3 simple reasons:
- Data Scientists are hard to find and hire. Want to become one? There’s a lab for that. Enroll in the Data Science on GCP quest by Friday, October 12th and you’ll get 40 credits (free of charge, no CC required). The labs in this quest are derived from the book Data Science on Google Cloud Platform.
- Data analysts with SQL skills (and other programming languages) are also rare. Get practice running SQL queries in the BigQuery console in the Introduction to SQL for BigQuery and Cloud SQL lab. Then experiment with SQL and ML in the Ingest data into the Cloud Using Google App Engine lab. You’ll run Python scripts to download, automatically fetch, and clean data using Google App Engine. You’ll also create a new application and deploy it to the Google App Engine:
Then use a Flask framework to ingest data and invoke it using cron:3. Infrastructure AI is resource-intensive, in terms of both staff, and compute power. Many on-prem setups simply cannot handle the demands of AI. You can learn to take advantage of Google Cloud’s compute power to run your advanced AI jobs. The Google Cloud Dataflow to process data lab shows you how to configure BigQuery and install Python packages to use Apache Beam:
Then you will monitor the progress of your Cloud Dataflow job and inspect the processed data:
Need more practice with the GCP infrastructure? Visualize data with Google Data Studio by running the query to get the IP address to connect to the Cloud SQL:
Then create table views to look at flights that are delayed by 10, 15, 20 minutes:
After connecting with the Data Studio, you’ll create a data visualization for flight delays:
Tackle the quest to practice ingestion, preparation, processing, querying, exploring and visualizing of data sets using GCP. When you earn the badge, let us know. You can find us at @Qwiklabs – and we’re always happy to hear about your accomplishments!