Starting off With ‘Google Data Studio’ via CXL Institute
Tags: Google Data Studio, Maps, Visualization

I now move to Google Data Studio after learning about Excel and Pivot tables in the last course.

The instructor is Michelle Kiss and we are going to be learning about the use of Google Data Studio. Michelle works as a Senior Partner with a company called Analytics Demystified, and has around 13 years of work in the digital analytics industry.

Data studio is a free products and is Google’s versions of tableau. It connects to a lot of data sources. It launched as a beta in 2016 so it is fairly new. It is also constantly changing. And the best part is that is free.

So our first lesson is an overview of the Data studio, types of data and how to explore data and look at some basic functionality and some fundamentals of data viz

Link for example data: mkiss.me/gademodata

We are going to be using GA data for this.

We take a live look at the UI.

Now there are a number of pre-build templates, but we can jump straight to create one new.

Data Source:

Where is the data coming from? Where is the source?

Now there are Google native connectors for some products, there are also partner connectors sou can bring in other data sources.

Anatomy of a Data Source:

Green = Dimensions

Blue = Metics

Type (Dimensions)

No

Text

date

True/False

Goe

Currency

URL

Metrics

No

Percent

Duration

Currency

We will create a new data source from a GA account (demo). We are going to use View 1.

We are shown how to create a report. There is also an option called “Explore” which allows us to see or get a feel of the data before using it.

We learn a few things about how to use the ‘reports’ section. We see about different layouts and tips.

From here, we move onto our second lesson which is going to dive into the different visualisations that are available in the Data Studio. This looks like a really long session, slightly more than an hour.

We are going to learn about how we can use different viz to convey different messages, get the audience to see whats in the data and how different chart types might work together to paint a really complete picture of your data.

We then move onto building our first chart. Some data viz that are there in the Data Studio are tables, scorecards, line charts, Bar, column, area charts, maps, donut charts, etc

Our first chart is a line chart that we are attempting to build. We toggle thru many of the features that are there like Legend which can be helpful with guiding us to understand what data we are looking at.

Also we should remember that Data Studio might make some choices where it starts your axis where it decides it wants to be, but we can configure it under the style.

We spend time going through many of the features about how to use create charts and what all we can do with them. We make an ‘Area Chart’ — add a chart — it shows up on the page — no of sessions we get from mobile, desktop, etc from Google merchandise store.

We can choose colour from our metrics. We can also show many series we want to show.

We can also add axis title, etc. There are a lot of options there to use. We then move to learn about Sparklines. We get a quick gist of what the trend is using them. It can help in showing these are what are the big numbers. People want to know that something did not drop off or there is no major spikes.

Above the time series, we have the Sparklines. We use a sparkline in conjunction with another chart. There is also in-table bars as this makes it easy for the users and help them to understand what are the high and lows of the data set.

In-table bars below pic.

We look at this example and see how we can work with it.

Scorecards are not tables.

Be thoughtful of how u use decimals. look at u r data and make the right choice.

PIE-CHARTS — Why they fail?

1. Human Brain can’t judge the size difference in area very well

2.People use too many pieces

When to USE

1. less than three items

2.Must be parts of a whole.

Yay!!!! MAPS — they are so COOL

It helps in knowing from where our traffic comes from. It also take in a lot of real estate. Of course, a bar chart will take up less space.

When we hover over the map, we get the value over it.

Overall, in this section, we looked at a lot of data viz options that are available and how we can convey messages through them. Also we learnt about how to customise them so that our users can drill down and drill up different charts.

Viz are something that I have been doing for a while now. As a ‘data journalist’, I often used many online tools like datawrapper, Infogr, tableau, etc to make interesting and interactive charts and graphs for many of the sites I’ve worked with in the past.

Now I am off to Colours

(This is the a part of my series as a fellow with CXL institute where I am pursuing a mini degree in Analytics)