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Google Cloud for Data Analysts

Course Summary

The Introduction to Google Cloud for Data Analysts training course is designed for beginner data analysts, SQL developers, and software engineers to start using Google Cloud for insightful, scalable, and responsive data analysis and impactful data visualization.

This course will begin with learning about the BigQuery Serverless data warehouse on Google Cloud and understand the advantages of using columnar vs. row-based data stores for effective structured data analysis. After reviewing key SQL concepts for popular data analysis use cases, students will learn how to import data into BigQuery and how to ensure data quality once it has been imported. Next, students will learn how to improve the performance of the data analysis SQL queries by exploring how optimizations for columnar data stores with table partitions differ from traditional normalization/denormalization techniques in relational databases. Students will also learn about more advanced SQL 2011 standard features such as arrays and structs for semi-structured data analysis. The remainder of the course will cover Data Studio for building effective and actionable dashboards based on the results of your analysis. Students will learn how to create and share BigQuery-based reports and to ensure that the reports are shared safely with role-based access control and row-level access security. The course also delves into working with BigQuery ML for predictive analytics to generate experimental regression and classification models with sample datasets.

This course targets beginner data analysts and SQL developers who have some experience with relational databases like Oracle, SQL Server, MySQL, or PostgreSQL. The course will be conducted on the Google Cloud Platform.

Purpose
 Learn how to perform insightful and responsive data analysis at scale and delight the consumers of the analysis with effective data visualizations.
Audience
 Beginner data analysts and SQL developers who have some experience with relational databases like Oracle, SQL Server, MySQL or PostgreSQL.
Role
Business Analyst - Technical Manager
Skill Level
Introduction
Style
Fast Track - Learning Spikes - Workshops
Duration
2 Days
Related Technologies
Google Cloud

 

Productivity Objectives
  • Describe the capabilities of Google Cloud for structured data analytics
  • Adopt basic and advanced SQL capabilities for common data analysis use cases
  • Modify SQL statements to improve their performance and reduce associated costs
  • Build effective visualization dashboards based on visual theory techniques

What You'll Learn:

In the Google Cloud for Data Analysts training course, you'll learn:
  • Google Cloud Fundamentals
  • Why Google Cloud for Data Analysts?
    • Data Analysis Query, Storage, and Infrastructure
    • Managed Virtual Infrastructure vs. Serverless
  • BigQuery
    • Serverless Data Warehousing
    • Columnar vs. Row-Based Data Storage
    • Projects, Datasets, Tables
    • SQL and User Interface
  • SQL Functions
    • Output Formatting
    • Locating and Excluding Duplicate Records
    • Date/Time Analysis
    • Union and Join Tables
    • Approximate Aggregations
    • User-Defined Functions in SQL and JavaScript
  • Data Import/Export
    • Managed Storage
    • Federated Storage
    • Permanent Tables vs Views
    • Integration with Google Cloud
    • Streaming Data Ingest/Import
    • Query Cache vs. Direct Access
  • Data Quality
    • Garbage-In/Garbage-Out Issues
    • Validity, Accuracy, and Consistency
    • Uniformity and Uniqueness
  • Cost Planning
    • Storage and Processing Costs
    • Batch and Streaming Data Costs
    • On-demand vs. Flat rate vs High Compute Plans
    • Cost Optimizations
  • Performance Optimization
    • Data Skew
    • Normalization vs. Denormalization
    • Semi-Structured Data with Columnar Stores
    • Arrays and Structs for Semi-Structured Data
    • Query Performance Analysis
  • Data Studio
    • Data Visualization Basics
    • Effective Visuals
    • Prefetch and Query Cache
    • Report Sharing and Collaboration
  • Predictive Analytics
    • Machine Learning with BigQuery ML
    • Regression vs. Classification
    • Machine Learning Model Training and Evaluation
    • Predictive Analytics with a Trained BigQuery ML Model
  • Security
    • Role-Based Access Control
    • Row-Level / User-Specific Access Control
real world content

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
expert practitioners

Expert Practitioners

Industry experts that bring their battle scars into the classroom.
experiential learning

Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
tailored outlines

Tailored Outlines

One-size-fits-all doesn't apply to training teams. That's where we come in!
“I appreciated the instructor's technique of writing live code examples rather than using fixed slide decks to present the material.”

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