AI Private

Leveraging AI in Data Analytics (AIA300)

2 days
AIData

Use generative AI assistants to collect, clean, analyze, and visualize data, with responsible-use and governance practices built in. No programming required.

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  • Private class for your team
  • Live expert instructor
  • Online or on‑location
  • Customizable agenda
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Course Overview

This course is designed for working analysts who want to add generative artificial intelligence to their analytical practice without learning to program. Participants compare AI and machine learning with conventional analytical methods, then use assistants to source, prepare, examine, visualize, and interpret data. Responsible-use and governance practices are integrated throughout, helping learners uncover less-obvious patterns and make better-supported decisions in a range of business settings.

Course Benefits

  • Distinguish between AI-assisted analytics, machine learning approaches, and traditional data analysis methods, and recognize when each is appropriate for a given analytical workflow.
  • Apply generative AI assistants to collect, clean, and preprocess data, including resolving missing values and generating synthetic data for testing.
  • Direct AI tools to produce summary statistics, surface patterns, and build interactive visualizations and reports that decision makers can act on.
  • Evaluate the bias, privacy, and transparency risks of AI-assisted analysis and apply organizational data governance policies to mitigate them.
  • Adapt AI analytics applications drawn from multiple industry sectors into a personal integration roadmap for the participant's own role.

Delivery Methods

Private Class
Delivered for your team at your site or online.

Course Outline

  1. Day 1: AI Foundations and AI-Assisted Data Preparation
    1. AI and ML Foundations for Analytics: Core concepts of artificial intelligence and machine learning; how AI and ML differ from traditional data analysis; where each approach adds value.
    2. Generative AI Tool Selection and Privacy Screening: Vendor-neutral comparison framework; privacy-focused options; matching tools to organizational data policies.
    3. AI-Assisted Data Collection: Automated sourcing techniques; validation checkpoints; evaluating collection outputs.
    4. AI-Assisted Cleaning, Missing Data, and Synthetic Data: Prompt-driven cleaning; missing value handling; generating synthetic test data.
  2. Day 2: Analysis, Reporting, Governance, and Adoption
    1. Summary Statistics and Pattern Detection: Descriptive statistics automation; anomaly detection; recognizing hidden relationships.
    2. Interactive Visualization and Reporting with AI: Natural-language chart generation; interactive visualizations; annotated reporting; revising AI-generated dashboards.
    3. Ethics, Bias, Privacy, and Governance: Bias identification; privacy-preserving practices; transparency and compliance; organizational AI policies.
    4. AI Analytics Across Industries: Cross-sector case studies; adapting applications to regulated environments.
    5. Personal AI Integration Roadmap: Immediate application planning; adoption sequencing; measuring success.

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this AI class:

Data Analysis Fundamentals or equivalent experience (proficiency with spreadsheet navigation, basic data manipulation, and descriptive statistics).

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