From Data to Insights with Google Cloud

By the end of this course, you’ll be able to query and draw insight from millions of records in Google’s BigQuery public datasets. You’ll learn how to assess the quality of your datasets and develop an automated data cleansing pipeline that will output to BigQuery. Lastly, you’ll practice writing and troubleshooting SQL on a real Google Analytics e-commerce dataset to drive marketing insights.

This intermediate course builds upon the previous course in the path. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Google Data Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.

Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. We will dive into optimizing your queries for performance and how you can secure your data through authorized views. Lastly, we define what Machine Learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as building your own Machine Learning models using just SQL with BigQuery ML.