Google Cloud Fundamentals for Researchers
In this course you will learn how to use various tools in Google Cloud to ingest, manage and leverage your data to derive insights in your research. You will be introduced to tools used on Google Cloud by researchers, then you will learn how to ingest your unstructured and structured data into Cloud Storage and BigQuery respectively. Next, you will learn how to curate your data and understand costs in Google Cloud. Finally you will learn how to leverage notebook environments and other Google Cloud tools for descriptive and predictive analysis.
What you will learn
- Understand products available in Google Cloud for research
- Load unstructured and structured data into Google Cloud
- Manage access and sharing your data on Google Cloud
- Understand costs on Google Cloud
- Leverage Jupyter Notebook environments in Vertex AI Workbench
- Utilize machine learning solutions on Google Cloud
Prerequisites
- Basic knowledge of data types and SQL
- Basic programming knowledge
- Machine learning models such as supervised versus unsupervised models
Target audience
- Customers, Researchers
Training Program
6 modules to master the fundamentals
Objectives
- Explore research use cases in Google Cloud through interactive demos.
Topics covered
- →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
Objectives
- Understand how resources in Google Cloud are managed across organizations, folders and projects.
- Control access to projects and resources using IAM
- Explore billing in Google Cloud
Topics covered
- →Organizing resources in Google Cloud
- →Controlling Access to projects and resources
- →Cost and billing management
Objectives
- Understand the methods of interacting with Google Cloud
- Store your data in Cloud Storage buckets
- Provision Compute Engine virtual machines
- Understand computing costs on Google Cloud
- Explore how you can create HPC clusters on Google Cloud
Topics covered
- →Interacting with Google Cloud
- →Create and Manage Cloud Storage Buckets
- →Compute Engine virtual machines
- →Understanding computing costs
- →Introduction to HPC on Google Cloud
Activities
Lab: Create and Manage a Virtual Machine (Linux) and Cloud Storage
Optional Lab: Deploy an HPC Cluster with Slurm
Objectives
- Understand the fundamentals of BigQuery
- Query public datasets in BigQuery Studio
- Manage datasets in BigQuery
- Connect data in BigQuery to Looker Studio
Topics covered
- →BigQuery fundamentals
- →Querying public datasets
- →Importing and exporting data in BigQuery
- →Connecting to Looker Studio
Activities
Lab: BigQuery and Looker Studio Fundamentals
Objectives
- Explore Vertex AI as a machine learning platform
- Provision Jupyter notebooks using Vertex AI Workbench
Topics covered
- →Vertex AI
- →Vertex AI Workbench
- →Connecting Jupyter notebooks to BigQuery
Activities
Lab: Interacting with BigQuery using Python and R Running in Jupyter Notebooks
Objectives
- Explore machine learning options on Google Cloud
- Understand unstructured data using prebuilt ML APIs
- Create no-code custom ML models using Vertex AI AutoML
- Create custom ML models using SQL on BigQuery ML
Topics covered
- →ML Options on Google Cloud
- →Prebuilt ML APIs
- →Vertex AI AutoML
- →BigQuery ML
Activities
Optional Lab: Extract, Analyze, and Translate Text from Images with the Cloud ML APIs
Optional Lab: Identify Damaged Car Parts with Vertex AutoML Vision
Optional Lab: Getting Started with BigQuery Machine Learning
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote