Data and AI Fundamentals is a comprehensive introductory training program designed to help professionals, aspiring technology specialists, business users, and curious learners build a strong understanding of data, artificial intelligence, analytics, and the technologies shaping today’s digital world.
Data, AI, Analytics, and Technology Fundamentals Course Overview
This foundational course provides participants with a practical understanding of data, artificial intelligence, analytics, and modern digital technologies. It explains how organizations collect and manage data, generate insights, use AI responsibly, automate processes, and make evidence-based decisions.
The course combines accessible explanations, demonstrations, discussions, case studies, and hands-on exercises. It is designed for a general professional audience and does not require programming experience.
Objectives
- Explain the roles of data, analytics, AI, and technology in modern organizations.
- Identify common types, sources, formats, and uses of data.
- Describe the data lifecycle, from collection and storage to analysis and disposal.
- Recognize the importance of data quality, governance, privacy, ethics, and security.
- Distinguish descriptive, diagnostic, predictive, and prescriptive analytics.
- Select appropriate charts, metrics, and analytical methods for common business questions.
- Explain AI, machine learning, generative AI, automation, and related terminology.
- Use generative AI tools effectively through structured prompting and critical evaluation.
- Identify suitable—and unsuitable—use cases for AI and analytics.
- Recognize AI risks such as bias, hallucinations, privacy breaches, and overreliance.
- Explain the basic roles of cloud computing, cybersecurity, APIs, databases, and automation.
- Develop a high-level proposal for a data-, analytics-, or AI-enabled improvement initiative.
Duration 5 Days – 35 hrs.
Target Audience
- Employees and managers seeking general digital literacy
- Business, operations, administrative, and support teams
- Project and program managers
- Human resources, finance, sales, marketing, and customer-service professionals
- Government and nonprofit personnel
- Entrepreneurs and small-business owners
- New analysts and technology professionals
- Leaders responsible for digital transformation or technology-enabled change
Prerequisites
- Basic computer and internet skills
- Familiarity with standard office applications
- Basic spreadsheet knowledge
- General awareness of their organization’s processes
- Programming, advanced mathematics, statistics, and prior AI experience are not required.
Course Outline
Day 1: Digital Technology and Data Fundamentals
Module 1: The Digital and Technology Landscape
- Digital transformation
- Relationship among data, analytics, AI, and technology
- Major technology trends
- People, processes, data, and technology
- Digital-improvement opportunities
Module 2: Data Fundamentals
- Types, formats, and sources of data
- Data lifecycle
- Databases, spreadsheets, data warehouses, and data lakes
- Metadata and master data
- Data quality
- Data ownership and stewardship
- Practical data-inventory and quality exercise
Day 1 output: Data inventory and preliminary data-quality assessment.
Day 2: Data Governance, Analytics, and Visualization
Module 3: Data Governance, Privacy, Ethics, and Security
- Data governance principles
- Data classification and access control
- Privacy, consent, and confidentiality
- Ethical data use
- Cybersecurity fundamentals
- Organizational and individual responsibilities
- Data-protection scenario exercise
Module 4: Analytics Fundamentals, Part 1
- From data to insight and action
- Descriptive, diagnostic, predictive, and prescriptive analytics
- Measures, dimensions, metrics, and KPIs
- Basic analytical workflow
- Introductory data-analysis exercise
Day 2 output: Governance recommendations and an initial analysis of a sample dataset.
Day 3: Applied Analytics and Data Storytelling
Module 4: Analytics Fundamentals, Part 2
- Averages, percentages, distributions, and trends
- Correlation versus causation
- Interpreting analytical results
- Common analytical errors
- Completing the sample-data analysis
Module 5: Data Visualization and Storytelling
- Choosing suitable charts
- Reports, dashboards, tables, and scorecards
- Visual hierarchy, color, labels, and accessibility
- Avoiding misleading visualizations
- Communicating findings and limitations
- Building a clear data story
Capstone Project Launch
- Select a business problem or opportunity
- Identify users and stakeholders
- Define the proposed improvement
- Identify initial data requirements and success measures
Day 3 output: Data story and initial capstone proposal.
Day 4: Artificial Intelligence and Generative AI
Module 6: Artificial Intelligence Fundamentals
- AI, machine learning, and deep learning
- Rules-based systems and automation
- Supervised, unsupervised, and reinforcement learning
- Natural-language processing and computer vision
- Generative AI and large language models
- AI capabilities, limitations, and use cases
- Selecting appropriate AI and non-AI solutions
Module 7: Generative AI and Prompting
- Practical operation of generative AI
- Prompt structure and refinement
- Summarization, drafting, extraction, and classification
- Output verification
- Hallucinations, bias, privacy, and copyright
- Responsible workplace use
- Hands-on prompting exercise
Day 4 output: AI use-case assessment and a set of tested workplace prompts.
Day 5: Technology, Responsible AI, and Implementation
Module 8: Modern Technology Foundations
- Hardware, software, networks, and the internet
- Cloud computing
- Applications and platforms
- Databases and APIs
- System integration
- Workflow automation
- Internet of Things
- Scalability, reliability, and interoperability
Module 9: Responsible AI and Risk Management
- Fairness, transparency, and accountability
- Bias, privacy, cybersecurity, and accuracy
- Human oversight
- AI governance
- Risk assessment and monitoring
Module 10: Implementation and Capstone Development
- Problem definition and stakeholder analysis
- Data and technology readiness
- Value, feasibility, cost, and risk
- Pilot planning
- Success measures
- Adoption and change management
Capstone Presentations and Final Assessment
- Participant or group presentations
- Facilitator and peer feedback
- Final knowledge check
- Course evaluation and closing
Day 5 output: Completed project proposal, risk assessment, implementation plan, and presentation.
