Data Analytics on Google Cloud

Paths

Data Analytics on Google Cloud

Author: Authored by Google Cloud

This path teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and... Read more

What you will learn:

This path teaches the following skills

  • Navigate Google Cloud Platform Project Basics
  • Code High Quality Standard SQL
  • Characterize Dataset Shape and Skew
  • Clean and Transform Data
  • Understand Data Visualization Principles
  • Connect Google Data Studio to Google BigQuery
  • Ingest New Datasets into BigQuery
  • Merge Historical Data Tables
  • Troubleshoot and Solve Data JOIN Pitfalls
  • Use Analytical Window Functions
  • Safeguard Data with One-Way Field Encryption
  • Create Date-Partitioned Tables in BigQuery
  • Leverage Performance Tradeoffs of Normalization vs Denormalization
  • Use Arrays and Nested Data in Google BigQuery
  • Optimize Queries for Performance
  • Optimize Queries for Cost
  • Control Access with Data Security Best Practices
  • Control Access with Authorized Views
  • Understand Feature Selection
  • Derive Insights from Unstructured Data using Prebuilt Machine-Learning models
  • Extract, Analyze, and Translate Text from Images

Pre-requisites

Participants should have some prior experience with ANSI SQL.

Beginner

By the end of this course, you’ll be able to query and draw insight from millions of records in Google’s BigQuery public datasets. You’ll learn how to assess the quality of your datasets and develop an automated data cleansing pipeline that will output to BigQuery. Lastly, you’ll practice writing and troubleshooting SQL on a real Google Analytics e-commerce dataset to drive marketing insights.

Exploring and Preparing your Data with BigQuery

by Authored by Google Cloud

Jun 5, 2020 / 4h 36m

4h 36m

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Description

In this course, we see what the common challenges faced by data analysts are and how to solve them with the big data tools on Google Cloud Platform. You’ll pick up some SQL along the way and become very familiar with using BigQuery and Cloud Dataprep to analyze and transform your datasets.

Table of contents
  1. Welcome to From Data to Insights with Google Cloud Platform - Exploring and Preparing your Data
  2. Introduction to Data on Google Cloud Platform
  3. Big Data Tools Overview
  4. Exploring your Data with SQL
  5. Google BigQuery Pricing
  6. Cleaning and Transforming your Data

Intermediate

This intermediate course builds upon the previous course in the path. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Google Data Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources.

Creating New BigQuery Datasets and Visualizing Insights

by Authored by Google Cloud

Sep 30, 2019 / 2h 37m

2h 37m

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Description

This is the second course in the Data to Insights specialization. Here we will cover how to ingest new external datasets into BigQuery and visualize them with Google Data Studio. We will also cover intermediate SQL concepts like multi-table JOINs and UNIONs which will allow you to analyze data across multiple data sources. Note:Even if you have a background in SQL, there are BigQuery specifics (like handling query cache and table wildcards) that may be new to you.

Table of contents
  1. Introduction
  2. Storing and Exporting Data
  3. Ingesting New Datasets into Google BigQuery
  4. Joining and Merging Datasets
  5. Data Visualization
  6. End of Course Recap

Advanced

Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. We will dive into optimizing your queries for performance and how you can secure your data through authorized views. Lastly, we define what Machine Learning is and how it can benefit your business. You'll see a few demos of ML in action and learn key ML terms like instances, features, and labels. In the interactive labs, you will practice invoking the pretrained ML APIs available as well as building your own Machine Learning models using just SQL with BigQuery ML.

Achieving Advanced Insights with BigQuery

by Authored by Google Cloud

Oct 1, 2019 / 3h 25m

3h 25m

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Description

Achieving Advanced Insights with BigQuery will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views.

Table of contents
  1. Introduction
  2. Advanced Functions and Clauses
  3. Schema Design and Nested Data Structures
  4. More Visualization with Google Data Studio
  5. Optimizing for Performance
  6. Advanced Insights with Cloud Datalab
  7. Data Access
  8. Summary

Applying Machine Learning to your Data with GCP

by Authored by Google Cloud

Oct 1, 2019 / 2h 52m

2h 52m

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Description

Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This accelerated online course teaches participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The course features interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The course covers data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization.

Table of contents
  1. Introduction
  2. Introduction to Machine Learning
  3. Pre-trained ML APIs
  4. Creating ​ML Datasets in BigQuery
  5. Creating ML Models in BigQuery
  6. End of Course Recap
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