Certification In Data And Analytics Using Google Cloud Gcp

Learn AI model training using Vertex AI, Data Storage Solutions , Big Data Processing with BigQuery, ETL, ML using GCP

What you'll learn

You will learn about the Introduction to Google Cloud Platform (GCP), including the core benefits of cloud computing with GCP, its major services, and key use.

You will also explore how GCP supports modern data science, machine learning, and business analytics workflows

You will explore Data Storage Solutions in GCP, including the different storage types such as Cloud Storage, Cloud SQL, Firestore, and Bigtable

You will learn how to choose the right storage service based on workload and data type, implement best practices

You will get introduced to Big Data Processing with BigQuery, Google’s powerful serverless analytics platform

You will understand the big data workflow in BigQuery, optimize query performance, and handle structured and semi-structured data.

You will learn how to build Data Integration and ETL Pipelines on GCP. You will study the components of ETL workflows

Explore data ingestion strategies using tools like Cloud Dataflow and Cloud Pub/Sub, and implement real-time and batch processing

You will explore Data Visualization and Business Intelligence (BI) in GCP using tools such as Looker and Data Studio.

You will learn about different types of visualizations, the BI process, and how to design interactive dashboards

You will gain practical knowledge on Machine Learning with GCP, including how to prepare data, train models using Vertex AI

You will explore pre-trained models and AutoML for faster experimentation. Hands-on tasks include training and evaluating a machine learning model

You will understand MLOps and Workflow Automation on GCP, focusing on continuous integration and deployment (CI/CD)

You will study tools like TFX, Cloud Build, and Vertex Pipelines, and examine case studies that demonstrate successful MLOps implementations

You will study Security and Governance in GCP, including IAM (Identity and Access Management), data encryption, network security

You will learn how to protect sensitive data and follow best practices for governance in analytics and ML projects. Hands-on tasks include setting up IAM roles,

Requirements

You should have an interest in data science, machine learning, and cloud technologies

A desire to learn how to store, manage, analyze, and visualize data

Interest in applying machine learning models to real-world scenarios

Description

DescriptionTake the next step in your cloud-powered AI and data analytics journey! Whether you're an aspiring data scientist, ML engineer, developer, or business decision-maker, this course will equip you with the skills to leverage Google Cloud Platform (GCP) for scalable, real-world data science and machine learning solutions. Discover how services like BigQuery, Vertex AI, Cloud Storage, and Looker are driving innovation across industries through intelligent insights, automation, and predictive capabilities.Guided by hands-on labs and real-world use cases, you will:• Master the fundamentals of cloud computing, big data workflows, and machine learning using GCP services.• Gain hands-on experience managing and analyzing data with BigQuery, Cloud Storage, Cloud SQL, and Dataflow.• Learn to train, optimize, and deploy ML models using Vertex AI, AutoML, and TensorFlow/PyTorch in GCP.• Explore practical applications across sectors such as retail, healthcare, manufacturing, and media using GCP’s AI/ML tools.• Understand security, compliance, and cost management best practices in cloud-based data science projects.• Position yourself for future-ready careers by mastering high-demand skills at the intersection of cloud computing, AI, and big data analytics.The Frameworks of the Course• Engaging video lectures, case studies, real-world projects, downloadable resources, and interactive exercises—designed to help you deeply understand how to leverage Google Cloud Platform (GCP) for data analytics, machine learning, and cloud-based solutions.• The course includes domain-specific case studies, GCP-native tools, reference guides, quizzes, self-paced assessments, and hands-on labs to strengthen your ability to build, manage, and deploy ML models using GCP services.• In the first part of the course, you’ll learn the fundamentals of cloud computing, GCP services, and how Google Cloud supports scalable and intelligent data workflows.• In the middle part of the course, you will gain hands-on experience with tools like BigQuery, Cloud Storage, Cloud SQL, and Vertex AI to build ETL pipelines, analyze big data, and train machine learning models.• In the final part of the course, you will explore model deployment, MLOps automation, data governance, security best practices, and real-world use cases across sectors. All your queries will be addressed within 48 hours with full support throughout your learning journey.Course Content:Part 1Introduction and Study Plan· Introduction and know your instructor· Study Plan and Structure of the CourseModule 1. What is GCP1.1. Key Benefits of GCP1.2. GCP Core Services1.3. GCP Use Cases1.4. Getting Started with GCP1.5. Next Steps - Deploy your first virtual machine, Store and retrieve data with Cloud Storage, Train and AI model using Vertex AI1.6. Conclusion of What is GCPModule 2. Data Storage Solutions in Google Cloud Platform2.1. Types of Data Storage Solutions in GCP2.2. Choosing the Right Storage Solution in GCP2.3. Best Practices for Data Storage in GCP2.4. Next Steps - Explore Cloud Storage for storing unstructured data, Use BigQuery for Data Analytics, Deploy a Cloud SQL Database for your application2.5. Conclusion of Data Storage Solutions in Google Cloud Platform (GCP)Module 3. Big Data Processing with BigQuery3.1. Big Data Processing Features in BigQuery3.2. Big Data Processing Workflow in BigQuery3.3. Real World Use Cases for Big Data Processing in BigQuery3.4. Best Practices for Big Data Processing in BigQuery3.5. Next Steps - Get Hands- On with BigQuery3.6. Conclusion of Big Data Processing with BigQueryModule 4. Data Integration and ETL Pipelines4.1. Components of an ETL Pipeline4.2. Data Integration Approaches4.3. Best Practices for Building ETL Pipelines4.4. Real - World Use Cases for ETL Pipelines4.5. Next Steps - Build an ETL Pipeline4.6. Conclusion of Data Integration and ETL PipelinesModule 5. Data Visualization and Business Intelligence5.1. Example - Creating a Bar Chart in Python (Matplotlib)5.2. Types of Data Visualization5.3. Business Intelligence (BI) Process5.4. Creating Dashboards in BI Tools5.5. Real-World Use Cases of Data Visualization and BI5.6. Next Steps - Build Your Own BI Dashboard5.7. Conclusion of Data Visualization and Business IntelligenceModule 6. Machine Learning with Google Cloud Platform (GCP)6.1. Data Preparation for ML in GCP6.2. Training ML Models on GCP6.3. Deploying ML Models on GCP6.4. Real-World Use Cases of ML on GCP6.5. Hands - on ML Project on GCP6.6. Conclusion of Machine Learning with Google Cloud PlatformModule 7. MLOps and Workflow Automation7.1. MLOps Workflow and Pipeline Automation7.2. Tools for MLOps and Workflow Automation7.3. Continuous Integration and Deployment (CI CD) in MLOps7.4. Model Monitoring and Drift Detection7.5. Real-World MLOps Case Studies7.6. Hands-on MLOps Project - Automating a Customer Churn Prediction Model7.7. Conclusion of MLOps and Workflow AutomationModule 8. Security and Governance in Google Cloud Platform (GCP) Analytics8.1. Identity and Access Management (IAM) in GCP8.2. Data Security and Encryption8.3. Network Security in GCP8.4. Compliance and Audit Logging8.5.Threat Detection and Monitoring8.6.Governance Best Practices in GCP Analytics8.7.Conclusion of Security and Governance in Google Cloud Platform (GCP)Module 9. Real-World Use Cases and Applications Using Google Cloud Platform (GCP)9.1. Data Analytics and Business Intelligence9.2. Machine Learning and AI Solutions9.3. Real-Time Data Processing and IoT9.4. Cloud - Based Applications and DevOps9.5. Security and Compliance9.6. Healthcare and Life Sciences9.7. Media and Entertainment9.8.Conclusion - Unlocking the Power of GCPPart 2Capstone Project.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Module 1. What is GCP

Lecture 3 1.1. Key Benefits of GCP

Lecture 4 1.2. GCP Core Services

Lecture 5 1.3. GCP Use Cases

Lecture 6 1.4. Getting Started with GCP

Lecture 7 1.5. Deploy your first virtual machine, Train AI model using Vertex AI

Lecture 8 1.6. Conclusion of What is GCP

Section 2: Module 2. Data Storage Solutions in Google Cloud Platform

Lecture 9 Module 2. Data Storage Solutions in Google Cloud Platform

Lecture 10 2.1. Types of Data Storage Solutions in GCP

Lecture 11 2.2. Choosing the Right Storage Solution in GCP

Lecture 12 2.3. Best Practices for Data Storage in GCP

Lecture 13 2.4. Next Steps - Explore Cloud Storage for storing unstructured data

Lecture 14 2.5. Conclusion of Data Storage Solutions in Google Cloud Platform (GCP)

Section 3: Module 3. Big Data Processing with BigQuery

Lecture 15 Module 3. Big Data Processing with BigQuery

Lecture 16 3.1. Big Data Processing Features in BigQuery

Lecture 17 3.2. Big Data Processing Workflow in BigQuery

Lecture 18 3.3. Real World Use Cases for Big Data Processing in BigQuery

Lecture 19 3.4. Best Practices for Big Data Processing in BigQuery

Lecture 20 3.5. Next Steps - Get Hands- On with BigQuery

Lecture 21 3.6. Conclusion of Big Data Processing with BigQuery

Section 4: Module 4. Data Integration and ETL Pipelines

Lecture 22 Module 4. Data Integration and ETL Pipelines

Lecture 23 4.1. Components of an ETL Pipeline

Lecture 24 4.2. Data Integration Approaches

Lecture 25 4.3. Best Practices for Building ETL Pipelines

Lecture 26 4.4. Real - World Use Cases for ETL Pipelines

Lecture 27 4.5. Next Steps - Build an ETL Pipeline

Lecture 28 4.6. Conclusion of Data Integration and ETL Pipelines

Section 5: Module 5. Data Visualization and Business Intelligence

Lecture 29 Module 5. Data Visualization and Business Intelligence

Lecture 30 5.1. Example - Creating a Bar Chart in Python (Matplotlib)

Lecture 31 5.2. Types of Data Visualization

Lecture 32 5.3. Business Intelligence (BI) Process

Lecture 33 5.4. Creating Dashboards in BI Tools

Lecture 34 5.5. Real-World Use Cases of Data Visualization and BI

Lecture 35 5.6. Next Steps - Build Your Own BI Dashboard

Lecture 36 5.7. Conclusion of Data Visualization and Business Intelligence

Section 6: Module 6. Machine Learning with Google Cloud Platform (GCP)

Lecture 37 Module 6. Machine Learning with Google Cloud Platform (GCP)

Lecture 38 6.1. Data Preparation for ML in GCP

Lecture 39 6.2. Training ML Models on GCP

Lecture 40 6.3. Deploying ML Models on GCP

Lecture 41 6.4. Real-World Use Cases of ML on GCP

Lecture 42 6.5. Hands - on ML Project on GCP

Lecture 43 6.6. Conclusion of Machine Learning with Google Cloud Platform

Section 7: Module 7. MLOps and Workflow Automation

Lecture 44 Module 7. MLOps and Workflow Automation

Lecture 45 7.1. MLOps Workflow and Pipeline Automation

Lecture 46 7.2. Tools for MLOps and Workflow Automation

Lecture 47 7.3. Continuous Integration and Deployment (CI CD) in MLOps

Lecture 48 7.4. Model Monitoring and Drift Detection

Lecture 49 7.5. Real-World MLOps Case Studies

Lecture 50 7.6. Hands-on MLOps Project - Automating a Customer Churn Prediction Model

Lecture 51 7.7. Conclusion of MLOps and Workflow Automation

Section 8: Module 8. Security and Governance in Google Cloud Platform (GCP) Analytics

Lecture 52 Module 8. Security and Governance in Google Cloud Platform (GCP) Analytics

Lecture 53 8.1. Identity and Access Management (IAM) in GCP

Lecture 54 8.2. Data Security and Encryption

Lecture 55 8.3. Network Security in GCP

Lecture 56 8.4. Compliance and Audit Logging

Lecture 57 8.5. Threat Detection and Monitoring

Lecture 58 8.6. Governance Best Practices in GCP Analytics

Lecture 59 8.7. Conclusion of Security and Governance in Google Cloud Platform (GCP)

Section 9: Module 9. Real-World Use Cases and Applications Using Google Cloud Platform-GCP

Lecture 60 Module 9. Real-World Use Cases and Applications Using Google Cloud Platform

Lecture 61 9.1. Data Analytics and Business Intelligence

Lecture 62 9.2. Machine Learning and AI Solutions

Lecture 63 9.3. Real-Time Data Processing and IoT

Lecture 64 9.4. Cloud - Based Applications and DevOps

Lecture 65 9.5. Security and Compliance

Lecture 66 9.6. Healthcare and Life Sciences

Lecture 67 9.7. Media and Entertainment

Lecture 68 9.8. Conclusion - Unlocking the Power of GCP

Section 10: Capstone Project

Lecture 69 Capstone Project

Aspiring data scientists, machine learning engineers, and AI practitioners,Developers and software engineers looking to integrate scalable data pipelines, analytics, and AI models,Analysts, business intelligence professionals, and data visualization experts,IT professionals, cloud architects, and DevOps engineers