Spreadsheet data analysis of fake sales data using Python - CFG
‣ Collaborative group project with another member involving spreadsheet data analysis of fake data provided by CFG and using Replit to work together on our code.
‣ Created data visualisations using matplotlib and numpy and extracted csv data.
Find the code here. Some data visualisations produced were:
(i) Monthly profit in 2018
(ii) Monthly sales and expenditures in 2018
(iii) Monthly sales in 2018
(iv) Percentage change in sales over the months
Some implications that can be drawn from our findings and data analysis include further questions such as:
Why was expenditure particularly high during the months of February, March and December?
Based on the type of services the firm provides, further questions that could arise are: How could expenses be controlled during these periods to increase operational efficiency? What are the components of the expenditure (was it primarily due to increased borrowing costs or was it used to invest and accumulate further capital)?
Is the change in profit in 2018 consistent over all years?
If not, what could be the reasons behind the fluctuations in 2018?
What are some ways through which the firm could increase sales during months that had low demand?
And possibly measure the impact of the methods
and many more.