Diploma in Business
Data, Insights and Decisions
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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
6. CLO6 Critically evaluate the suitability of data and data sources to identify and analyse business
Due Date Weighting Format Length/Duration Submission
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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.
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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
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
• 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)
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.
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.
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
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 attributesbetweensuccessfulandnot-successfulstart-ups;
o trendofthreevariablesthatyoudeemimportantforthesuccessofcompanies.Youarefree
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)
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
• 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
• 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.
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).
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?
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 sec
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