W07 — Data Analytics with Google Cloud BigQuery and Looker Studio

Introduction

After completing the LinkedIn Learning course Data Analytics with Google Cloud BigQuery and Looker Studio, I gained a clear understanding of how these two tools work together to transform large datasets into meaningful, visually compelling insights. The instructor used practical examples, such as analyzing global temperature trends, to demonstrate how data can support storytelling and informed decision‑making. Overall, the course emphasized how cloud‑based tools streamline the process of exploring, analyzing, and presenting data.

Exploring Data in BigQuery

The training began with an introduction to navigating the BigQuery interface and accessing public datasets. I learned how datasets are structured, how to preview tables, and how to read a schema before writing any queries. This foundation helped me understand the layout of the data and identify which fields were relevant for analysis.

Running Basic SQL Queries

The course covered essential SQL skills for working with large datasets. I practiced selecting columns, filtering rows, and preparing data for further analysis. Even with large volumes of information, BigQuery processed the queries quickly, demonstrating the efficiency of cloud‑based SQL execution.

Using Aggregation Functions

I learned how to summarize data using aggregation functions such as averages, counts, and sums. These techniques were especially helpful when examining trends over time, including changes in temperature across different periods. The course also reinforced the importance of grouping data correctly to ensure that summaries are accurate and meaningful.

Joining Multiple Tables

Some datasets were divided across multiple tables, so the course introduced how to join them using SQL. This helped me understand how to combine related information and create more complete datasets for analysis. Learning how to perform joins made it easier to work with more complex data sources.

Building Visualizations in Looker Studio

Once the data was prepared in BigQuery, the course shifted to Looker Studio. I learned how to connect BigQuery results to a report and create visualizations such as line charts, bar charts, and scorecards. The instructor demonstrated how to adjust fields, customize chart settings, and format visuals so the insights were clear and easy to interpret.

Creating Interactive Dashboards

The course also showed how to bring multiple visualizations together into a single interactive dashboard. I learned how to add filters, date controls, and other interactive elements that make the dashboard more intuitive for users. This highlighted how Looker Studio can turn raw data into a dynamic reporting tool.

Working with Dimensions & Geo Charts

I gained an understanding of how dimensions influence the structure of charts and how to switch between different visualization types. The course also introduced geo charts, which allowed me to map data across countries and compare trends visually. This added a valuable geographic perspective to the analysis.

Understanding More Complex SQL

The instructor walked through a more advanced SQL query to show how multiple functions, joins, and conditions work together. Breaking down the query step by step helped me better understand how complex data transformations are constructed.

Conclusion Wrap-Up

The course concluded with hands‑on challenges that brought all the concepts together—from querying data in BigQuery to preparing it for visualization and building dashboards in Looker Studio. The final section emphasized the importance of continued practice and highlighted how widely these tools are used in real analytics and reporting environments.

Certification Screenshot