# AI for Data Analytics Canonical URL: ## Overview Learn how to use AI-powered tools to streamline the data analysis process, from cleaning raw data to generating predictions and communicating findings. In this hands-on course, students work with real datasets, use current AI tools, and create practical deliverables they can apply to reporting, business analysis, and decision-making tasks. Critical evaluation is built into the course from the start, not saved for the end, so students learn how to use AI responsibly as they go. By the end of the workshop, students will know how to clean, explore, analyze, visualize, and report on data with AI while using validation and traceability practices to support trustworthy results. ## What you'll learn - Use AI tools to clean, explore, analyze, visualize, and report on datasets. - Write stronger prompts for data work using a simple analytical prompt framework. - Validate AI-generated results with a structured 7-step checklist. - Build and evaluate predictive models through natural language, without coding. - Document your workflow with an AI Traceability Document for accountability and reproducibility. - Present and defend AI-assisted findings in professional settings. ## 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 — NYC - Aug 19, 2026 – Aug 20, 2026 — NYC - Oct 29, 2026 – Oct 30, 2026 — NYC ## Instructors ### Dan Rodney — School Director, Instructor, & Senior Course Developer Dan Rodney has been a designer and web developer for over 20 years, creating coursework and leading innovative training initiatives at Noble Desktop. He teaches courses covering Figma, HTML & CSS, Adobe Photoshop, InDesign, Illustrator, and Power BI. Dan has also been at the forefront of integrating AI into design and business workflows, spearheading Noble Desktop’s latest AI course offerings. In addition to teaching and curriculum development, he writes custom scripts for InDesign (Make Book Jacket, Proper Fraction Pro, and more) and works with automation and AI-driven tools in his free time. You can find Dan on X (Twitter), LinkedIn, Facebook, and at danrodney.com. Learn more about [Dan Rodney's](/dan-rodney) background and expertise. ### Garfield Stinvil — Senior Instructor Garfield is an experienced software trainer with over 16 years of real-world professional experience. He started as a data analyst with a Wall Street real estate investment company & continued working in the professional development department at New York Road Runners Organization before working at Noble. He enjoys bringing humor to whatever he teaches and loves conveying ideas in novel ways that help others learn more efficiently. Since starting his professional training career in 2016, he has worked with several corporate clients including Adobe, HBO, Amazon, Yelp, Mitsubishi, WeWork, Michael Kors, Christian Dior, and Hermès. Outside of work, his hobbies include rescuing & archiving at-risk artistic online media using his database management skills. ### Mourad Kattan — Program Director & Instructor Mourad Kattan is an instructor and Program Director of Business, Finance, & Excel at Noble Desktop, teaching classes and designing courses in Excel, finance, accounting, and financial modeling. Before Noble Desktop, Mourad worked as a financial analyst at Credit Suisse and H/2 Capital Partners. In those positions, he used advanced analytical and financial skills to evaluate a variety of investments. Mourad graduated from the University of Pennsylvania summa cum laude and is part of the Beta Gamma Sigma honor society. Learn more about [Mourad Kattan's](/mourad-kattan) background and expertise. ### Christophe Drayton — Instructor Christophe is the instructor for the UX & UI Design Certificate, Figma Advanced, and Generative AI courses at Noble Desktop, and the founder and Chief Design Officer of Kaaiind, which specializes in Applied Artificial Intelligence in the Creative field. With over 20 years in Branding, UX Design, and Accessibility across Europe and the US, Christophe has positively impacted the digital experience of a large spectrum of companies, ranging from big data to startups, government entities to nonprofits, both in the private and public sectors. As an Educator, Christophe has led transformative and award-winning UX programs at Thinkful, the City University of New York (CUNY), and the Brooklyn Public Library, which have opened doors for underrepresented groups in tech who have secured roles at top companies like Google, Uber, Citi, and IBM. Most recently, he has developed innovative curricula about AI in the workplace for the University of Phoenix, LinkedIn Learning, and Coursera. Today, he focuses on developing Allie, one of the first AI-powered, patent-pending color-blind safe and WCAG-compliant design system generator, while concluding his 5-year fundamental research on color vision deficiencies in digital environments. Christophe believes in hands-on, practical application of human-centered strategy and ethical and inclusive design. He is passionate about sharing his knowledge to inspire and empower the next generation of designers, especially those entering the field through nontraditional paths. ## Pricing **Tuition:** $695