Data Science on Google Cloud
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 tools and services.
注册该挑战任务，系统会全程跟踪进度，直到您赢得徽章为止。
Ingesting Data Into The Cloud
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.
Loading Data into Google Cloud SQL
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.
Visualizing Data with Google Data Studio
This lab demonstrates how to use Google Data Studio to visualize data stored in Google Cloud SQL.
Processing Data with Google Cloud Dataflow
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.
Visualize Real Time Geospatial Data with Google Data Studio
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.
Loading Data into BigQuery for Exploratory Data Analysis
You will learn how to load text data into BigQuery and then use that data for rapid exploratory data analysis using Google Cloud Datalab notebooks.
Exploratory Data Analysis Using AI Platform
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.
Evaluating a Data Model
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.