DPBS 1190 DATA, INSIGHTS AND DECISIONS 2024

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DPBS 1190

DATA, INSIGHTS AND DECISIONS

May  2024

Diploma in Business

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 datasets 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.

ASSESSMENT l:TUTORIAL PORTFOLIO

3.1     DESCRIPTION OFTHE 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 datasets 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 often (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.

For this assessment task, you will be marked according to the criteria provided below.

ASSESSMENT 2: INDIVIDUAL PROJECT REPORT

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 datasets 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 using a dataset on startup companies across different cities in Australia.

A startup 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 several 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 expanses    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 start-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

Management= This indicates level of management efficiency:  1 is poor, 2 is moderately efficient; and 3 is highly efficient

Type = 1 is service provider, 2 is manufacturing; and 3 is financial institution Size= 1 is small entity, 2 is medium entity; and 3 is large entity

Market = 1 is Asian Market, 2 is European Market, 3 is Middle East and North Arica Market; and 4 is North America Market

You area 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 regarding various start-up companies and prepare a report.

The major objective of this exercise is to derive a general understanding what factors tend to contribute to the success of startups; along with gaining insights about the attributes between successful and non-successful startups.

You must present your findings, supported by data visualisations and descriptive statistics, in the form of a written report (maximum of 1000 words) that should include:

•    Setting the goal for your exploratory data analysis exercise.

•    Descriptive statistics of relevant variables in the dataset in line with your goal and explain why such statistics are relevant to your analysis.

•    Data sub-setting that you deem necessary to conduct your analysis.

•    A visual data analysis using bar plot, line chart, histogram, and bubble plot to show the relationship among different variables in determining success of startups.

•    Impact of outliers in your analysis through box plot on any two variables in the dataset. You are free to choose to select these variables. For outlier analysis, you are required to identify the exact number of outliers using appropriate R code.

•    Interpretation of your findings and actionable insights from your visualization and descriptive statistics outlining key messages from your analysis.

•    Your findings and insights should be supported by data visualization and descriptive statistics.

•    You are expected to apply your broader understanding about the operation of startup companies through undertaking online research and apply such understanding into your analysis to generate insights. You are not expected to review more than two articles in your online research.

4.2 SUPPORTING RESOURCES AND LINKS

The assessment dataset is provided in the Moodle

4.3      TIPS FR AN ALY SIG 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 to your analysis in a thorough manner in terms of factors responsible for successful  startups. 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 success of startup companies. Asking questions, like what are the major attributes for making startup successful? This can help provide greater structure for your analysis.  You are strongly advised to undertake online research as to understand the attributes for successful startup companies. Your online research should ideally limit to two articles.

4. 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, line chart, histogram, and bubble plot to support your analysis. In addition to your visualization exercise, you also need to perform descriptive statistical analysis.

5. Also look for potential outliers in the dataset through box plot. What can we infer from these outliers? 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.

6.  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.

7. 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.

8. 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.

9. You must sign a 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”.

10. You are required to provide appropriate references (done via Harvard in-text reference). This do not count towards the assessment’sword count. Consult the link for further information about referencing https://www.student.unsw.edu.au/harvard-referencing

4.4 SUBMISSION INSTRUCTION

Submit a word document 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. The R codes will not be included in the word count. 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 14th June 2024 (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.

5 ASSESSMENT3: GROUP PROJECT REPORT

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 datasets 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 2. 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.

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 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.

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    ASSESSMENTTASKAND FOCUS

In  this  assessment,  you  will  continue  to  explore  the  same  data  set  as  of  your  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:

define a clear project goal;

develop predictive models using regression R codes and splitting the data into train and test

data with a ratio of 80:20;

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 which factors are likely to contribute the success of startups;

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).

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 inline 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.

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. Your online research should be limited to 2 articles. Remember, business analysts should  work  like  designers,  exploring  possible  alternatives  through  understanding  specific business requirements.

5. If appropriate,connect findings or questions from your individual reports to your group report.

6. The report should have the following sections:

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 the startups.

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:

o Developing predictive models, including regression and best subset regression and detailed interpretation of results in line with the assessment task and focus mentioned above.

o 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.

Actionable insights and recommendation (400 words) Actionable insights from your analysis in the context of project goals and outlining key message(s) to a range of stakeholders, primarily targeted to the senior management of MarketGo, including non-technical audiences. Your recommendations should be supported by your data analysis and highlight the strategies for improved operations of startups.

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 DPBS 1190 and how such learning has helped you in undertaking this project.

R codes used to analyse and interpret the data should be given in the appendix 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.

Table demonstrating each member’s participation in their respective allocated work based on the team contract (word count will not apply).

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.

A declaration signed by all members of the group confirming that it is the original work of group members (word count will not apply for this). This declaration must be in the cover page of your report and include the statement with signature from all members of the group:

“We declare that the work we have submitted relating to this assessment task is completely of our own and we confirm that we have complied with all requirements of UNSW Academic Honesty and plagiarism policy”.

You are required to provide appropriate references (done via Harvard in-text reference). This do not count towards the assessment’sword count. Consult the link for further information about

referencinghttps://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 references, tables, appendices, and R code) for the understanding of non-technical audience.

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 26th July 2024 (AEST/AEDT).

Your submission must Include your group 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.

5.8     SUPPORTING RESOURCES AND LINKS

You should get guidance on group work through visiting https://student.unsw.edu.au/groupwork