ITECH1103: Big Data and Analytics - Data Analytical Report Writing - IT Assignment Help

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Assignment Task:

Purpose: The purpose of this task is to provide students with practical experience in working in teams to write a data analytical report to provide useful insights, pattern and trends in the RETAILDEMO_2 dataset in the light of guided questions asked on page 4 of this document. This dataset is located in SAS Viya archives of datasets. This activity will give students the opportunity to show innovation and creativity in applying SAS Analytics, and designing useful visualization and predictive solutions for various analytics problems.

Project Details:
This is a group assignment and you will complete the task with your team. Your team will be made up of at most 3 members who are all enrolled in the same laboratory – the teams will be allocated by your tutor. It is expected that each team member will contribute equally to the project.

Tasks

• Task 1- Background information - Write a description of the dataset and project, and its importance for the organization. Discuss the main benefits of using visual analytics to explore big data. In this you should include a justification for using the visualizations that you will use and how they have been successful in other similar projects. This discussion should be suitable for a general audience. Information must come from at least 6
appropriate sources (2 per student) be appropriately referenced. [2 to 3 pages]

• Task 2 – Reporting / Dashboards - For your project, perform the relevant data analysis tasks by answering the guided questions provided (see Appendix for questions and dataset) and, identify the visualization you need to develop.
Note: remove any missing data points from your visualizations where possible/suitable

• Task 3 – Additional Visualizations - In addition to the guided questions, it is expected that each student will provide at least two other visualizations of the data (i.e. for a group of 3 students this is 6 extra visualizations).
These additional visualizations will be judged in terms of quality of the findings and complexity of analysis.

 

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