Modern Data & AI Essentials

• One day - approximately 6.5 hours of instruction, demonstrations and discussion.
• Introductory: no programming or advanced technical experience required.
• Follows the data lifecycle end to end, from collection through analytics, machine learning and AI.
• Covers modern platforms and the roles involved, so participants know who does what and why.

• No programming or advanced technical experience is required.
• A basic understanding of how organizations use data is expected.
• General familiarity with business or IT processes is helpful.

By the end of this course, participants will be able to:

• explain how data science, analytics, business intelligence, machine learning and AI fit together
• describe the major stages of the data lifecycle
• recognize why data quality, preparation, governance, privacy and responsible use matter
• explain how analytics and visualization turn data into business insight
• explain fundamental machine learning and generative AI concepts, including LLMs and RAG
• describe modern data platforms and architecture concepts
• recognize common data and AI roles and how they work together
• identify the opportunities and the common failure modes in data and AI initiatives

• 1. The Modern Data-Driven Enterprise
  • Data science, analytics, business intelligence, machine learning and AI.
  • How the data landscape has evolved, from reporting to predictive and AI-driven decision-making.
  • Common business use cases; demonstration and discussion.
• 2. Data Collection, Preparation and Quality
  • Structured, semi-structured and unstructured data; collection and integration.
  • Cleaning, preparation and quality; preparing data for analytics and AI.
  • Data governance, privacy and responsible data use.
• 3. Analytics and Data Visualization
  • Descriptive, diagnostic, predictive and prescriptive analytics.
  • Basic statistical concepts used in analytics.
  • Principles of effective visualization and the categories of BI tooling.
• 4. Machine Learning and AI Fundamentals
  • What machine learning is and is not; supervised and unsupervised learning.
  • Classification, regression, clustering and forecasting; training data, models and model limits.
  • Traditional AI versus generative AI; large language models and Retrieval-Augmented Generation.
  • Privacy, security, bias, hallucinations and responsible AI.
• 5. Modern Data Platforms, Tools and Roles
  • Traditional databases and modern data environments; warehouses, lakes and lakehouses.
  • Cloud-based, distributed and scalable data processing.
  • Roles including Data Analyst, Data Engineer, Analytics Engineer, Data Scientist,
  • AI/ML Engineer, Data Architect and Data Governance, and how they work together.
• 6. Putting Data and AI to Work
  • Starting with the business problem, then identifying and preparing the right data.
  • Building cross-functional teams and moving from proof of concept to production.
  • Measuring business value; responsible AI and governance.
  • Why data and AI initiatives commonly fail.