NCAA® March Madness®: Bracketology with Google Cloud

NCAA® March Madness®: Bracketology with Google Cloud

Google BDL-NCAAGC

USD 39.60
In this series of labs you will learn how to use BigQuery to analyze NCAA basketball data with SQL. Build a Machine Learning Model to predict the outcomes of NCAA March Madness basketball tournament games.
 
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USD 39.60

Using BigQuery in the Google Cloud Console

Module 1

Choice between: Using BigQuery in the Google Cloud Console or BigQuery: Qwik Start - Command Line

Using BigQuery in the Google Cloud Console

Google LIS-GOOGLE-2224
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
This lab shows you how to query public tables and load sample data into BigQuery using the GCP Console. Watch the following short video Get Meaningful Insights with Google BigQuery.
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BigQuery: Qwik Start - Command Line

Google LIS-GOOGLE-2020
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days

Storing and querying massive datasets can be time consuming and expensive without the right hardware and infrastructure. BigQuery is a serverless, highly scalable cloud data warehouse that solves this problem by enabling super-fast SQL queries using the processing power of Google's infrastructure. Simply move your data into BigQuery and let us handle the hard work. You can control access to both the project and your data based on your business needs, such as giving others the ability to view or query your data.

You can access BigQuery by using the Console, Web UI or a command-line tool using a variety of client libraries such as Java, .NET, or Python. There are also a variety of solution providers that you can use to interact with BigQuery.

This hands-on lab shows you how to use bq, the python-based command line tool for BigQuery, to query public tables and load sample data into BigQuery.

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Introduction to SQL for BigQuery and Cloud SQL

Module 2

Introduction to SQL for BigQuery and Cloud SQL

Introduction to SQL for BigQuery and Cloud SQL

Google LIS-GOOGLE-2194
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days

SQL (Structured Query Language) is a standard language for data operations that allows you to ask questions and get insights from structured datasets. It's commonly used in database management and allows you to perform tasks like transaction record writing into relational databases and petabyte-scale data analysis.

This lab serves as an introduction to SQL and is intended to prepare you for the many labs and quests in Qwiklabs on data science topics. This lab is divided into two parts: in the first half, you will learn fundamental SQL querying keywords, which you will run in the BigQuery console on a public dataset that contains information on London bikeshares.

In the second half, you will learn how to export subsets of the London bikeshare dataset into CSV files, which you will then upload to Cloud SQL. From there you will learn how to use Cloud SQL to create and manage databases and tables. Towards the end, you will get hands-on practice with additional SQL keywords that manipulate and edit data.

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Exploring NCAA Data with BigQuery

Module 3

Exploring NCAA Data with BigQuery

Exploring NCAA Data with BigQuery

Google LIS-GOOGLE-2074
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
Use BigQuery to explore the NCAA dataset of basketball games, teams, and players. The data covers plays from 2009 and scores from 1996. Watch How the NCAA is using Google Cloud to tap into decades of sports data.
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Bracketology with Google Machine Learning

Module 4

Bracketology with Google Machine Learning

Bracketology with Google Machine Learning

Google LIS-GOOGLE-2237
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
In this lab you use Machine Learning (ML) to analyze the public NCAA dataset and predict NCAA tournament brackets.
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