# AI for Data Analytics Canonical URL: ## Overview Transform the way you work with data using AI-powered tools for cleaning, analysis, prediction, and communication. In this hands-on workshop, you’ll work with real datasets, explore current AI tools, and create useful artifacts you can bring into your own reporting and decision-making workflows right away. Rather than treating verification as an afterthought, this course builds critical evaluation into every stage of the process. You’ll learn how to use AI to explore, analyze, visualize, and report on data while developing the habits needed to question outputs, document your process, and produce more trustworthy results. ## What you'll learn - Use AI tools to clean, explore, analyze, visualize, and report on datasets. - Write better prompts for analytics work using practical patterns and structure. - Check AI-generated results with a 7-step validation process. - Build predictive models through natural language, with no coding required. - Create a traceable workflow that supports accountability and reproducibility. - Communicate and defend AI-assisted findings more effectively. ## Prerequisites No programming or statistics background is required. Participants should have basic spreadsheet skills and access to at least one AI tool (such as ChatGPT, Claude, or Microsoft Copilot). A laptop with a modern browser and reliable internet is required, and bringing an anonymized work dataset is optional. ## Curriculum #### Trust but Verify - Why verification is taught first: AI failure modes including hallucinations, wrong methods, and context blindness - The 7-step AI Validation Checklist for systematically evaluating any AI-generated analysis - Live hallucination example: seeing how AI fabricates plausible statistics and fictional citations - Introduction to the AI Traceability Document for professional accountability #### The AI & Analytics Landscape - The analytics maturity curve: descriptive, diagnostic, predictive, and prescriptive analytics - AI taxonomy for analysts: how machine learning, deep learning, and generative AI relate to data work - The ACHIEVE framework for deciding when AI adds value vs. when manual methods are better - Bias and fairness in AI: real-world examples and how to incorporate fairness into your verification practice #### GenAI as Your Analytics Co-Pilot - The AI-augmented analytics workflow: Import, Clean, Explore, Analyze, Visualize, Report, Verify - Hands-on lab: clean a messy dataset, generate statistics, ask analytical questions, visualize findings, and verify results - Understanding the “dirty data” problem: how AI automates cleaning but requires your judgment on every decision - Why “clean” doesn’t mean “perfect”: recognizing data quality issues that survive automated cleaning #### Prompt Engineering for Data Work - Three things every analytical prompt needs: role, task with data specifics, and output format - Six prompting patterns for analysts: Describe, Explore, Compare, Predict, Explain, Validate - Iterative prompting techniques: Refine, Redirect, Constrain, and Challenge - Comparing AI tools: running the same prompt in different tools and evaluating where they agree and disagree - Building a personal prompt library of tested, reusable prompts for real job tasks #### Predictive Analytics Demystified - Core concepts: regression, classification, and clustering — when to use each, no math required - Key metrics: R-squared, p-values, accuracy, precision, recall, and the train/test split - Hands-on lab: build a classification model, evaluate metrics, write data-backed recommendations, and self-critique - Defending AI-assisted findings under stakeholder questioning using your traceability document #### Critical Evaluation & Responsible AI - Progressive verification: detecting Simpson’s Paradox, confounding variables, selection bias, and overfitting - Finding subtle errors in professional-looking AI analyses through structured evaluation exercises - Applying the full validation checklist collaboratively at speed - Data privacy and governance: when NOT to upload data, and regulatory considerations (HIPAA, FERPA, GDPR, FISMA) #### AI Tools, Chain Reaction & Live Problem-Solving - The 2026 AI analytics tool landscape: ChatGPT, Claude, Copilot, Gemini, Tableau AI, and ThoughtSpot - End-to-end automation demo: from raw data to stakeholder-ready executive brief in minutes - Live problem-solving: a real work problem solved with AI in real time, unrehearsed - Advanced techniques overview: NLP for text analysis and time series forecasting #### Capstone - Redesign a real workplace workflow with AI tools, verification steps, and traceability built in - Map the before and after: current steps, tools, and time vs. the AI-augmented version - Estimate time savings, identify risks, and define a concrete first implementation step - Present and defend your redesign in a mini stakeholder simulation ## Schedule - Jun 15, 2026 – Jun 16, 2026 — Live Online - Aug 19, 2026 – Aug 20, 2026 — Live Online - Oct 29, 2026 – Oct 30, 2026 — Live Online ## Pricing **Tuition:** $695