Oracle Cloud Infrastructure Data Science Professional (2025): Hands-on Workshop

Module 1: Welcome to Data Science

• Lesson 1: Data Science Professional Course Overview
• Course Speakers
• Course Contributors
• Target Audience
• Prerequisites
• What the Data Science Exam Validates
• Course Outline
• Data Science Demos and Labs
• Getting the Most Out of This Course
• Ratings and Feedback

Module 2: Introduction and Configuration

• Lesson 1: Data Science: Introduction
• Data Science and Machine Learning (ML) in History
• Data Science and ML Today
• Importance of Data Science and AI
• What is Oracle AI?
• OCI Services That Support AI and ML
• Oracle Cloud Infrastructure Data Science Overview
• Core Principles of OCI Data Science
• OCI Data Science Details
• Data Science Features and Terminology
• Ways to Access OCI Data Science
• Where to Find Data Science
• Lesson 2: ADS SDK Overview
• Accelerated Data Science (ADS) SDK Overview
• Ways to Access ADS SDK
• ADS SDK Features
• Data Visualization
• Feature Engineering
• Model Training
• Model Evaluation
• Model Interpretation and Explainability
• Model Deployment
• Lesson 3: Tenancy Configuration Basics
• Tenancy Configuration Concepts
• How Data Science Components Work Together
• Compartments & Creating Compartments
• User Groups & Dynamic Groups
• Policies (Required, Optional)
• Demo
• Lesson 4: Configure a Tenancy with OCI Resource Manager
• Automatic Configuration and Data Science Service Template
• What the Data Science Service Template Creates
• Running Oracle Resource Manager (ORM) Stack
• Accessing Terraform Script
• Demo
• Lesson 5: Networking for Data Science
• Cloud Networking Components Overview
• Data Science and Networking Connectivity
• Default Networking
• Custom Networking
• Demo
• Lesson 6: Authenticate to OCI APIs
• Importance of Authentication
• Authenticating Different Interfaces
• Resource Principals & Data Science Service Authentication
• OCI Configuration File & Format
• Demo

Module 3: Workspace Design and Setup

• Lesson 1: Projects
• What Are Projects?
• Create, View, Edit, Delete Projects
• Demo
• Lesson 2: Notebook Sessions
• What Are Notebook Sessions?
• Create, View, Edit, Delete Notebook Sessions
• Activate/Deactivate Notebook Sessions
• Notebook Session Metrics
• Demo
• Lesson 3: How to Work with JupyterLab
• JupyterLab Overview and Interfaces
• Features
• Demo
• Lesson 4: Conda Environments: Overview
• What is a Conda Environment?
• Benefits
• Environment Explorer
• Data Science Conda Environments (Published & Installed)
• Demo
• Lesson 5: Data Science Conda Environments
• Types and Families of Conda Environments
• Naming Conventions
• Special Use Cases: Computer Vision, ML, NLP, ONNX, PyTorch, PySpark, TensorFlow
• Modify & Publish
• Lesson 6: Manage Conda Environments
• Recap & Functionality
• Browse, Search, Install, Clone, Delete
• Create from YAML
• Demo
• Lesson 7: OCI Vault: Introduction
• Importance of OCI Vault in Data Science
• Vaults, Keys, Master and Data Encryption Keys
• Rotating Keys
• Secrets
• Lesson 8: Using OCI Vault in OCI Data Science
• Oracle-Managed and Customer-Managed Key Encryption
• Encrypt, Encode, Store, Retrieve Secrets
• Using OCI Vault with ADS
• Demo
• Lesson 9: Code Repositories (Git)
• Version Control Overview
• Centralized vs Distributed VCS
• Using Git in Data Science Workflows
• OCI Git Extension & Terminology
• Working with Remote Repo: GitHub
• Git Commands
• Demo

Module 4: Machine Learning Lifecycle

• Lesson 1: ML Lifecycle Overview

Machine Learning Lifecycle Stages: Data Access, Exploration, Feature Engineering, Modeling, Validation, Deployment, Monitoring

• Lesson 2: Access Data

Data Sources: Object Storage, Local Storage, Autonomous DB, MySQL, Amazon S3, HTTP/HTTPS

• DatasetBrowser, PyArrow
• Supported Sources & Formats
• Demo
• Lesson 3: Data Preprocessing
• Data Transformations, Cleaning, Imputation, Dummy Variables
• Outlier Detection, Feature Scaling, Dimensionality, Text Data
• ADS Automated Transformations
• Demo
• Lesson 4: Introduction to Feature Types
• Feature Types, Exploratory Data Analysis (EDA), Correlation Tables & Plots
• Lesson 5: Custom Feature Types
• Feature Statistics, Plots, Warnings, Validators, Creating Custom Feature Types
• Lesson 6: Data Visualization
• ADS Smart Visualization Tool, AutoML Pipeline
• Methods: corr(), show_in_notebook(), plot(), Seaborn, Matplotlib, GIS
• Data Profiling
• Lesson 7: Model Training
• Model Training Process, Libraries, Ways to Train
• Lesson 8: Oracle AutoML: Introduction
• AutoML Overview, Approaches, Benefits, Workflow
• Model Explanations
• Lesson 9: Hyperparameter Tuning: ADSTuner
• Search Spaces, Tuning Process, Custom Search Spaces
• Lesson 10: Model Evaluation
• ADS Evaluators: Binary, Multiclass, Regression
• Metrics, Charts, and Plots
• Lesson 11: Model Explanations: Global Explainer

Feature Permutation Importance, Feature Dependence Explanations, Accumulated Local Effects (ALE)

• Lesson 12: Model Explanations: Local and WhatIf Explainers
• Lesson 13: Model Catalog: Overview

Artifacts, Metadata, Input/Output Schema, Model Provenance, Introspection, Taxonomy

• Lesson 14: Model Serialization
• Saving Models via ADS SDK, Model Catalog Operations
• Lesson 15: Model Deployment
• Deployment Architecture, Create/Invoke Models, Monitoring, Managing Deployments
• Demo
• Lesson 16: LLM Training & LangChain Integration
• Fine-Tuning Pre-Trained Models, LangChain Integration
• Demo
• Lesson 17: OCI Data Science Operators
• Types, Demo
• Lesson 18: OCI Data Science AI Quick Actions
• AI Quick Actions Overview
• Demo

Module 5: MLOps Practices

• Lesson 1: MLOps Architecture
• Continuous Practices, Importance, Automation
• OCI MLOps Architecture
• Lesson 2: OCI Data Science Jobs
• Jobs Service, Life Cycle, Runs, Scaling, Batch/Distributed Inference
• Oracle Cloud Access for Jobs
• Lesson 3: Jobs Monitoring and Logging
• Job Metrics, Logs, Events, Rules, and Actions
• Lesson 4: Data Science Pipeline
• Pipeline Steps, Life Cycle, Demo Scenario
• Lesson 5: Model Deployment: Autoscaling
• Data Science Model Scaling Issues, Metric-Based Autoscaling

Module 6: Related OCI Services

• Lesson 1: Spark Applications, Data Flow, and Data Science
• OCI Data Flow Overview, Components, Capabilities, Security
• Spark Applications Configuration and Integration with Data Science
• Build and Train ML Models with Data Flow
• Best Practices
• Learn More About ADS
• Lesson 2: Oracle Open Data
• Overview, Benefits, Access
• Lesson 3: OCI Data Labeling
• What, Who Uses, Industry Use Cases
• Data Labeling in AI/ML Life Cycle
• Scenario: Data Labeling for AI
• Data Label Types and Integrations