Certification in Data and Analytics using Google Cloud GCP

Explore Google Cloud Platform data storage solutions for structured, semi-structured, and unstructured data, including Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable, and BigQuery.

Explore the types of data storage solutions in GCP, including Cloud Storage, Cloud SQL, BigQuery, Firestore, Cloud Spanner, and Cloud Bigtable, with data transfer options and security features.

Choose the right GCP storage solution by matching data needs to options like Cloud Storage, Cloud SQL, Firestore, BigQuery, Cloud Spanner, and Cloud Bigtable.

Master data practices in GCP by selecting storage based on scalability, access speed, and cost, enforce IAM access, apply encryption, lifecycle policies, and automated backups to secure and optimize costs.

Explore cloud storage for unstructured data, create buckets with storage classes and regional or multi-regional setups, access via gsutil or the GCP SDK, analyze with BigQuery and deploy Cloud SQL.

Explore data storage solutions in Google Cloud Platform (GCP), including Cloud Storage, Cloud SQL, Cloud Spanner, Firestore, and Bigtable. See how these services support unstructured data, real-time apps, and analytics.

Explore big data processing with Google BigQuery, a serverless, fully managed data warehouse that supports petabyte-scale SQL queries, real-time analytics, and BigQuery ML for AI/ML.

Explore big data processing features in BigQuery, including columnar storage, distributed query processing, federated queries, streaming ingestion, and BigQuery ML for SQL-based models like linear regression, classification, and clustering.

Load data into BigQuery from GCS and other formats via streaming or batch uploads; then query with standard SQL and optimize using partitioning, clustering, and caching, plus BigQuery ML.

Explore real world use cases for big data processing in BigQuery across ecommerce, finance, healthcare, marketing, and IoT. Analyze customer behavior, product trends, sales forecasting, and real-time analytics.

Explore best practices for big data processing in BigQuery, including partitioning strategies, clustering, federated queries, and query caching to optimize performance and costs using cost estimation tools.

Get hands-on with BigQuery by exploring Google's public datasets and practicing SQL queries without uploading data. Learn to train ML models end-to-end with BigQuery ML and optimize SQL for analytics.

Analyze massive volumes of data with BigQuery, a fully managed, serverless data warehouse on Google Cloud that uses standard SQL and separates storage from compute for scalable, real-time, cost-efficient analytics.

Explore data integration and ETL pipelines, including extract, transform, load steps. Consolidate data from databases, APIs, and cloud storage into data warehouse or data lake with automation and governance.

Explore the etl pipeline components—extract, transform, and load—from databases, APIs, and data lakes, with practical examples of PostgreSQL extraction, data cleaning, and loading into BigQuery.

Compare batch ETL pipelines scheduled with Airflow and real-time streaming with Kafka. Explore ELT patterns in cloud warehouses like BigQuery for analytics-ready data.

Master best practices for ETL pipelines, including optimizing data extraction by selecting necessary columns and early filtering. Leverage schema evolution, partitioning, clustering, and automation with Airflow, Glue, or Cloud Composer.

Explore real-world ETL pipeline use cases across industries, including e-commerce analytics, financial transactions, healthcare data integration, and IoT streaming for real-time insights and predictive maintenance.

Build an automated extract, transform, and load pipeline with Apache Airflow, from setup to dag creation, loading data into cloud warehouses such as BigQuery, Redshift, and Snowflake.

Leverage Google Cloud Platform to automate data integration and extract-transform-load pipelines. Orchestrate workflows with Cloud Composer, and transform data using Cloud Dataflow and Cloud Data Fusion for analytics and reporting.

Explore data visualization and business intelligence, transforming data into charts, dashboards, and reports with tools like Tableau, Power BI, Google Data Studio, Looker, and Python libraries, powered by BigQuery.

Learn how to create a bar chart in Python using matplotlib to compare department revenues across sales, marketing, IT, and HR, using plt.bar, labels, and a title.

Explore the types of data visualizations, including bar charts, line charts, pie charts, heatmaps, histograms, scatter plots, and boxplots, and learn how to create a heatmap using Seaborn in Python.

Learn the end-to-end business intelligence process, from data collection and processing to warehousing in BigQuery, Snowflake, or Redshift, then analysis, visualization dashboards and reports, and data-driven decisions.

Create interactive dashboards in BI tools such as Tableau, Power BI, and Google Data Studio, connecting to SQL, Excel, and APIs to visualize KPI indicators, revenue trends, and customer segmentation.

Explore real-world use cases of data visualization and BI across industries, including sales analytics, marketing performance, customer insights, finance and risk management, and healthcare analytics.

Import a dataset into Power BI or Tableau and load from Excel, CSV, or API sources; then clean and prepare data to build interactive dashboards with filters and drill downs.

Explore how Google Cloud Platform's data visualization and business intelligence use Looker and Looker Studio to turn raw data into interactive dashboards, reports, and real-time insights for informed decisions.

Explore machine learning on Google Cloud Platform with Vertex AI, a platform for building, training, and deploying models, including BigQuery ML and no-code AutoML for vision, text, and structured data.

Prepare data for ML in GCP by cleansing, transforming, and storing it using BigQuery, Cloud Storage, Dataflow, and Dataprep, with SQL-ready pipelines for ML.

Train ML models on GCP using Vertex AI with AutoML or custom training, prepare data in notebooks, evaluate performance, and leverage BigQuery ML for SQL-based modeling.

Deploy ml models on gcp using Vertex AI endpoints or Cloud Functions for real-time or batch predictions, and implement MLOps with Cloud Composer and Vertex AI Pipelines for automated workflows.

Explore real world ml use cases on GCP, including churn prediction, personalized recommendations, fraud detection, image classification, anomaly detection, and sentiment analysis with Vertex AI, BigQuery ML, and AutoML.

Build a customer churn prediction model on GCP by centralizing data in BigQuery, training with BigQuery ML or Vertex AI, deploying endpoints, and automating retraining with pipelines for Looker Studio.

GCP offers a comprehensive suite of machine learning tools, with Vertex AI as the unified platform to build, train, and deploy models at scale using AutoML or custom code.

Learn how MLOps and workflow automation streamline the end-to-end lifecycle of machine learning models—from development to deployment and monitoring—using data engineering, model versioning, ci cd pipelines, and drift-aware retraining.

Discover how an MLOps workflow automates data ingestion and pre-processing, feature engineering, model training and hyperparameter tuning, deployment, and continuous monitoring with retraining for production ML systems.

Explore tools for MLOps and workflow automation across data management, feature engineering, CI/CD, deployment, and monitoring, including Apache Airflow, Kubeflow pipelines, feature stores, MLflow, Docker, Kubernetes, and Vertex AI.

Explore continuous integration and deployment in MLOps, automating data validation, model training and retraining, model registry, deployment, and continuous monitoring with GitHub Actions pipelines.

Learn to monitor production models with metrics like prediction accuracy, latency, and throughput, and detect data drift, concept drift, and label drift using Prometheus and Grafana.

Explore real-world ml ops case studies, including real-time fraud detection with Kafka, AutoML and TensorFlow on Google Cloud, plus predictive maintenance for IoT with SageMaker and CloudWatch.

Automate a customer churn prediction MLOps pipeline with BigQuery data prep, training on Vertex AI, SageMaker, or Azure ML, and deployment in Docker and Kubernetes.

Conclude MLOps and workflow automation in GCP by using Vertex AI for end-to-end ML lifecycle, including pipelines, automated training, versioning, monitoring, and ci/cd pipelines for ML deployment.

Learn how GCP analytics security and governance protect data with IAM, encryption at rest and in transit, VPC and private networking, audit logs, threat detection, and DLP.

Explore IAM in Google Cloud Platform, implementing RBAC, least privilege, service accounts, and hierarchical policies to control access across projects, folders, and organizations, with external federation for SSO.

Learn Google Cloud data security and encryption, from default encryption at rest with Google managed keys to CMK and CSK options, plus encryption in transit with TLS/SSL and Cloud KMS.

Google Cloud Platform employs a layered defense with VPC isolation, firewall rules, Cloud Armor, and VPC service controls to protect front end and shared services.

Learn how Google Cloud Platform's compliance and audit logging delivers an immutable audit trail with system event logs, data access, and access transparency, enabling assured workloads under HIPAA and GDPR.

GCP delivers AI powered threat detection and monitoring through the Security Command Center, enabling asset discovery, threat detection, misconfiguration analysis, and compliance visibility with Cloud Armor, Cloud IDS, and Chronicle.

Implement governance best practices for GCP analytics by enforcing least-privileged IAM, perimeters with VPC service controls, Cmec encryption, and centralized Security Command Center and audit logs for ongoing compliance.

Explore security and governance in GCP analytics with IAM, Cloud Audit Logs, DLP, and encryption in transit and at rest, ensuring HIPAA, GDPR, and FedRAMP compliance.

Explore real world use cases of Google Cloud Platform, including hosting with App Engine, Compute Engine, and Cloud Run, data storage, BigQuery analytics, artificial intelligence, and internet of things.

Explore data analytics and business intelligence with Google Cloud tools like BigQuery, Looker Studio, Cloud Storage, and Cloud Dataflow to transform raw retail data into real-time insights for inventory optimization.

Explore machine learning and AI solutions for sentiment analysis using Google Cloud tools like Cloud Natural Language API, BigQuery ML, and Vertex AI AutoML to classify reviews and improve experience.

Discover real-time data processing and IoT in smart traffic management using Google Cloud, including Cloud IoT Core, Pub/Sub, Dataflow, and BigQuery to optimize traffic signals.

Explore cloud-based applications and DevOps on Google Cloud Platform, showcasing a serverless, auto-scaling e-commerce backend deployed with Cloud Run, Cloud SQL, Cloud Load Balancing, and Firebase Authentication.

Explore security and compliance for a fintech fraud-detection system on Google Cloud, using BigQuery ML, AI Platform hosted model, Pub/Sub, Dataflow, Firestore, and Chronicle for real-time detection and dashboards.

Explore how healthcare teams use Google Cloud AI and data analytics to predict diabetes progression with BigQuery ML on EHR data, via Cloud Healthcare API and Vertex AI.

Automate video tagging for media and entertainment with Google Cloud Video Intelligence API, extracting labels, objects, faces, and scenes, store metadata in BigQuery from cloud storage to improve searchability.

Explore how GCP enables scalable, secure data analytics, AI, and IoT solutions with DevOps across industries, including retail, healthcare, and finance, powered by BigQuery and Vertex AI.

Design an end-to-end data analytics solution on Google Cloud Platform, from Pub/Sub ingestion and Dataflow processing to BigQuery storage, with Vertex AI ML and Looker Studio dashboards.