₦airaland Forum

Sageez:
Sir,

For the first dashboard, a histogram or bar chart would have been better.
Line charts are used for showing trend, but for categorical variables, histogram is usually the best.

Please correct me if I am wrong cuz I am also here to learn

Yes, you are absolutely correct. There are rules to data visualisation. Every chart has its purpose and must be used the right way. But on this dashboard, the emphasis was on the data label below the chart and not the chart itself. When creating this dashboard I'd imagine the stakeholder or client asking for a more labeled dashboard and Based on personal experience I have worked with clients who are only interested in labels not the chart itself. And that is why I opted to using Scatterplot on all boards and focus more on giving a well detailed label.

This is where I press the stop button There were more results I got from analyzing this dashboard but I can't share them all. I think the ones above are enough

Thank you for your time. Peace.

#dataanalytics
#dataanalyst
#datavisualization

6) And the just like the one above, I was also able to get the airline, number of flights, hours and price of flights that made Zero stops using the STOPS slicer.

5) Using the STOPS slicer this dashboard shows the airline, number of flights, cities, hours and price of flights that made two or more stops before getting to their final destination.

4) This is just like the dashboard above but with a different selection. This time I filtered the data to show me the airline and flights that left in the morning and arrived at night.

3) From this dashboard I was able to filter the data to show me the airline and flights that left early in the morning and arrive arrive early in the morning. This also shows the source cities and destination cities of flights that left early in the morning and arrive early in the morning. You also see the price made from this kind of flight, and also the number of hours spent on air.

Note: I also modified the slicer to make it more explanatory and attractive
* clicking on a selected item with data shows an Ash color
* the unselected item with data shows Blue.
* and finally the unselected item with no data shows Yellow.

2) The complete Dashboard without any Filters (i.e. using the slicer)

For the past few weeks I have been visualizing my data using Python and Power BI coz that has been the requirements from most client. I had forgotten you could also visualize your data using Microsoft Excel .

So today I got on Kaggle, got some sample data and worked on it.

This is a flight dataset set that shows the flight, source city, departure etc..... of each Airline.

The purpose of this analysis is to know the Airline(s) and cities that are dominating the aviation industry in India and how much revenue is been created from this airlines and cities.

So Here We Go....

1) The Flight Dataset

5) This pics shows Subquery using the where clause. The result of this query gives you genres and publishers not in the common genres and publishers from both tables. Hope you understand. Lol.

This is me just messing around with joins and subqueries.

4) This pics below shows Subquery using the From clause. The first pics (result) of this query gives you the not common genres and publishers from both tables and the second pics shows the common genres and publishers from both tables.

3) More filtering like the picture above but with # in it. The # in front of a query means that line won't be executed. Instead of writing queries over and over again, using the # kinda helps and saves times. All I need to do is put # in font of the queries I Don't want executed.

2) The pictures below shows filtering of the joined tables using the where, Group by and order by clause.

One of the queries I try to avoid most times in SQL is the subquery. Not the simple ones, the ADVANCED Subquery. Lol. Joins are quite easy to work on.

A subquery is a query that is nested inside a SELECT , INSERT , UPDATE , or DELETE statement, or inside another subquery.

When I am not working on a real dataset for clients, I do go on kaggle to get open dataset to work on.

I am gonna be sharing with you joins and subqueries I just worked on. It's ADVANCED but not deep like that. Lol.

This a Game dataset with two different tables.
(I) ps4_gamessales table
(II) xboxone_gamesales

The picture below shows how I created the database "Game_sales" and the joining of both tables and the result.

dauddy97:
Great job.
Use sns.set_style('darkgrid'); I love that dark shade

lots of color palettes in seaborn. I will check this out. Gracias.

Raalsalghul:
Jamerflepz try using one colour for your bar chart.

You can do this by editing the color parameter in your method.

Using different colours for your bar charts is kind if loud.

Thanks man. Noted.

thanksjosh005:
I think I like what I see. Good job jamer... You tried. I do mostly love the first and second one, especially the second one. All the best

Thank you.

Suspect33:
Boss, how long did it take you to become this good in data analysis, and in your opinion, which of the softwares do you think is the most difficult to learn?

Two years. I'm no expert. I'm still learning everyday. For me python was kinda hard to learn.

More.......

When I'm not working on a real dataset for clients, i do go online and look for sample datasets i can work on. That way I don't forget things. Learning four or more different tools for analysis ain't easy. After weeks of working on SQL, Excel and Power BI, I decided to brush up my python a little.

This is a "used car dataset" I found on kaggle. Instead of Excel or Power Bi for my visualization, I chose Python today.

I did some cleaning and analysing with Excel Power Query before loading them on python.

And ladies and gentlemen, this is what I was able to accomplish.

Not advanced. It's quite explanatory. No need to explain anything.

temiyato:
Ur visual is okay but needs little advice to make it better. Before i give u my little advice , am currently taking "google data analyst" on coursera , "fundamentals of data visualisation with tableau on coursera and other courses too.
1) top N shuldnt be visualized in RED . it is generally accepted that red should be used for LEAST N , loss etc
2) a pie chart should have better visualised " local address with top sales" since its in percentage or a simple bar chart would also be good compared to the area chart u used.
God bless us all sir

Noted.

Desammyst:
Which version of MS Excel did you use in creating this visualization?

Excel 2019

airsaylongcome:
So I have a second GUI frontend that connects without issue to the remote Mariadb. But not Mysql. Yeah let's wait to see if the OP has any ideas. Thanks for the troubleshooting though

I can't really say. Never really had issues like this. Maybe it could be the user/password error. Have you tried resetting your Mysql/Mariadb root password. Coz that is what a friend did when he had issues connecting to postgre server from mysql. And it worked.

3) More Filters

2) I also designed the Filters using Macro and VBA(visual basic for applications). And if you look closely the data is different from the one above.

In my last thread I posted a simple project I did with MySQL and today I will sharing with you the dashboard I created with the same dataset.

Data visualization is an important aspect in data analysis. The goal of data visualization is to make it easier for us to understand trends and patterns in large datasets.

I use Power BI for most of my visualizations. But today I decided on Microsoft Excel instead.

1) After more cleaning and analysing of the dataset, I created this simple dashboard.

airsaylongcome:
Let me go and dust up my SQL skills. Reading this was very nostalgic. With v8 I'm assuming you don't touch Maria. Postgres?

Nah I don't. I do work on postgreSQL sometimes but not as much as MySQL.

airsaylongcome:
5.7 or v8?

v8

gbengaoyeladun:
Well done

Thank you.

adebayal:
Guys Euphoria season 2 episode 5 is da BOMB ....cried thru it

I agree. I had to rewatch that scene with rue, her mom and sister over and over again. Fucking emotional. That girl can act.

jamerflepz:
2) I decided to clean and analyse the data by:
a) adding row numbers to the data
b) using the CASE function to create a new product_ranking column
c) using the WHERE clause to remove product_name that has ? on it.
d) highlighting from 9 - 17 and running it, gives you the result below.
e) this is my new dataset

This new dataset is exported to Excel or Power BI or tableau or python for data visualization. Creating a dashboard is important. Very important.

7) Finally Just like the STORED PROCEDURE above, I created another to store products with price greater than the total average price.
a) writing query from 75 - 84
b) calling the stored procedure by highlighting 86 only and executing it, which then gives you the result below.

6) Instead of sending multiple sql statements, I created a procedure to store the products with price lesser than the total average price
a) writing query from 61 - 70
b) calling the stored procedure by highlighting 72 only and executing it, which then gives you the result below.