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.
4. 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.
5. Where appropriate, connect findings or questions from your individual reports to your team report.
6. The report should have the following sections:
➢ 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 start-up business. (250 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).
➢ You should develop FIVE models using different sets of variables for analysing the performance of
start-up companies and provide justification which is the best model. This justification should be
included in the section outlining the findings of the project.
➢ You should split the data into a training dataset and a testing dataset with a split of approximately
80/20 and explain why you need to split the data into a training data set and test data set. This should
be mentioned in your analysis while you will focus on findings of your project.
➢ 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,
including non-technical audiences. 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 operation (250 words).
➢ Any ethical implications, if you find relevant in dealing with this dataset (50 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). 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
6. Your report should demonstrate a thorough analysis of relevant data set in the context of your project
goals using knowledge gained in the course. Among others, your report should highlight the predictive
analysis using R. 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.
Submit a word document with all relevant codes in the appendix and references to the Turnitin assessment
submission link on Moodle. You must submit your work by 4 pm on Friday 29th July, 2022 (AEST/AEDT). Your
submission must include a cover page, with group members names, their zID and the word count.
The appendix must have all relevant R code. Not including appropriate R codes will result in substantial
reduction of marks.
Assignments that are submitted late (without approval) will be penalised at a rate of 10% per day, including
the weekend and public holidays.
You should get guidance on group work through visiting https://student.unsw.edu.au/groupwork
Study-break Week: 4:00 pm Friday, 5th August, 2022 (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.
Maximum 4 Power point slides, including the title slide
Via Moodle course site, through Assignment
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 ensuring the presentation includes both audio and
video recording. Students are required to present their allocated section (as per the table submitted in their
group report outlining their respective allocated work) of the group report individually. Having said that in
order to clearly communicating the message, you need to relate your allocated tasks with the goals of the
project and with other parts of the group project to establish the context. Failure to do this will lead to
significant reduction in marks. This will call for your presentation to the different parts of the project to
establish the appropriate context of your presentation. Each member needs to demonstrate how they have
contributed to their respective group project and present their findings/observations succinctly together with
proper explanation relating them to the project context.
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.
Please note submission of power points only without your voice recording and video is not acceptable.
Therefore, you recording should have both audio and video. A step by step guidelines for recording voice
over power points will be available in your course moodle page in the Section Assessment 4: Individual
Presentation.
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, 5th August, 2022 (AEST/AEDT).
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
DPBS 1190 – Assessment Rubrics
