A data analytics project starts with collecting the data and ends with communicating the results from the data. In between, there are multiple steps that are required to be followed- data preprocessing is one of the most important steps among them. The data preprocessing step itself has multiple steps depending on the nature, type, value etc. of the data. On the other hand, data visualisation uses visual representations to explore, make sense of, and communicate data that often includes charts, graphs, illustrations etc. Today, there is a move towards visualisation that can be observed among many big companies.
Timelines and Expectations Students are expected to work individually to prepare a report that details the use and applications of data preprocessing and data visualisation techniques on a selected data set. The aim of this assessment is to enable students to create a report that evaluates the use of data preprocessing and data visualisation techniques applied to a given case. Students are required to select a data set and answer the following questions:
What is the purpose of the data set, and what kind of insights can be extracted from the chosen data set?
Have you applied any data cleaning approaches (e.g., missing value handling, noisy data handling) for the chosen data set? Explain in your own words what data cleaning approaches you have perform or why it was not required.
Have you applied any data transformation techniques (normalisation, attribute creation, discretisation etc.) for the chosen data set? What data transformation techniques you have performed or why it was not required to perform any transformation? Explain in your own words.
Have you applied any data reduction techniques (reduce dimension, reduce volume, balance data) ?If yes, then describe the data transformation technique(s) you have followed; otherwise, explain why no transformation techniques were not required.
Design an interactive dashboard using 3-4 charts/graphs/illustrations to represent the data.
Case Study Report
Expected word count 1,500 words
Students are expected to submit their assessments via Turnitin on Moodle.
Learning Outcomes Assessed
The following course learning outcomes are assessed by completing this assessment task:
LO1. review and differentiate between the methods of data analysis and presentation; LO2. analyse internal and external sources of data relevant to business environments including technology and service utilisation data to identify relationships and trends;
LO3. develop and apply skills in spreadsheets to sort, manage, summarise and display data to support managerial decision-making;
For this assignment, students are required to write 1,500 words report on a specific case study and explain the use and applications of data preprocessing and data visualisation techniques on a selected data set. Students can choose any suitable data set that is publicly available on the internet. In week 6, students will be required to submit their report on moodle. Students are expected to work individually and undergo their own research without collaboration with any other student. Students are expected to prepare a comprehensive report on the application of their knowledge of data preprocessing and visualisation on a given case study.
All reports must include at least 5 academic references which must be done using APA7 reference style.
The case study must assess the value propositions of the chosen data set and discuss what types of business questions can be answered using the data set. It must highlight the suitability of data cleaning approachesfor the selected data set. It must highlight the data transformation techniques that are applicable to the data set. Students must also highlight how an interactive dashboard can be designed for the chosen data set to communicate the data effectively.
This unit requires you to use APA system of referencing. See Sydney International’s quick reference guide. It should be used in conjunction with the online tool Academic Writer: https://extras.apa.org/apastyle/basics-7e/#/.
A passing grade will be awarded to assignments adequately addressing all assessment criteria. Higher grades require better quality and more effort.
For example, a minimum is set on the wider reading required. A student reading vastly more than this minimum will be better prepared to discuss the issues in depth and consequently their report is likely to be of a higher quality. So before submitting, please read through the assessment criteria very carefully.
Individual report sample structure
- Coversheet (mandatory)
- Title page
- Table of content
2. Overview of the data
3. Data Preprocessing
b. Data Transformation
c. Data Reduction
4. Dashboard Design
Note: Students are allowed in include other sections as they deem necessary based on their case study.
Dataset You Can Use From Below Links
Sample data set for case study:
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