FIT5147 Data Exploration and Visualisation

Semester 1, 2025

Programming Exercise 1: Tableau (5%)

Please carefully review all the requirements below to ensure you have a good understanding of what is required for your assessment.

1. Instructions &Brief

2. Assessment Resources

3. Assessment Criteria

4. How to Submit

5.   Word Count & Penalties

1. Instructions & Brief

In this assignment you are required to read in some data and explore and visualise it using Tableau Public/Desktop, then also submit a brief report showing your findings and the visualisations you used. It is an individual assignment and worth 5% of your total mark for FIT5147.

Relevant learning outcomes for FIT5147:

1. Perform. exploratory data analysis using a range of visualisation tools;

6. Implement interactive data visualisations using R and other tools

THE DATA:

The data set used in this assignment is based on the AusStage online resource. It is “a data  set of live Events with dramatic and performance content covering all of Australia and New Zealand plus many additional International links” (AusStage, n.d.) and is regularly updated. While the data we are using was collected in February 2025, it includes both recent events  as well as ones from previous centuries.

We introduced this dataset in the Week 1 Workshop. For this assignment, use the provided PE1 dataset to produce your Tableau visualisations and visual analysis.  It is based on that found in AusStage but has been slightly modified.

To enhance your understanding of the context and metadata, you can check the data source link. Using the various interactive tools for the data source of the full dataset may help enrich your visual analysis: https://www.ausstage.edu.au/pages/learn/search-ausstage . If you discuss or replicate the visualisations or metadata provided by AusStage, be sure to reference these correctly in your report1.

Please note that the event and performance names relate to real Australian performance art and culture. Some names may have some explicit terms.

For this PE1 assignment, the resulting data describes when and where events occurred in the state of Victoria. In this activity we will explore the use of a few attributes:

Table 1: Fields of the “AusStage_S12025PE1” data set

In this data there are some irregularities or errors that were part of the original data. One of the requirements of this assignment is for you to find (using data visualisation), describe and handle them. This modified dataset can be found on Moodle in the Assessments section under the Programming Exercise 1 heading.

References:

AusStage. (n.d.). AusStage: About. AusStage. Retrieved February 27, 2025, from

http://www.ausstage.edu.au/pages/learn/about

VISUAL ANALYSIS QUESTIONS TO BE ADDRESSED

Using the data and visual analytics, you will need to answer the following questions:

1A.       What are the most common event names?

To answer this question, discuss how you are going to identify, measure and visualise what are the most common events.

1B.       How many events started each year over the last 25 years?

To answer this question, discuss how you are going to identify, measure and visualise the number of events started each year.

1C.        How many events were run or performed by each organisation?

To answer this question, treat each Organisations value as a single organisation, even  if it includes different groups. Discuss how you are going to measure and visualise the number of events for each organisation.

1D.       How many organisations started events each year over the last 25 years?

To answer this question, discuss how you are going to identify, measure and visualise the number of events started each year.

1E. How long did each event run for?

To answer this question, discuss how you are going to identify, measure and visualise the number of years each event ran in.

2.          Based on the visualisations and findings for 1A-E, is it possible for you to now

explain who (i.e., Organisation) ran or performed what events over the last 25 years?

For this question, you need to discuss whether your visual analytics for 1A-E have enabled you to answer this question or not. Be sure to explain how you came to that conclusion.

ASSESSMENT TASK

The task has two components: data exploration using Tableau, and a short written report. Data Exploration using Tableau:

The steps you are expected to complete:

1.   Load the dataset in Tableau Public/Desktop

2.   Use data visualisation in Tableau to check for and find at least two aforementioned irregularities in the dataset. Each type of irregularity may occur multiple times in the data. These irregularities are not related to missing data.

3.   Amend the data to correct these errors using any tool of your choice (e.g., Excel, Python, R, Tableau) and justify your choice of correction based on the irregularity.

4. Use Tableau to create at least one visualisation per question (not more than 2 per question) to conduct your visual analysis and answer the above question.

Remember to select appropriate visual variables to suit the data and your chosen visualisation.

5.   Polish up your visualisations for presentation, e.g.,  add a suitable title, correctly label your axis, make sure labels and values are not truncated, include a legend. Ensure the font, font size and colour are suitable and legible for your report.

6.   Write a report that presents and describes your data exploration process and visual analysis. See below for details.

This exploration must be submitted as a Tableau workbook file (*twb suffix).

Note: Indications of missing data like UNKNOWN/unknown, NULL/null, N/A, tba values should not be regarded as irregularities for this assignment.  If Tableau has any issues automatically recognising any date or time information, then this is not to be regarded as an irregularity for Step 2 but can be corrected by you in Tableau.

Written Report

Once you have finished your data exploration, write a report that contains the following information:

1.   Data loading, checking and cleaning (i.e., Steps 1 to 3)

o  A brief explanation (maximum of one paragraph per error) and an

accompanying image of each of the errors or irregularities that you have found, showing how you found them using Tableau, and explaining & justifying how you resolved them. The image must show a relevant visualisation, not just the data or a table.

2.   Data Exploration and Presentation (i.e., Steps 4 and 5)

o  Explanation of what insights you have found out through the visual analysis in order to answer the questions. This should include:

■   Your answer to the question, based on your visualisation(s). Include relevant visualisations in 1 or 2 figures per question.

.   Description of your visualisation(s) and how they relate to the data and question (i.e. why it is an appropriate visualisation choice)

.   Justification of your visualisation(s) and choice of visual variables

.   Any further insights, or issues that you have identified from the data or visualisation(s) while answering the question.

The report should also:

●    Be submitted as a PDF file

●    Be no more than 5 pages in length, including figures, with a minimum font size of 10 (title page and any table of contents are excluded from the page limit)

●    Be properly structured with headings, subheadings, figure captions (in-text  referencing of captions), page numbers, and references (where appropriate)

●    Have high quality images of your visualisations with clearly readable and legible text/labels (presume that it is read as part of an A4 document with no zooming).

●    You must use proper academic referencing for all reports in this unit. This should follow either the APA or IEEE  structure as recommended by the Faculty. Use the library referencing guide for support.

●    Not include any code snippets except for key Calculated Fields in Tableau.

● No Generative AI software or system may be used to complete this assessment task. This includes using any software that paraphrases, translates or rewrites your text.

2. Assessment Resources

● AusStage_S12025PE1.csv (Available on Moodle)

3. Assessment Criteria

The following outlines the criteria which you will be assessed against. The focus of the marker will be on what you have included in your report, but your submitted Tableau Workbook may be examined if there are any concerns with the academic integrity of your work.

●    Demonstrated ability to check and clean data and read into Tableau [1%]

●    Demonstrated ability to appropriately visualise data for data exploration using Tableau [2%]

●    Demonstrated ability to see trends/patterns in data [1%]

●    Quality of report [1%]

4. How to Submit

Once you have completed your work, take the following steps to submit your work.

1.   Save your report as a .pdf file.

2.   Name your file using the following structure PE1_Surname_StudentID

3.   Save your Tableau workbook as a .twb file.

4.   Compress the .twb workbook file into a .zip file so it can be submitted to Moodle. DO NOT include your report in your zip file, only your Tableau workbook.

5.   Name your zip file using the following structure PE1_Surname_StudentID

6.   Click the Add Submission button on Moodle to submit and upload your report and workbook

Please note that your assignment MUST show a status of "Submitted for grading" before it can be marked. Any submission left in draft mode will not be marked. We recommend always double checking your submission has been completed and that you have uploaded the correct files. Penalties will apply to any submission which needs amendment after the deadline.

5. Word Count & Late Penalty

The report must not be more than 5 pages of graded material including figures (min. font size 10). Up to 2 additional pages may be used if you wish, but restricted to:

● 1 page prior to the report as a title page with a table of content.

●   1 page after the report only for references.

1 mark (out of the total of 5) will be deducted if the report does not meet these requirements.

As per Monash policy: All late submissions will receive a penalty of 5% per day (0.25 marks per day out of a total of 5 marks) late inclusive, including weekends. Work submitted more than seven days after the due date will not be marked.