01
Introduction to Data on the Google Cloud Platform
Highlight Analytics Challenges Faced by Data Analysts.
Compare Big Data On-Premise vs on the Cloud.
Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud.
Navigate Google Cloud Platform Project Basics.
Lab: Getting started with Google Cloud Platform.
02
Analyzing Large Datasets with BigQuery
Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools.
Demo: Analyze 10 Billion Records with Google BigQuery.
Explore 9 Fundamental Google BigQuery Features.
Compare GCP Tools for Analysts, Data Scientists, and Data Engineers.
Lab: Exploring Datasets with Google BigQuery.
03
Exploring your Public Dataset with SQL
Compare Common Data Exploration Techniques.
Learn How to Code High Quality Standard SQL.
Explore Google BigQuery Public Datasets.
Lab: Troubleshoot Common SQL Errors.
04
Cleaning and Transforming your Data with Cloud Dataprep
Examine the 5 Principles of Dataset Integrity.
Characterize Dataset Shape and Skew
Clean and Transform Data using SQL
Clean and Transform Data using a new UI:UX
Introducing Cloud Dataprep
05
Visualizing Insights and Creating Scheduled Queries
Overview of Data Vizualization principles
Exploratory and Explanatory analysis approaches
Demo: Comment Data Vizalization pitfalls
Connect Google Data Studio to Google BigQuery.
06
Storing and Ingesting new Datasets
Compare Permanent vs Temporary Tables
Save and Export Query Results
Ingesting new datasets
07
Enriching your Data Warehouse with JOINs
Merge Historical Data Tables with UNION.
Introduce table Wildcards for easy merges
Review Data Schemas: Linking Data Across Mutiple tables
Walkthrough JOIN Example and pitfalls
08
Partitioning your Queries and Tables for Advanced Insights
Introduce Advanced Functions (Statistical, Analytic, User-Defined)
Date-Partitioned Tables
09
Designing Schemas that Scale: Arrays and Structs in BigQuery
Compare Google BigQuery vs Traditional
Relational Data Architecture.
Demo: Nested and Repeated Fields
Review ARRAY and STRUCT syntax.
BigQuery Architecture.
10
Optimizing Queries for Performance
Avoid BigQuery Performance Pitfalls
Prevent Hotspots in your Data
Diagnose Performance Issues with the Query
Explanation map
11
Controlling Access with Data Security Best Practices
Safeguard Data with One-way Field Encryption.
Creating Authorized Views
Compare IAM and BigQuery Dataset Roles.
Avoid Access Pitfalls
Review Members, Roles, Organiztions, Account Administration and Service Accounts
12
Predicting Visitor Return Purchases with BigQuery ML
Machine Learning on Structured Data
Scenario: Predicting Customer Lifetime Value.
Choosing the right model type.
Creating ML models with SQL
13
Deriving Insights from Unstructured Data using Machine Learning
ML drives business value
How does ML on unstructured data work?
Choosing the right ML approach.
Pre-built AI building blocks.
Using Pre-built AI to create a chatbot.
Customizing Pre-built models with AutoML.
Building a custom model
Trần Tuấn Anh
IT
Nguyễn Ngọc Minh Thy
Data Engineer
Trương Quốc Thắng
Data Engineer
Phạm Văn Hùng
IT
Dương Minh Phương
Engineer
Cloud Ace is a Google Cloud training unit, so it does not organize exams and provide Google Cloud certifications. Cloud Ace only supports providing certificates of course completion for students while waiting for the Google Cloud certification exam
In addition, if you want to take the Google Cloud certification exam, Cloud Ace will guide you to register for the Online or Offline exam at the authorized Google Cloud test centers in Vietnam.
Of course, during the learning process, you will constantly be solving quizzes, simulated mock tests that are similar to Google Cloud's actual exam questions. In addition, Cloud Ace also provides Dump questions that are constantly updated with question types, exam questions from Google Cloud to help you have the best preparation for the exam.
Of course. You will be supported by Cloud Ace during the learning process and even at the end of the course. You can interact with the Trainer via Slack, email hoặc qua Group Google Cloud Plartform User HCM để được các Trainer hỗ trợ nhé.
After completing the course, if you have any questions about the knowledge or have difficulties in implementing the project on Google Cloud, you can contact the Trainer for answers.
The Google Cloud course is not only suitable for software engineers or system development engineers, but also suitable for data processing engineers such as Data Analytics, Data Engineer, Data Scientist.
In addition, if you are a Marketer or working in the field of finance, banking, e-commerce, logistics .... constantly faced with big data to solve, then you can refer to the courses Big Data Machine Learning Fundamental or From Data to Insight on Google Cloud Platform courses to refer to simple data processing and create professional reports on Google Cloud.
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