Certification in Data and Analytics using Google Cloud GCP

Description

Take 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 1

Introduction and Study Plan

· Introduction and know your instructor

· Study Plan and Structure of the Course

Module 1. What is GCP

1.1. Key Benefits of GCP

1.2. GCP Core Services

1.3. GCP Use Cases

1.4. Getting Started with GCP

1.5. Next Steps - Deploy your first virtual machine, Store and retrieve data with Cloud Storage, Train and AI model using Vertex AI

1.6. Conclusion of What is GCP

Module 2. Data Storage Solutions in Google Cloud Platform

2.1. Types of Data Storage Solutions in GCP

2.2. Choosing the Right Storage Solution in GCP

2.3. Best Practices for Data Storage in GCP

2.4. Next Steps - Explore Cloud Storage for storing unstructured data, Use BigQuery for Data Analytics, Deploy a Cloud SQL Database for your application

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

Module 3. Big Data Processing with BigQuery

3.1. Big Data Processing Features in BigQuery

3.2. Big Data Processing Workflow in BigQuery

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

3.4. Best Practices for Big Data Processing in BigQuery

3.5. Next Steps - Get Hands- On with BigQuery

3.6. Conclusion of Big Data Processing with BigQuery

Module 4. Data Integration and ETL Pipelines

4.1. Components of an ETL Pipeline

4.2. Data Integration Approaches

4.3. Best Practices for Building ETL Pipelines

4.4. Real - World Use Cases for ETL Pipelines

4.5. Next Steps - Build an ETL Pipeline

4.6. Conclusion of Data Integration and ETL Pipelines

Module 5. Data Visualization and Business Intelligence

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

5.2. Types of Data Visualization

5.3. Business Intelligence (BI) Process

5.4. Creating Dashboards in BI Tools

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

5.6. Next Steps - Build Your Own BI Dashboard

5.7. Conclusion of Data Visualization and Business Intelligence

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

6.1. Data Preparation for ML in GCP

6.2. Training ML Models on GCP

6.3. Deploying ML Models on GCP

6.4. Real-World Use Cases of ML on GCP

6.5. Hands - on ML Project on GCP

6.6. Conclusion of Machine Learning with Google Cloud Platform

Module 7. MLOps and Workflow Automation

7.1. MLOps Workflow and Pipeline Automation

7.2. Tools for MLOps and Workflow Automation

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

7.4. Model Monitoring and Drift Detection

7.5. Real-World MLOps Case Studies

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

7.7. Conclusion of MLOps and Workflow Automation

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

8.1. Identity and Access Management (IAM) in GCP

8.2. Data Security and Encryption

8.3. Network Security in GCP

8.4. Compliance and Audit Logging

8.5.Threat Detection and Monitoring

8.6.Governance Best Practices in GCP Analytics

8.7.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 Intelligence

9.2. Machine Learning and AI Solutions

9.3. Real-Time Data Processing and IoT

9.4. Cloud - Based Applications and DevOps

9.5. Security and Compliance

9.6. Healthcare and Life Sciences

9.7. Media and Entertainment

9.8.Conclusion - Unlocking the Power of GCP

Part 2

Capstone Project.