In this beginner level Looker Studio Course, we will quickly cover the basics of Looker Studio and get familiar with the user interface and then we’ll see a high-level overview of most key features of Looker Studio through building a simple eCommerce dashboard based on Google Analytics data from scratch.

Today's mission is twofold:

In just 60 to 90 minutes, we'll construct a complete dashboard side by side. Buckle up, and let's get started!

In this chapter, we will learn Looker Studio’s chart toolbox to see what options we have available to us to visualize data.

Our goal is to become experts in Looker Studio and data visualization. Then, we can successfully create web analytics, marketing, and business intelligence dashboards to drive our business forward. Today, we dive into the second part of our Looker Studio Masterclass: Chart Types Overview.

In the chapter, we created an e-commerce dashboard from scratch in just an hour. We quickly explored almost all of Looker Studio's features. Today, we'll take a closer look at the available chart types and visualizations.

In the following chapters we'll cover dashboard planning, data connectors, data modeling, in-depth data visualization, creating beautiful reports, interactions, sharing security, managing reports with clients, and BigQuery. Stay tuned!

We'll discover Looker Studio's built-in charts, their capabilities, and key features. We'll also learn when to use each chart type for data visualization.

To create a useful reporting dashboard for our report viewers, we must plan its structure beforehand. Dashboard planning is thus essential for successful and efficient implementation.

In this chapter, we will discuss how information hierarchy applicable to any business can be mapped to our dashboard’s hierarchy. This will enable us to plan and structure our dashboards more effectively.

In the 2nd part of this chapter, we’ll take a look at different ways of bringing in metrics and dimensions from different sources (Google Ads, Google Analytics, GA4, Google Sheets, Google Cloud BigQuery, 3rd Party marketing & analytics softwares, SQL databases, etc.).

When creating a dashboard, it's tempting to dive right in and start building. But if you want a truly useful and valuable dashboard, you should plan it in advance.

To plan a dashboard, you need to understand two concepts: information hierarchy and dashboard hierarchy. We'll look at how information is structured in a client's business and how a dashboard in Looker Studio is organized.

When creating a dashboard, you usually have a main subject in mind, for example, website engagement.

Within this subject, you have different topics, such as video engagement, scroll depth, and page visits. For each topic, you'll want to present data that answers specific questions. For example, for video engagement, you might ask:

The information hierarchy consists of Subject, Topic, Question, and Answer (STQA).

Now, let's map this information hierarchy to a Looker Studio dashboard.

Use pages to declutter your dashboard and present one topic at a time. Create sections within pages to answer different types of questions about that topic. Finally, use charts within sections to answer specific questions.

Let's say we're asked to create a Website Usability and Engagement Report. We can divide this subject into three topics: Engagement, Engagement by Source, and 404 Pages.

Under Engagement, we'll have sections for Video Engagement and Scroll Engagement. Under Engagement by Source, we'll have a section for Micro Goals (engagement by traffic source). Under 404 Pages, we'll have sections for Total and Trend, and By URL.

For each section, design charts to answer the questions you've identified. For example, in the 404 Pages topic, we could have a scorecard with conditional formatting to show the number of broken pages and how bad the situation is, and a time series chart to show the trend.

To create charts, you need data. In the next part of this lesson, we'll talk about providing data to charts using data connectors.

After learning the dashboard planning framework and understanding data connectors and data sources, we can now model (or transform) our data before visualization.

In this chapter we will cover Data Modeling: how to shape, process, and clean data in data sources before visualizing and sharing it with your team, clients, or other users.

We learned in the previous lessons that the first place we can perform data modeling is at the source (the tool), such as setting a goal in Google Analytics or labeling campaigns in an Ad platform. And the second place we can perform data modeling on is at the Data Connector level.

Modeling data at the source (the tool) and within the data connector (such as how it can be done in Dataddo, Supermetrics, or Funnel.io) are out of the scope of this course. Mainly because those are specific to the tool being used, and not the core functionalities of Looker Studio.

In this chapter we will cover the third place where we can transform data to make it ready for visualization in Looker Studio, which is at the Data Source level.

It’s fundamental to understand this concept: At its core, every chart or visualization is a table, with rows, and columns. It doesn’t matter it’s a scorecard, an area chart, or a fancy scatter plot: at the core, all charts are tables (or their underlying dataset can be represented by a table).

When we perform data modeling, we do it while presenting data as a table. We will cover why in the next lessons.

In this chapter we will study another data modeling fundamental: Working with Text.

This is useful especially since half of the data we’re going to deal with are in text format, the other half being numbers.

We’ve already talked about data sources, aggregation, working with numbers, scope of custom fields, and working with text.

In this chapter we will discuss how we can use the CASE and IF() conditional functions to group and categorize data so we can attain how we want to present and visualize our data in our reports.

We will discuss the most common and useful applications of the CASE function especially when working with web tracking data.

In this first lesson we will tackle how we can nest the IF() function to text functions to categorize metrics or dimensions into two categories.

In our data modeling journey, we've covered data sources, aggregation methods, mathematical formulas, custom field scopes, text processing, and regular expressions.

Now, it's time to dive into grouping and categorizing data using conditional functions.

In this lesson, we'll explore how to group data similarly to how Google Analytics groups channels, breaking them down into categories we understand based on our business logic. We'll be using the IF() function to achieve this.

Remember that every chart in Looker Studio is a table. We're always working with tables, and when we're happy with the results, we connect them to charts.

While grouping and categorizing data, we'll be creating custom fields or groups using conditional functions.

There are two main conditional functions in Looker Studio to help us group and categorize data: the IF() function and the CASE function. We'll use the IF() function for simple use cases with only two options or groups, while the CASE function will be used for more complex scenarios.

Let's say we previously used the REPLACE function to replace the top-level domain in a hostname and extract the subdomain. Instead of just returning the value of the subdomain, such as "shop" or "www," we want to report the actual value in the reporting interface.

For example, if the website uses "s" for "shop" or "a," "b," "c" for departments, we want to report the actual names. In this case, we can use the IF() function to return the desired result based on a condition.

In this chapter we will cover the best practices in data visualization and will learn how we can be more effective when it comes to data storytelling in Looker Studio (formerly Google Data Studio).

In the previous lessons we tackled best practices when it comes to interactive data visualization: storytelling, adding context, communicating status, using colors, avoiding distraction, and how to tell not show.

In this chapter we will learn the ways to style like a pro. We will see what tools we have at our disposal to efficiently implement and complete our vision when visualizing data.

Remember that the goal of data visualization is to communicate information clearly and effectively. Ensure that your visualizations convey the message you want to present. Guiding your viewer through the story helps them understand the data and derive insights more easily.

Colors play a significant role in data visualization. Use them wisely and consistently to enhance your viewer's understanding. For example, apply conditional formatting with red, green, and yellow to indicate negative, positive, and warning statuses, respectively. By doing so, you can draw attention to the most important aspects of your data.

Keep your visualizations clean and straightforward by reducing visual clutter. Focus on increasing the data-ink ratio – the proportion of ink used to display the data compared to the total ink used in the graphic. Simplify your charts by removing unnecessary elements and highlighting the most important information.

To make your visualizations stand out, it's crucial to become proficient with the tools you have at hand: to familiarize yourself with the features and functionalities of Looker Studio. The more comfortable you are with the tools, the more efficient and creative you can be in bringing your vision to life.

We have four main sections today:

Let’s begin!

Data blending is a powerful technique that helps you merge tables and combine data from multiple sources into a single, unified dataset. In this chapter, we'll explore the foundations of data blending and why it matters for your Looker Studio (Google Data Studio) projects.

Understanding the "why" behind data blending can help you appreciate its potential for your data modeling projects. Let's dive into some use cases to see the different end results you can achieve with data blending:

One of the primary benefits of data blending is the ability to combine fields from different data sources on a single chart. For instance, you might want to show costs from Facebook Ads, Google Ads, and Microsoft Ads on one chart to get a comprehensive view of your marketing spend across platforms.

You can also use data blending to aggregate data from the same marketing tool but for different accounts. For example, you might want to see a roll-up chart displaying summarized data from multiple Google Ads accounts for all your clients.

Data blending allows you to de-aggregate and re-aggregate data, which is often the only way to achieve the desired results. We'll see a live example of this later in the session.

Data blending enables you to create calculated fields using data from different sources. For example, you might want to calculate the conversion rate using metrics from multiple data sources.

Data enrichment and widening are useful techniques for adding more context to your dataset. For instance, you can enrich your data by looking up the profit margin for each SKU on an e-commerce site or by adding population data for a city. In both cases, you're "widening" your dataset by adding more columns of information.

In the previous lessons we discussed the foundations of data blending: what data blending is and how it works behind the scenes mainly using the Left Outer Join Type.

We learned how useful data blending can be for us to bring up to five data sources (e.g. Google Analytics, Google Sheets, Facebook Ads data source, etc.) together into a single, blended data source so we can visualize it on a chart.

In this chapter we will learn about the other join types aside from Left Outer Join.

Remember, in Looker Studio, we can only use one data source per chart. However, we can blend data sources based on specific criteria and join keys, essentially creating a single blended data source. This allows us to connect multiple data sources to a single chart.

Initially, data blending had some limitations:

While the limitation of blending up to five data sources still exists, we now have access to multiple join types and keys across different joins.

In addition to the left outer join, Looker Studio now offers right, inner, full outer, and cross join types. This provides greater flexibility in how you can mix and match different join types between tables. For example, you might want a left join between the first and second tables, a full outer join between the second and third tables, and a cross join for the final table.

Venn diagrams are often used to illustrate different join types. These overlapping circles show how data from different tables is combined based on the join type being used.

Let's take a closer look at the various join types in Looker Studio and how they can be applied in real-life scenarios.

In this chapter we will talk about Data Control: the application of Filters and Controls in reports.

Filters limit the data shown in a report based on a dimension or metric.

We’ll also discuss the different functions of filters, where they can be applied, the conditions used, as well as restrictions between editors and viewers.

Controls allow report viewers to limit the values shown in a chart based on different categories of a dimension, similar to filters.

Controls differ from filters since some controls can enable the report editors to gather user input for parameters to be used in calculated fields.

Controls also have different functions that can affect the dataset, time frame, and parameter values.

Welcome to an exciting lesson about filters and controls in Looker Studio. These powerful tools are essential when creating and working with reports. So let's dive in and learn how they work.

First, let's understand the distinction between filters and controls. Filters are for the report editor, allowing you to decide what to apply. You can filter based on dimensions or metrics. On the other hand, controls are for the viewer and include data control, date range control, filter controls, advanced filter control, and chart cross filters.

Filters allow you to limit the data shown on a chart. You can apply them to a chart, control, page, or the entire report. Keep in mind that report viewers cannot change filters, so you'll need to inform them about the filters applied.

Filters do not transform data, they only limit it. You can include or exclude dimension values, apply filters with more than one condition, and have multiple filters applied to a single chart. Filters are linked to a data source.

Let's start with filtering a chart based on dimension values. First, select the chart and look at the data property in the sidebar. Scroll down to find the "table filter" section and click to add a filter.

Name your filter descriptively, then decide whether you want to include or exclude certain values. For example, you might want to exclude rows where the source medium contains "not set."

Once you've set up your filter, save it. The chart should now only display the data you want to see. Keep in mind that the order of the filters does not matter, as they are all applied on top of each other.

You can also apply multiple criteria to a filter. For example, if you want to include data where the device is either desktop or mobile, use the "OR" operator. If you need to include or exclude more than ten values, use the "IN" operator followed by a list of values separated by commas.

Filters can also be applied to scorecards, which don't have dimensions. Simply add a filter and choose from the ones you've already created. The numbers on the scorecard should change accordingly.

In this chapter we are going to cover a Looker Studio concept that falls under data modeling and report interaction: parameters.

Filters and controls do not model data or change the underlying calculation. This is where parameters come into play.

Parameters allow users to provide data to the calculated fields and charts.

We will cover in the following lessons how we can use controls to work with parameters to enable our report viewers to have an input in the way we transform data in Looker Studio.

Parameters can be used in:

With above use cases, we see how parameters are flexible, powerful, and open-ended.

In our last session, we explored filters and controls, which allowed us to narrow down the information presented in our reports based on certain dimension values or metric ranges. We learned that filters are designed for editors and controls for viewers. Both perform the same functionality, but now we'll dive into controls and their relationship with parameters.

Parameters are an exciting addition to Looker Studio. For the first time, we can offer users the ability to influence how data is transformed and calculated.

This means the viewer of the report can have their input on how they want those calculations to be performed.

In Looker Studio, we can connect to external data sources and collect values from users using parameters. Parameters allow us to also collect data and provide it to calculated fields and charts, making them more relevant and useful to the viewer.

For example, let's say we have a metric coming from a data source like Google Analytics, BigQuery, or Google Sheets. We can combine this metric with a value provided by the viewer, perform some calculation, and present the result in a chart. This chart now has aspects that are customized by the viewer, making it more relevant and useful to them.

Imagine a scenario where a user wants to forecast revenue for the next quarter. They could provide a parameter (let's call it X) to adjust the forecast based on their expectations, such as doubling the revenue or increasing it by 20%. This way, users can directly influence the calculations in their reports.

Parameters provide a level of customization and interactivity that hasn't been available in Looker Studio before. By allowing users to influence the underlying calculations, we can create more meaningful and personalized reports that cater to our clients’ unique needs and preferences.

In the next lessons, we'll dive deeper into parameters, advanced features, explore how to create them, and learn how to use them in our data visualizations.

In the previous lessons we learned about parameters and how to use them in Looker Studio.

In this chapter we will talk about Advanced Control and Parameters.

In particular, we will discuss:

We will also cover Chart Interactions, Metric Sliders, Optional Metrics, Dimension Drill Down, and lastly, Custom Link Navigation.

Filtering in Looker Studio works only across data sources of the same type, meaning those created by the same data connector. For example, if you have two different Google Analytics data sources from two different domains, they can be filtered together since they were both created using the same connector.

Suppose you have four data sources: two Google Analytics data sources, a Google Sheet, and a Google Ads source. All of them have a field called "country" (or something similar like "country/territory"). As humans, we understand that these fields represent the same information. However, controls in Looker Studio cannot filter charts based on different data source types.

Imagine a control (in yellow) that's connected to a Google Analytics data source. If you filter by "United States," the charts based on Google Analytics data sources will refresh to display data only from the United States. However, the charts based on the Google Sheet and Google Ads data sources will not change. They cannot be cross-filtered since they come from different data connectors.

The same applies to filtering by the Google Sheet or Google Ads data sources - only the corresponding charts will be affected.

There might be instances where you want to force Looker Studio to filter across different data sources, even though it's not designed to do so. In the next lesson, we will explore a workaround to help you achieve this.

In this chapter, we'll explore working with date fields in Looker Studio and discover how to manipulate and utilize them effectively in our working reports. We'll then delve into the "Extract Data" connector, understanding its use cases and benefits, and finish by learning how to embed external content within Looker Studio dashboards. Follow along as we navigate through these essential topics in Looker Studio.

In this first part, we’ll discuss Working with Dates. We’ll take a look at the different fields of date types, different ways we can work with dates, as well as the different date functions and their use cases.

Date and time fields are commonplace in reporting as they typically represent vital data points for analysis. As such, understanding how to work with date fields and how they interact with reporting components, such as a date range selector, is essential.

In this lesson, we work with a dataset comprising user IDs, the date users become leads, and the date users become paid members. The goal is to calculate the number of days it takes for a user to convert from a lead to a paid member, as well as the conversion rate per week. Let's begin by learning about the DATE_DIFF function.

The DATE_DIFF function calculates the difference in days between two date fields. By simply passing two date fields to this function, you can quickly determine the number of days between these dates. In our live example, we used the following formula to obtain the desired result:

In this chapter we’ll talk about the various options we have when it comes to Sharing, Tracking, Security, and Management of Looker Studio reports.

In particular, we will discuss:

Welcome to this tutorial where we'll dive into the core aspects of sharing, tracking, and management of reports in Looker Studio.

Your reports are ready, gleaming with insights, just waiting to be shared. Your audience might be clients, team members, or perhaps the world. So what's the best way to share?

We'll consider the various options at your disposal. We'll weigh the pros and cons and help you identify the most effective sharing strategy for each scenario.

An important facet of sharing is access control. We'll discuss how to restrict the level of access for your viewers and editors. Remember, you have the power to determine how much they can see or edit.

Looker Studio isn't just about creating reports and sharing them. It goes above and beyond with features such as presentation mode and role-level security. We'll delve into these features and see how they can save you considerable time and effort, based on the specifics of each case.

It's crucial to understand the usage of your reports. Who's viewing them? How often? Are they even being used? You can keep track of all these factors in Looker Studio.

But wait, there's more. You can also audit your reports. You can understand who's accessing or editing them, whether anything has been altered. You can even receive alerts about these changes. We'll explore these functions in detail.

Finally, we'll investigate report publishing settings and version history management. These are especially useful when you're actively working on a report, whether to add new pages, incorporate additional functionalities, or correct any issues.

Time to roll up our sleeves and immerse ourselves in the vibrant world of Looker Studio. We have an exciting journey ahead!

In this chapter we’ll cover an Introduction to BigQuery: What BigQuery is, when we should or should not use this tool, and the limitations of the current data-stack in Looker Studio.

We’ll also cover how we can bring in and work with data in BigQuery, as well as how to connect it to Looker Studio, and finally, we’ll take a look at some case studies on how BigQuery is used.

Welcome to the advanced Looker Studio course, where we'll dive into the world of BigQuery. This lesson will help you understand what BigQuery is, why it's interesting, and how it might fit into your business. By the end of this lesson, you'll be equipped with the knowledge to decide whether or not BigQuery is right for you and your organization.

Before we jump into the specifics of BigQuery, let's discuss why you might want to consider it in the first place. You may have heard about others using it and wondered if it's worth exploring for your own purposes.

Every data stack has its limitations. Whether you're currently using Looker Studio or another tool, understanding these limitations can help you determine if BigQuery could be a valuable addition to your arsenal.

BigQuery is a powerful tool that can help overcome some of these limitations. In this section, we'll explore what makes BigQuery unique and how it can benefit your data analysis process.

Not every situation calls for using BigQuery. In this part of the lesson, we'll discuss when incorporating this tool might make sense for your business needs.

If you decide that utilizing BigQuery is a good move for your organization, you'll need to bring your data into it so that you can work with it effectively. We'll cover various methods for importing data into this platform.

Once your data is inside BigQuery, there are numerous ways to manipulate and analyze it. We'll go over some key techniques for working with data within this environment.

To get even more value from using both tools together, we'll discuss how to connect Looker Studio to BigQuery. This connection will enable you to take full advantage of the capabilities offered by both platforms.

Finally, we'll explore some real-life case studies that demonstrate the power and potential of combining Looker Studio and BigQuery. By examining these examples, you'll gain a better understanding of how these tools can work together to create valuable insights for your business.