Data Science on the Google Cloud Platform
Advanced 10 Steps 1 day 60 Credits
The Google Cloud Platform provides many different frameworks and options to fit your application’s needs. In this introductory-level quest, you will get plenty of hands-on practice deploying sample applications on Google App Engine. You will also dive into other web application frameworks like Firebase, Wordpress, and Node.js and see firsthand how they can be integrated with GCP.
In this lab you will learn fundamental SQL clauses and will get hands on practice running structured queries on BigQuery and Cloud SQL.
In this lab you'll learn how to use a bash script to download selected data from a large public data set that is available on the internet.
This lab demonstrates how to use local Python scripts to retrieve data from the US Bureau of Transport Statistics website, then modify the data so they can be run using Google App Engine.
In this lab you will import data from CSV text files into Cloud SQL and then carry out some basic data analysis using simple queries.
This lab demonstrates how to use Google Data Studio to visualize data stored in Google Cloud SQL.
In this lab you will simulate a real-time real world data set from a historical data set. This simulated data set will be processed from a set of text files using Python and Google Cloud DataFlow, and the resulting simulated real-time data will be stored in Google BigQuery.
Use Google Dataflow to process real-time streaming data from a real-time real world historical data set, storing the results in Google BigQuery and then using Google Data Studio to visualize real-time geospatial data.
You will learn how to load text data into Google BigQuery and then use that data for rapid exploratory data analysis using Google Cloud Datalab notebooks.
Learn the process of analyzing a data set stored in BigQuery using AI Platform to perform queries and present the data using various statistical plotting techniques.
Learn the process for partitioning a data set into a training set that will be used to develop a model, and a test set that can then be used to evaluate the accuracy of the model and then independently evaluate predictive models in a repeatable manner.