How to Create a Dashboard with Financial Data using Google Data Studio
By: Barovier Allybose
Tags: Google Data Studio, Visualization, Dashboard Design, Financial Data, Data Science

After a few years of experimenting with different programming tools, I was curious and ready to add a variety of visualization tools to my tool kit. I felt that I had covered the basics of model building but had never really touched on visualization as an independent topic.

Living in a visual world where pictures and symbols make everything simpler, I didn’t have to search long for the right tools — there are many programs that allow us to create visual dashboards. And after learning about Tableau, Microsoft Power BI, and Dash, one tool that stood out to me, because of its built-in features, affordability (it’s FREE) and convenience, was Google Data Studio. If you have used Google Drive products before then the features of this interface should be very familiar.

In this tutorial, I will go through a simple example of how to visualize financial data from Yahoo Finance by creating a dashboard in Google Data Studio.

The tutorial is structured as follows:

Overview of the data from Yahoo finance

Plan of the visualization and dashboard layout

Finally, creating the dashboard in Google Data Studio

Getting Started

The first questions in approaching any data science problem includes: “What questions am I trying to answer?”, and “What data do I have?”

We are tasked with finding out how stock prices change overtime and what relationship exists between Netflix (NFLX) stock prices and stock volume.

The sample dataset that we will be using for this tutorial is a record of NFLX stock data corresponding to the period between 2009 and 2019, inclusive. Yahoo Finance provides free and accessible historical stock data for publicly traded companies.

Two important terms to keep in mind as we move forward are:

Metrics: quantitative measurements

quantitative measurements Dimensions: attributes of the data

There are several attributes in our dataset, namely:

DATE : refers to the year, month and day that the stock is traded (e.g. Dec 27, 2006)

: refers to the year, month and day that the stock is traded (e.g. Dec 27, 2006) Adj Close : Stands for adjusted closing price, amends the stock’s closing price to reflect the stock’s value after accounting for any corporation actions.

: Stands for adjusted closing price, amends the stock’s closing price to reflect the stock’s value after accounting for any corporation actions. Close : A number that represents the price of the security at the end of the day’s business in a financial market

: A number that represents the price of the security at the end of the day’s business in a financial market High : A number that indicates the highest price that the stock trades during the day

: A number that indicates the highest price that the stock trades during the day Low : A number that indicates the lowest traded price for a security during the day

: A number that indicates the lowest traded price for a security during the day Open : A number that identifies the price at which the security first trades upon the opening of the exchange on a trading day

: A number that identifies the price at which the security first trades upon the opening of the exchange on a trading day Volume: An amount of the asset that changes hands over the course of a day

From this list we will only be using DATE, Adj Close and Volume for the tutorial.

With our data downloaded, let’s get started!

Choosing the right visualizations

Google Data Studio offers a variety of visualizations, ranging from candlesticks to line charts, which you can view here. To know which visuals you would like to work with, you have to inspect the dataset, keeping in mind the problem that you are solving.

In this tutorial, we would like to see how the stock prices of NFLX changes over time, along with volume. We will choose the time series line graph.

Since the data is already organized, it will be straightforward how to create this plot. However, bear in mind that it will not always be this simple! Where we may have unstructured or uncleaned data, this adds another element to the process — we would be required to feature engineer our data to fit the appropriate time series chart. For example, this could include converting dates to a format that is generally acceptable, or changing “NAN” values that would otherwise complicate our plot.

It is also important to understand the data type that we will be working with: categorical vs numerical. Bar and pie charts are best for categorical data. Dot plots, box plot, scatterplots, and time series graphs are usually preferred for numerical data.

The next step is to brainstorm what our dashboard should look like. Oftentimes, I have heard the saying, “your dashboard should be like a menu”, with a few sections that presents the most crucial information about what you want to offer. It is also similar to a roadmap: simple, clear and concise.

This is a good place to stop and sketch what we would like our dashboard to look like.

Creating the Dashboard in Google Data Studio

Open Google Data Studio. From the ‘create’ menu list in the top left corner of our screen, we will select ‘Data Source’:

The next step is to upload the file that we downloaded from Yahoo Finance. To do this, we search for ‘File Upload’ from the list of Google Studio products. One useful way to do this is to search for ‘File upload’ in the search bar:

Once our file is uploaded, in the upper right corner of our screen, we will click ‘Connect’. This connects our dataset to our new report. We can edit the name of our report at the top left section of our screen then click ‘Create Report’. We select the theme that we would like to use for our report and adjust our layout from the “Theme and Layout” sidebar accordingly.

The navigation bar provides tools to add new pages and rename them. We will add a new page by clicking the “Add a Page” button. We will then click on the drop down arrow, then the triple dots for the options to rename, duplicate or remove the existing page.

Next, to add a chart, we select “Add a Chart” from the navigation bar. We will find that many of these options are intuitive: i.e. ‘Add a chart’ creates a new chart, ‘Add a page’ creates a new page. New metrics and dimensions can be added to our data using the pop-up sidebar to the right of our screen. Here we can manipulate and customize our data. We will only be using one page with two charts, the first chart shows the prices of NFLX stock overtime and the second compares the price and volume of the stock overtime.

With the respective charts selected, we can add the necessary metrics. We will use the Adj Close prices. We first select the chart we want to edit, then from the sidebar, select and drag Adj Close (listed under available fields), to the metrics section of the Data source section. In this case we apply changes to the single metric or one dimensional time series chart.

For the second chart, named Volume, we add Adj Close and Volume to the two dimensional time series chart. Since Volume and Adj Close are on different scale, we will add a secondary axis for Volume, our Series #2 represented by bar charts. We accomplish this under the style tab of the Theme and Layout sidebar. From here we can also change the color of your Series and format your axis.

We have finally completed the groundwork for our dashboard. To add our final touches, we will add titles and logos to our dashboard, using the insert button from the menu bar to upload our own images.

Summary

This brief tutorial on creating a dashboard with Google Data Studio has outlined:

Accessing data from Yahoo Finance

Uploading data to Google Data Studio

Choosing the right visuals

Adding metrics to the dashboard

Formatting the dashboard

I hope this information was helpful. This is my first tutorial so let me know if you have any questions or comments if things are not clear!

Happy Analyzing!