Course Overview of Google Cloud Fundamentals for Researchers

This instructor-led course introduces researchers to Google Cloud tools and services for managing, analyzing, and leveraging research data. Participants will explore how to ingest structured and unstructured data into Google Cloud and utilize cloud-based analytics and machine learning services to derive meaningful insights. 

The course covers Google Cloud project concepts, Cloud Storage, Compute Engine, BigQuery, Looker Studio, Vertex AI Workbench, and machine learning solutions such as Vertex AI AutoML and BigQuery ML. Learners will also gain practical experience using Jupyter Notebook environments for descriptive and predictive analysis workflows. 

Through guided labs, demos, and real-world research use cases, participants will build foundational skills for cloud-based research computing, analytics, and AI-driven research workflows on Google Cloud.

After Completing Google Cloud Fundamentals for Researchers Course

  • Understand products available in Google Cloud for research workflows.
  • Load structured and unstructured data into Google Cloud.
  • Manage access control and data sharing using IAM.
  • Understand billing and cost management on Google Cloud.
  • Create and manage Cloud Storage buckets and Compute Engine virtual machines
  • Query and analyze data using BigQuery.
  • Visualize data using Looker Studio.
  • Utilize Jupyter Notebook environments in Vertex AI Workbench.
  • Explore machine learning solutions on Google Cloud.
  • Build descriptive and predictive analytics workflows using cloud services.

Upcoming Batches

Loading Dates...

Key Features of Google Cloud Fundamentals for Researchers

  • Research-Focused Cloud Training 

  •  Hands-On Learning Experience

  • Cloud Infrastructure and Storage 

  • BigQuery and Data Analytics 

  •  Vertex AI and Notebook Workflows

  • Machine Learning for Researchers

  • Beginner-Friendly Research Enablement

  • Google Cloud Research Ecosystem Exposure

Who should Attend Google Cloud Fundamentals for Researchers

  • Researchers
  • Academic Professionals
  • Data Analysts
  • Data Scientists
  • Research Engineers
  • Cloud Practitioners
  • Professionals interested in cloud-based research workflows and analytics

Prerequisites of Google Cloud Fundamentals for Researchers

  • Basic knowledge of data types and SQL
  • Basic programming knowledge
  • Familiarity with machine learning concepts such as supervised and unsupervised learning
  • Interest in cloud-based research workflows and analytics

    • Why choose Cloudthat as your training partner for Google Cloud Fundamentals for Researchers

    • Specialized GCP Focus- CloudThat specializes in cloud technologies and delivers focused Google Cloud training programs with practical implementation experience and research-oriented use cases.
    • Industry-Recognized Trainers - Our trainers are certified Google Cloud professionals with expertise in cloud analytics, machine learning, research computing, and AI-driven data workflows.
    • Hands-On Learning Approach - CloudThat emphasizes practical learning through guided labs, notebook workflows, analytics exercises, and machine learning implementation scenarios.
    • Customized Learning Paths- Training programs are designed for researchers, data scientists, analysts, academic professionals, and cloud practitioners with varying levels of cloud expertise.
    • Interactive Learning Experience- Sessions include demonstrations, collaborative analytics workflows, notebook exercises, troubleshooting activities, and interactive discussions. 
    • Placement Assistance and Career Support - CloudThat supports learners with cloud learning paths, AI and analytics career guidance, interview preparation, and research computing best practices.
    • Continuous Learning and Updates- Course content is continuously updated to align with the latest advancements in Google Cloud analytics, Vertex AI, BigQuery, and AI-driven research technologies. 
    • Positive Reviews and Testimonials- Thousands of professionals and enterprises trust CloudThat for advanced cloud, AI, analytics, and research computing training programs.

    Learning Objectives of Google Cloud Fundamentals for Researchers

    • This course enables researchers and data professionals to leverage Google Cloud tools for research computing, analytics, machine learning, and AI-driven data workflows using BigQuery, Vertex AI, Cloud Storage, and related cloud services. 

    Course Outline for Google Cloud Fundamentals for Researchers Download Course Outline

    Lecture Content

    • Demo: Provision Compute Engine Virtual Machines
    • Demo: Query a Billion Rows of Data in Seconds using BigQuery
    • Demo: Train a Custom Vision Model using AutoML Vision
    • Research Use Cases on Google Cloud

    Learning Objectives

    • Explore research workflows using Google Cloud services
    • Understand practical analytics and ML use cases for researchers

    Lab Content

    • Demo-Based Activities

    Lecture Content

    • Organizing Resources in Google Cloud
    • Controlling Access to Projects and Resources
    • Cost and Billing Management
    • IAM Concepts and Access Control

    Learning Objectives

    • Understand Google Cloud resource organization
    • Manage access using IAM
    • Explore billing and cost management concepts

    Lecture Content

    • Interacting with Google Cloud
    • Create and Manage Cloud Storage Buckets
    • Compute Engine Virtual Machines
    • Understanding Computing Costs
    • Introduction to HPC on Google Cloud

    Learning Objectives

    • Store and manage research data in Cloud Storage
    • Provision and manage Compute Engine virtual machines
    • Understand computing costs and optimization
    • Explore HPC capabilities on Google Cloud

    Lab Content

    • Create and Manage a Virtual Machine (Linux) and Cloud Storage
    • Optional Lab: Deploy an HPC Cluster with Slurm

    Lecture Content

    • BigQuery Fundamentals
    • Querying Public Datasets
    • Importing and Exporting Data in BigQuery
    • Connecting to Looker Studio

    Learning Objectives

    • Understand BigQuery fundamentals and architecture
    • Query public datasets in BigQuery Studio
    • Manage datasets in BigQuery
    • Connect BigQuery datasets to Looker Studio

    Lab Content

    • BigQuery and Looker Studio Fundamentals

    Lecture Content

    • Vertex AI
    • Vertex AI Workbench
    • Connecting Jupyter Notebooks to BigQuery

    Learning Objectives

    • Explore Vertex AI as a machine learning platform
    • Provision Jupyter notebook environments
    • Connect notebooks with BigQuery datasets

    Lab Content

    • Interacting with BigQuery using Python and R Running in Jupyter Notebooks

    Lecture Content

    • ML Options on Google Cloud
    • Prebuilt ML APIs
    • Vertex AI AutoML
    • BigQuery ML

    Learning Objectives

    • Explore machine learning solutions on Google Cloud
    • Analyze unstructured data using prebuilt ML APIs
    • Create no-code ML models using Vertex AI AutoML
    • Build ML models using SQL with BigQuery ML

    Lab Content

    • Optional Lab: Extract, Analyze, and Translate Text from Images with Cloud ML APIs
    • Optional Lab: Identify Damaged Car Parts with Vertex AutoML Vision
    • Optional Lab: Getting Started with BigQuery Machine Learning

    Certification Details of Google Cloud Fundamentals for Researchers

      CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

    Click to Zoom

    Select Course date

    Loading Dates...
    Add to Wishlist

    Course ID: 28600

    Course Price at

    Loading price info...
    Enroll Now

    FAQs for Google Cloud Fundamentals for Researchers

    This course is designed for researchers, data professionals, academic users, and individuals interested in using Google Cloud for research workflows.

    No. This course is beginner-friendly and introduces Google Cloud services step-by-step.

    The course covers Cloud Storage, Compute Engine, BigQuery, Looker Studio, Vertex AI Workbench, AutoML, and BigQuery ML.

    Yes. The course includes practical labs involving BigQuery, Cloud Storage, virtual machines, notebooks, and machine learning workflows.

    Compute Engine, Cloud Storage, BigQuery, Looker Studio, Vertex AI Workbench, Vertex AI AutoML, and BigQuery ML.

    Yes. Learners will explore prebuilt ML APIs, Vertex AI AutoML, and BigQuery ML workflows.

    Yes. The course includes Vertex AI Workbench and Jupyter notebook workflows using Python and R.

    1-day instructor-led training with lectures, demos, labs, and guided implementation exercises.

    Yes. A CloudThat Course Completion Certificate will be awarded after successful completion of the training.

    Yes. This is an introductory-level course designed for researchers and professionals new to Google Cloud and cloud-based analytics workflows.

    Enquire Now