Free Udemy Course: Certification in Data and Analytics using Google Cloud GCP

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.