Course Description

Course Overview

The Data-Driven Transformation with Google Cloud (DDTGC) course is designed to provide individuals with the knowledge and skills necessary to drive data-driven transformation initiatives using the Google Cloud Platform (GCP). This course covers key concepts and strategies for leveraging GCP’s data tools and services to enable organizations to make informed decisions and achieve business objectives through data-driven insights.

Prerequisites

To enroll in the DDTGC course, participants should have a basic understanding of data analytics and cloud computing concepts. Familiarity with GCP fundamentals, as covered in the Google Cloud Fundamentals course, is recommended. Prior experience with data analysis, SQL, and statistical concepts will be beneficial for understanding the course material.

Methodology

The DDTGC course employs a combination of theoretical lectures, case studies, discussions, and hands-on labs. Participants will engage in instructor-led sessions where data-driven transformation concepts and best practices are explained. They will also have access to GCP resources and tools to gain practical experience with data analytics and transformation projects. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Data-Driven Transformation

Understanding the importance of data-driven decision making

Overview of the data-driven transformation journey

Key principles and challenges of data-driven transformations

Data Strategy and Governance

Defining data strategy and its role in transformation

Establishing data governance frameworks

Ensuring data quality, privacy, and security

Data Collection and Ingestion

Identifying data sources and collection methods

Designing data ingestion pipelines

Streamlining data integration and ETL processes

Data Storage and Management

Choosing appropriate data storage solutions on GCP

Implementing data warehousing and data lakes

Managing data catalogs and metadata

Data Exploration and Analysis

Leveraging GCP tools for data exploration and analysis

Querying and analyzing data using BigQuery

Utilizing data visualization tools for insights

Machine Learning and Advanced Analytics

Applying machine learning algorithms and techniques

Developing predictive and prescriptive analytics models

Automating decision-making processes with ML

Data-Driven Decision Making and Communication

Transforming insights into actionable decisions

Communicating data findings effectively

Driving data culture and adoption within organizations

Outcome

By the end of the DDTGC course, participants will have:

• Developed a comprehensive understanding of data-driven transformation principles and strategies
• Acquired practical knowledge in leveraging GCP’s data tools and services for analysis and insights
• Gained expertise in data collection, storage, analysis, and visualization on GCP
• Learned best practices for data strategy, governance, and decision-making processes
• Gained hands-on experience through practical labs and exercises
• Prepared to drive data-driven transformation initiatives within their organizations

Labs

The DDTGC course includes hands-on labs that provide participants with practical experience in working with GCP’s data tools and services. Some examples of lab exercises include:

• Designing and implementing data ingestion pipelines using Cloud Pub/Sub and Cloud Dataflow
• Creating and optimizing data storage solutions on GCP (e.g., Cloud Storage, BigQuery)
• Querying and analyzing large datasets with BigQuery
• Utilizing data visualization tools (e.g., Data Studio) to create interactive dashboards
• Applying machine learning algorithms for predictive analytics using AutoML or TensorFlow
• Developing data-driven decision-making frameworks based on case studies

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in leveraging GCP’s data tools and services, allowing them to develop practical skills in driving data-driven transformation within their organizations.