DASHBOARD RECYCLING
You might be wondering what’s dashboard recycling as a data analyst or whatever you might be reader.You have never heard of it right?
Well it just popped out of my head. Reading through this article will give you a glimpse of its definition.Yo! not talking too much but I think I just got a new word for data analysis vocabulary.
Hi my unique reader.You already know I’m here to talk about the behind scene of this beautiful dashboard.
Okay so this dataset has 13 columns and 1027 rows. I kind of just followed my instructor. Okay let’s wind to the behind the scene
Link to dataset:https://us.docworkspace.com/d/sIHjM2s1avKS9rAY?sa=e1&st=0t
SCENE ONE-DATA CLEANING
What’s data cleaning?
Data cleaning is simply as the name implies.It is cleaning your dataset so you can use it for analysis and visualization.
Cleaning for this dataset commences thus:
*Duplicate removal: All duplicated data was removed. 26 duplicate values were found and removed reducing the data set to 1000 unique value excluding the title row (just in case you wonder why the calculation doesn’t add up).I believe it does to you now.
*Find and replace: In data cleaning, some words might not be precise. To make it easier for our readers,we simply replace those words with what the readers can understand.
Okay so for the gender column, M was replaced with Male while F replaced with Female. Marital status column,M was replaced with Married and S with Single.
*Approximation:Do you find decimal values boring? I actually do in a way. So as is expected from a non-decimal fan, I rounded all my values to whole number.
*Column addiction (another new vocabulary I came up with): I feel this my new found word should be used often. Like there’s literally no data analyst that doesn’t add new columns to their dataset they are working on. If there are not adding, they are either deleting or hiding a column. Proof me wrong if you can in the comment section.
Age brackets columns were added. Want to know more about it? Get the dataset through the link as read previously.
That’s all for the cleaning,puuur!!
SCENE TWO-DATA VISUALISATION
Made use of:
*Pivot table: it helps to build dashboard for visualizations. Did this on a new sheet. About three or more pivot tables were created for this dashboard, namely:
1. Average income by gender
2.Customer commute distance
3.Customer age brackets that did or did not purchase a bike
4.Customers that own a car and also purchased a bike
*Dashboard
Each of the above pivot tables have been graphically represented on charts which build up to make this breathe taking dashboard. Slicers where attached for easy filtering and easy communication with the dashboard overall.
ENDING SCENE-CONCLUSION
Eyes can see that this is a color coordinated simple dashboard. The dashboard is self-explanatory that even a lame man can interpret it. Okay so for the data recycling as my topic choice, let me bring your curiosity to a halt. I called it recycling because it’s simple a recreation of this video https://youtu.be/opJgMj1IUrc?si=aEu9qeW3xxSmTn98. I learnt from the analyst wizard Himself-AlexFreberg.
You should start getting used to hearing new words from me which distinct you from others when in use.All said and done,hope you had a good read?
Do let me know what you want us to discuss in the comment section and recommendations to this work are highly welcomed.
ADIOS!!!
