From Data to Insights with Google Cloud Platform (DIGCP)

From Data to Insights with Google Cloud Platform (DIGCP)

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Duration

3 Days

Certified Instructor

Course Id

nextecGC810

Course Description

Course Overview

The From Data to Insights with Google Cloud Platform (DIGCP) course is designed to provide individuals with the knowledge and skills necessary to process, analyze, and visualize data using the Google Cloud Platform (GCP) services. This course focuses on the end-to-end data lifecycle, from data ingestion and storage to data processing and visualization, enabling participants to derive valuable insights from their data using GCP’s powerful data analytics tools.

Prerequisites

To enroll in the DIGCP course, participants should have a basic understanding of data concepts and familiarity with GCP fundamentals. Basic knowledge of SQL and data analysis techniques will be beneficial. Participants should also have access to a GCP project or demo environment to practice the concepts covered in the course.

Methodology

The DIGCP course follows a blended learning approach, combining theoretical instruction, demonstrations, discussions, and hands-on labs. Participants will engage in instructor-led sessions where data processing and analysis concepts, as well as best practices for GCP services, are explained. They will also have access to GCP resources and tools to gain practical experience in working with data analytics workflows. The course encourages active participation, discussions, and collaborative problem-solving to reinforce learning.

Course Outline

Introduction to Data Analytics on GCP

Overview of the data analytics process and GCP’s data analytics services

Understanding the data lifecycle and the role of GCP in each stage

Exploring GCP tools and resources for data analytics

Data Ingestion and Storage

Ingesting data from various sources into GCP using services like Cloud Storage, Pub/Sub, and Dataflow

Storing and organizing data using GCP’s data storage services such as BigQuery, Cloud SQL, and Cloud Spanner

Ensuring data reliability, consistency, and security in GCP

Data Processing and Transformation

Processing and transforming data using GCP’s data processing services such as Dataproc and Dataflow

Utilizing data processing frameworks like Apache Beam and Apache Spark on GCP

Implementing batch and real-time data processing pipelines

Data Analysis and Visualization

Analyzing and querying large datasets with GCP’s BigQuery

Performing advanced analytics using SQL and BigQuery ML

Visualizing data and creating interactive dashboards with Data Studio and Looker

Machine Learning for Data Analytics

Introduction to machine learning concepts and techniques

Applying machine learning algorithms and models to analyze data

Utilizing GCP’s machine learning services such as AutoML and AI Platform

Data Governance and Security

Implementing data governance policies and controls

Ensuring data privacy, compliance, and security in data analytics workflows

Monitoring and auditing data access and usage in GCP

Outcome

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

  • Developed a comprehensive understanding of the data analytics process and GCP’s data analytics services
  • Acquired practical knowledge in ingesting, storing, processing, analyzing, and visualizing data using GCP tools and services
  • Gained expertise in utilizing GCP’s data analytics services for deriving insights from data
  • Learned techniques for data governance, privacy, compliance, and security in GCP
  • Gained hands-on experience through practical labs and exercises
  • Prepared to work with data analytics workflows on GCP and derive valuable insights from data

Labs

The DIGCP course includes hands-on labs that provide participants with practical experience in working with data analytics workflows on GCP. Some examples of lab exercises include:

  • Ingesting data from various sources into GCP using Cloud Storage and Pub/Sub
  • Storing and organizing data in GCP’s data storage services like BigQuery and Cloud SQL
  • Processing and transforming data using GCP’s data processing services like Dataflow
  • Analyzing and querying large datasets with BigQuery
  • Creating interactive dashboards and visualizations using Data Studio or Looker
  • Applying machine learning techniques to analyze data using GCP’s machine learning services

These labs enable participants to apply the concepts learned in the course and gain hands-on experience in working with data analytics workflows on GCP, allowing them to develop practical skills in processing, analyzing, and visualizing data using GCP’s data analytics tools.

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