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FRCCOM-14CompTIA

CompTIA DataAI (DY0-001)

This five-day instructor-led course prepares professionals to leverage data and artificial intelligence (AI) technologies to support business operations, decision-making, and innovation initiatives.

Duration · 5 daysVirtual + In-PersonInstructor-Led

$2,450

Course Description

This five-day instructor-led course prepares professionals to leverage data and artificial intelligence (AI) technologies to support business operations, decision-making, and innovation initiatives. Students develop foundational knowledge of data management, machine learning, generative AI, analytics, AI governance, model evaluation, and responsible AI practices. Through hands-on exercises and real-world scenarios, participants learn how data fuels AI systems and how organizations can effectively implement AI-driven solutions. The course aligns with the CompTIA DataAI (DY0-001) certification objectives and provides practical skills for today's data- and AI-enabled workforce.

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Audience Profile

This course is intended for:

  • Data Analysts
  • Business Analysts
  • AI Practitioners
  • Data Scientists (Entry Level)
  • IT Professionals
  • Project Managers
  • Digital Transformation Leaders
  • Technology Consultants

Prerequisites

Before enrolling, you should have:

  • Basic computer and business technology knowledge
  • Familiarity with spreadsheets and data concepts
  • Understanding of business processes and decision-making
  • No prior AI or data science experience required

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Understand foundational concepts of data analytics and artificial intelligence.
  • 2Assess and prepare data for AI and machine learning applications.
  • 3Apply basic machine learning methodologies and evaluate model performance.
  • 4Utilize generative AI tools and prompt engineering techniques.
  • 5Implement responsible AI governance and risk management practices.
  • 6Communicate AI-driven insights to business stakeholders.
  • 7Support organizational AI adoption and digital transformation initiatives.
  • 8Prepare for and confidently attempt the CompTIA DataAI (DY0-001) certification exam.

— Day-by-Day —

Course Outline

Day 1: Data and Artificial Intelligence Foundations

Module 1

Introduction to Data and AI

  • Evolution of data analytics and artificial intelligence
  • AI terminology and concepts
  • Business applications of AI
  • Types of artificial intelligence
  • Machine learning fundamentals
  • AI adoption trends

Module 2

Data Fundamentals

  • Structured, semi-structured, and unstructured data
  • Data sources and collection methods
  • Data quality and integrity
  • Data lifecycle management
  • Data governance principles

Module 3

AI Ecosystem Overview

  • AI platforms and technologies
  • Cloud-based AI services
  • Data pipelines and workflows
  • AI development lifecycle
  • Enterprise AI architectures

Module 4

Ethical and Responsible AI

  • AI ethics principles
  • Bias and fairness considerations
  • Transparency and explainability
  • Privacy and security concerns
  • Regulatory and compliance considerations

Day 2: Data Preparation and Analytics for AI

Module 5

Data Collection and Preparation

  • Data acquisition techniques
  • Data cleansing and transformation
  • Data normalization and standardization
  • Feature engineering concepts
  • Preparing datasets for AI applications

Module 6

Data Analysis Fundamentals

  • Exploratory data analysis
  • Descriptive statistics
  • Trend identification
  • Pattern recognition
  • Correlation analysis

Module 7

Data Visualization

  • Visualization principles
  • Dashboard concepts
  • Data storytelling
  • Communicating analytical insights
  • Visualization best practices

Module 8

Data Management for AI

  • Data storage technologies
  • Databases and data warehouses
  • Data lakes and cloud storage
  • Metadata management
  • Data governance frameworks

Day 3: Machine Learning and AI Models

Module 9

Machine Learning Fundamentals

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning concepts
  • Training and testing datasets
  • Model development lifecycle

Module 10

Common Machine Learning Models

  • Classification models
  • Regression models
  • Clustering techniques
  • Recommendation systems
  • Forecasting concepts

Module 11

Model Evaluation and Optimization

  • Accuracy and performance metrics
  • Precision and recall
  • Overfitting and underfitting
  • Model validation techniques
  • Continuous improvement processes

Module 12

AI Operations Fundamentals

  • Model deployment concepts
  • Monitoring AI systems
  • AI performance management
  • AI lifecycle governance
  • MLOps overview

Day 4: Generative AI and Enterprise AI Applications

Module 13

Generative AI Fundamentals

  • Generative AI concepts
  • Large Language Models (LLMs)
  • Foundation models
  • Prompt engineering principles
  • Generative AI use cases

Module 14

Enterprise AI Applications

  • Customer service automation
  • Predictive analytics
  • Intelligent document processing
  • Business process automation
  • Decision support systems

Module 15

AI Security and Risk Management

  • AI security challenges
  • Data protection requirements
  • Model security considerations
  • Adversarial AI threats
  • Risk mitigation strategies

Module 16

AI Governance and Compliance

  • AI governance frameworks
  • Policy development
  • Risk assessment methodologies
  • Compliance requirements
  • Responsible AI implementation

Day 5: AI Strategy, Implementation, and Exam Preparation

Module 17

AI Project Planning

  • Identifying AI opportunities
  • Business case development
  • Stakeholder engagement
  • Resource planning
  • AI project management fundamentals

Module 18

AI Adoption and Change Management

  • Organizational readiness
  • Workforce impact considerations
  • AI adoption strategies
  • Training and awareness programs
  • Measuring AI success

Module 19

DataAI Business Scenarios

  • Healthcare analytics use cases
  • Financial services AI applications
  • Manufacturing and supply chain optimization
  • Marketing and customer analytics
  • Government and public sector AI initiatives

Module 20

CompTIA DataAI (DY0-001) Exam Preparation

  • Review of all exam domains
  • AI and data analytics scenario exercises
  • Practice assessments
  • Test-taking strategies
  • Exam readiness evaluation

— Additional Details —

What else is included

Hands-On Activities Included

  • Exploring AI use cases
  • Assessing data quality
  • Mapping AI workflows
  • Identifying ethical AI challenges
  • Cleaning and preparing datasets
  • Performing exploratory data analysis
  • Creating visualizations and dashboards
  • Evaluating dataset readiness for AI projects
  • Building simple machine learning models
  • Evaluating model performance
  • Comparing model outputs
  • Monitoring AI system effectiveness
  • Prompt engineering exercises
  • Generative AI content creation
  • AI risk assessment workshop
  • Enterprise AI solution evaluation

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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