Data Science on the Google Cloud Platform
Advanced 10 Steps 1 day 60 Credits
This is the first of two Quests of hands-on labs is derived from the exercises from the book Data Science on Google Cloud Platform by Valliappa Lakshmanan, published by O'Reilly Media, Inc. In this first Quest, covering up through chapter 8, you are given the opportunity to practice all aspects of ingestion, preparation, processing, querying, exploring and visualizing data sets using Google Cloud Platform tools and services.
Prerequisites:This Quest assumes you have access to the O’Reilly book Data Science on the Google Cloud Platform as the labs only include the exercises from the end of each chapter and do not contain the concepts or teaching from the text itself. The labs use GCP Services and Tools for data storage, transformation and warehousing, so it is recommended that the student also has earned Badges for the Baseline: Data, ML, and AI and the GCP Essentials Quests before beginning.
In diesem Lab lernen Sie grundlegende SQL-Klauseln kennen und führen praktische Übungen zu strukturierten Abfragen in BigQuery und Cloud SQL aus.
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 Cloud Datalab 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.