MySkill Data Analysis Part 3 : Data Visualization with Google Data Studio
Data visualization is a representation of data, information with the help of various visual elements, graphs, charts, and maps. It is used for quick information assimilation, pattern and relationship identification, discovering, pinpointing emerging trends, and communicating the story to others in a very visual and easy-to-understand way. Data visualization has changed the way we work with data, allowing us to create greater depth and respond to issues more rapidly than ever before.
There are many data visualizations apps available online, that provide various approaches for showing data visually. It might take a slight amount of time figuring out which app or service would provide the most suitable way of data representation for your business; however, if you are looking for something simple, easy-to-understand, versatile and easy to access, then Google Data Studio might be the data visualization dashboard creator you are looking for.
Google Data Studio is free, accessible for everyone, provides various options of data representation, very versatile, adaptable, easy-to-understand, and requires no software to be downloaded.
Visit google data studio homepage and create your first dashboard now!
This article will explain the exercises that given during Intensive Bootcamp Data Analysis by MySkill. For these exercises, we will use Google Data Studio because the platform is simple and not heavy as a program.
Task 1
Using this dataset, create visualizations in Google Data Studio which can answer the following questions:
Q : How many total bookings were made in each type of hotel? More
what type of hotel has been booked?
Answer :
For this question we need to visualize our data with barchart. Then we need to
- Use reservation_status_date in Data Range Dimension
- Put Total Booking metric on the barchart
- Put Hotel as dimension
- Sort by Record Count descending
Output
From the Bar Chart above it can be seen that the total bookings for city hotels were 79,988 and for resort hotels there were 40,277. So it can be concluded that the total bookings at city hotels were more than resort hotels. So based on this, resort hotels can further improve hotel services and facilities so that more and more people will book hotels.
Task 2
It is known that this dataset comes from resort hotels in the Algarve and city areas hotels in the city of Lisbon, both in Portugal (please read the paper, section “Specifications table”).
Q : Show a visualization that compares the number of bookings by local tourists (local market, from Portugal) and bookings by inbound tourists (inbound tourism, origin from other countries). Where do the most bookings come from?
Answer :
For this question we need to visualize our data with barchart. Then we need to
- Use reservation_status_date in Data Range Dimension
- Put Total Booking metric on the barchart
- Put Market as dimension
- Sort by Record Count descending
We need to make a Market field. This is the formula for the field:
CASE
WHEN country_fullname = 'Portugal' THEN 'Local Market'
ELSE "Inbound Tourism"
ENDOutput
From the Bar chart above it can be seen that the total bookings by inbound tourists (inbound tourism, origin from other countries) as many as 71,675 and local tourists (local market, from Portugal) as many as 48,590. So it can be concluded that the total bookings by Inbound Tourism are more than local tourists. So based on this, the hotel can carry out more promotions for local tourists so that more and more local tourists are booking hotels.
Task 3
ADR (Average Daily Rate) is one of the most important metrics in the industry hotel.
Q : How is the pattern of ADR in each type of hotel based on the average ADR in each week? In both types of hotels, the average ADR goes up and down over the period (week/month/season)?
Answer :
To answer this we need to visualize our data with Time Series Chart. We put the ADR in the metric. We use arrival_date (ISO Year Week) in dimension and hotel in breakdown dimension. We put reservation_date in Data Range Dimension also ADR in breakdown dimension sort.
Output
From the graphic above it can be seen that the average ADR of both types of hotels up and down in different period (week/month/season). However, at several points such as September 2015 (week 37), June 2016 (week 25), September 2016 (week 36) and June 2016 (week 25) it is seen that the average ADR of the two types of hotels is at the same point.
Task 4
Q : What is the cancellation rate for each type of hotel each month? Which type of hotel has the highest cancellation rate?
Answer :
To answer this we need to visualize our data with Time Series Chart, then we need to make a Cancellation Rate field. This is the formula for the field:
COUNT_DISTINCT(id cancellation rate)After that we put the Cancellation Rate field in the metric. We use arrival_date (Year Month) in dimension and hotel in breakdown dimension also reservation_date in Data Range Dimension
Output
From the graphic above it can be seen that cancellation rate from City Hotel is bigger than Resort Hotel. The biggest cancellation rate from City Hotel is 67.17% on July 2015 and the lowest is 24.37% on November 2015. Meanwhile the biggest cancellation rate from Resort Hotel is 38.5% on August 2017 and the lowest is 13.46% on January 2016. It can be seen in the graph that in July 2017 the cancellation rates from City Hotels and Resort hotels were the same at 37%.
Task 5
Q : How many canceled bookings for each type of market segments? In which market segment has the highest cancellation rate?
Answer :
To answer this we need to visualize our data with Combo Chart, then we need to make a Total Cancellation Booking field. This is the formula for the field:
COUNT_DISTINCT(cancelled bookings)After that we put the Total Cancellation Booking field and Cancellation Rate in the metric. We use market_segment in dimension and reservation_status_date in Data Range Dimension
Output
From the visualization above it can be seen that the number of canceled bookings for each type of market segment is :
- Online TA : 20,739
- Groups : 12,097
- Offline TA/TO : 8,311
- Direct : 1,934
- Corporate : 992
- Complementary : 97
- Aviation : 52
The highest market segment cancellation rate is groups with 61.06%.
This is the end of the explanation regarding the training assignments given by MySkill in Intensive Bootcamp Data Analysis to hone skills in visualizing data using Google Data Studio . Thank you, I hope every explanation is useful.
If you want to go to the other parts, go here:
- Part 1 : SQL for Data Analysis
- Part 2 : Python for Data Analysis