COMP1800 Data Visualization Assignment

Learning Outcomes:

1. Identify and discuss fundamental concepts related to visualization.
2. Demonstrate an understanding of different types of data visualization and identify appropriate types of visualization for various types of data.
3. Design, implement and evaluate interactive visualization systems.
4. Apply visualization tools and techniques to obtain insight from datasets.

Plagiarism

is presenting somebody else’s work as your own. It includes:

copying information directly from the Web or books without referencing the material; submitting joint coursework as an individual effort; copying another student’s coursework; stealing or buying coursework from someone else and submitting it as your own work. Suspected plagiarism will be investigated and if found to have occurred will be dealt with according to the procedures set down by the University.

All material copied or amended from any source (e.g. internet, books) must be referenced correctly according to the reference style you are using.

Your work will be submitted for electronic plagiarism checking.

Any attempt to bypass our plagiarism detection systems will be treated as a severe Assessment Offence.

Detailed specification

Please note that this is the same coursework and data as the main cycle coursework. If you already submitted work at the end of Term 2, you can use the feedback provided to improve it and resubmit.

You are to carry out a visual data exploration for ChrisCo, the fictional company whose sales and website data we have been analysing throughout the module, using a Python Notebook (in Colab or Jupyter).

ChrisCo is a fictional, but nonetheless very successful company managing a range of retail outlets across the UK. ChrisCo collects a huge amount of data about individual customers visiting its outlets using its loyalty card scheme but this customer data has been aggregated/averaged to give information about the company’s 45 outlets, each identified by a unique 3 letter code (e.g. ABC, XYZ, etc).

Data

Each student on the module has their own, randomised version of the dataset to explore. You will find your data in the following csv files, where ID is your 9-digit student ID number (e.g. 001234567):

• https://tinyurl.com/ChrisCoDV/ID/OutletDailyCustomers.csv listing the daily number of customer visits to the company's outlets
• https://tinyurl.com/ChrisCoDV/ID/OutletMarketing.csv the total annual spend on local marketing for each outlet
• https://tinyurl.com/ChrisCoDV/ID/OutletOverheads.csv the total annual cost of overheads for each outlet
• https://tinyurl.com/ChrisCoDV/ID/OutletSize.csv the size (floor space) in metres squared for each outlet
• https://tinyurl.com/ChrisCoDV/ID/OutletStaff.csv the total number of full-time staff employed at each outlet

Please contact the module leader if you cannot find your data files.

You should compile the data into two dataframes: one containing daily customer data (one row for each date); the other containing summary data (one row for each outlet), compiled from all of the .csv files, including the daily visitors.

Report

Your task is to investigate the data visually and present some conclusions about any characteristics you discover, including correlations, seasonal behaviour, outliers, etc., together with a suggestion about how the data might be best segmented, based on the total volume of visits at each outlet.

The company is most interested in the high and medium volume outlets but would like a summary of the low volume outlets plus any anomalies you identify in the data. You should also identify new outlets that have been opened during the year or outlets that the company has closed during the year.

You should present your findings in the form of a pdf report for the company, i.e. based on the assumption that the reader knows nothing about data visualization. The report should include:

• A brief introduction to data visualization (no more than ½ a page).
• A discussion of your findings, including a total of 8 visualizations (your mark will be capped if you include more – see below under Grading Criteria). Each visualization should be accompanied by two paragraphs of text in which you should present:
  • a justification for including that particular visualization:
  • a description of what the visualization reveals about the data – do not assume that the reader will recognize and understand correlations, seasonality and anomalies.
• A critical review of your work, with a discussion of what you have learned from the module, how you have applied it to the coursework, and how best practices were demonstrated (about ½ a page).
• A summary of the conclusions you have drawn (no more than ½ a page). You are not required to make business recommendations and the summary may contain conclusions as bullet points.

For the 8 visualizations you include, you should choose your most illuminating charts / plots and paste in a screenshot. It is strongly recommended to use Insert > Screenshot in Word or the Windows snipping tool (or similar) and to carefully crop each screenshot so that it shows only the visualization. Also do not distort the images when you resize them – if you do change the size make sure you maintain the aspect ratio.

The interactive visualizations (see below) should be included as 2 of the 8 visualizations in the report and for these the screenshots should illustrate some aspect of the interactivity (e.g. zoom, hover tools, etc).

All 8 visualizations should be carefully numbered and labelled, with a self-explanatory title and legend (if appropriate) and should be referred to in the text (e.g. "Figure 1 shows that …"). Do not paste in visualizations that are not referred to in the text, as you will not gain any marks for them.

Finally, the visualizations must demonstrate that you have understood and made use at least some of the techniques taught in the module. Also, the order of the visualizations should be carefully considered, leading the reader through the data exploration, ideally with each visualization leading on to the next one.

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