IMAT2913 Data Visualization
Summative Assessment Task 1
Report (60% of module coursework mark)
Imagine…
A supermarket is beginning to offer a line of organic products. The supermarket has a customer loyalty program. As an initial buyer incentive plan, the supermarket provided coupons for the organic products to all of their loyalty program participants and have now collected data that includes whether or not these customers have purchased any of the organic products. You are a data analyst and have been commissioned by the supermarket’s manager to analyse the products data and to provide the supermarket’s manager with some insights. The products data contains the attributes shown in the table below.
Attribute
Description
CUSTID
Customer loyalty identification numbe
GENDER
M = male, F = female, U=Unknown
AGE
Age (in years)
TV_REG
Television region
REGION
Geographic region
CLASS
Customer loyalty status: tin, silver, gold, platinum
ORGYN
Organics purchased? 1=Yes, 0=No
AFFL
Affluence grade on a scale from 1 to 30
LTIME
Time as loyalty card membe
OAC
Output Area Classification Demographic label sourced from ONS
1=Rural, 2=Cosmopolitan, 3=Ethnicity central, 4=Multicultural metropolitans, 5=U
anites, 6=Su
anites, 7=Constrained city dwellers, 8=Hard pressed living.
S_MT
Average Weekly Spend on Meat
S_FVEG
Average Weekly Spend on Fruit and Vegetables
S_CONV
Average Weekly Spend on Convenience Food
S_TOIL
Average Weekly Spend on Toiletries
Use the following guidelines to know what you should do with the provided dataset (refer to the Marking Grid For Summative Assessment Task 1 file for more details):
Data import and understanding
What you should do?
Deliverable Checklist
· Import data as a data frame.
· Print and comment on the data type of the imported attributes.
· Show and comment on the count of missing value and its percentage for each attribute.
· Show and comment on the descriptive statistics of the attributes.
· Explore and comment on the unique values of the attributes.
· Change from one data type to another with providing justification for any change.
· Print out of all python code (as figures).
· Print out of all generated outputs/charts.
· Your justifications and comments.
Data questions & visualization AND Visualisation channels and retinal variables
What you should do?
Start by choosing THREE QUESTIONS you'd like visualizations to answer (they should provide insights to help take actions). Next, design a visualization chart to answer each question. Then, provide a
ief description of your design (e.g., the use of data wrangling to prepare the data for visualization to find the answer for the posed question, choice of visualization plot, size, colour, scale, and other visual elements).
Deliverable Checklist
· Print out of all python code (as figures).
· Print out of all generated outputs/charts.
· A
ief description and justification of your design. Do not interpret the generated visualizations.
Reflection
What you should do?
Deliverable Checklist
Show how your data visualization knowledge improved over the course and how this might be applicable in a different context. In addition, show how you can validate your design to assess its effectiveness to communicate the derived insights.
· Your reflection.
· Design validation.
Note: Your contribution to the report should be no longer than 1,500 words. Use a minimum font size 12.
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