Overview
This one-day instructor-led course introduces participants to the big data capabilities of Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants get an overview of the Google Cloud platform and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud Platform.
Target Audience
- Data analysts, Data scientists, Business analysts getting started with Google Cloud Platform.
- Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results and creating reports.
- Executives and IT decision makers evaluating Google Cloud Platform for use by data scientists.
Prerequisites
To get the most of out of this course, participants should have:
- Basic proficiency with common query language such as SQL.
- Experience with data modeling, extract, transform, load activities.
- Developing applications using a common programming language such Python.
- Familiarity with machine learning and/or statistics.
Learning Outcomes
- Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform.
- Use Cloud SQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform.
- Employ BigQuery and Cloud Datalab to carry out interactive data analysis.
- Train and use a neural network using TensorFlow.
- Employ ML APIs.
- Choose between different data processing products on the Google Cloud Platform.
Course Outline
Session 1
Welcome
- Facility Logistics
1 Introduction to Google Cloud Platform
- Google Cloud Platform infrastructure and big data products
- Demo: BigQuery Github query
- The different data roles in an organization
- What you can do with GCP
- Activity: Explore a customer use case
- Lab 1: Exploring a Public Dataset with BigQuery
2 Product Recommendations using Cloud SQL and Spark
- Compare Google Cloud Big Data products and services
- Managed Hadoop in the cloud
- Demo: Creating a Cluster
- Your SQL database in the cloud
- Lab 2: Product Recommendation using Cloud SQL and Spark
3 Predicting Visitor Purchases using BigQuery Machine Learning
- Introduction to BigQuery
- Fast SQL Query Engine
- Managed Storage for Datasets
- Demo: Google Sheets to BQ
- Insights from Geographic data
- Demo: BigQuery ML
- Creating ML models with SQL w/BigQuery ML
- Lab 3: Predicting Visitor Purchases BigQuery ML
Session 2
4 Real-time Dashboards with Pub/Sub, Dataflow, and Data Studio
- Introduction
- Message-oriented architectures
- Serverless data pipelines
- Data Visualization w/Data Studio
- Lab 4: Real-time Dashboards with Pub/Sub, Dataflow, and Data Studio
5 Deriving Insights from Unstructured Data using Machine Learning
- Introduction to Machine Learning
- Pre-built ML models
- Demo: Cloud Vision API
- Codeless ML with AutoML
- Lab 5: Classify Images using AutoML
6 Summary
- Recap of lesson
Google Cloud learning paths
Want to boost your career in Google Cloud? Click on the roles below to see QA‘s learning pathways, specially designed to give you the skills to succeed.
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