# AI for Data Analytics Canonical URL: ## Overview Revolutionize your data analysis process with artificial intelligence (AI). This course provides a deep dive into AI tools that automate data collection, preprocessing, analysis, and visualization, enabling participants to extract valuable insights with minimal coding expertise. By the end of the course, learners will be proficient in utilizing AI-driven analytics across various sectors such as finance, marketing, and healthcare, and will be adept at presenting their findings effectively through sophisticated visualizations and reports. Throughout the course, participants will explore a range of modules that cover essential aspects of AI in data analytics. The curriculum includes an introduction to AI tools for data analysis, followed by hands-on training in data collection, cleaning, and preprocessing techniques. Learners will also delve into exploratory data analysis, predictive modeling, and advanced AI techniques such as natural language processing and time series forecasting. The course culminates in a capstone project where participants apply their skills to a comprehensive data analysis task, showcasing their ability to implement AI solutions in real-world scenarios. ## What you'll learn - Overview of popular AI tools and platforms like IBM Watson, Google AI, Tableau, and Microsoft Azure AI - Learn automated data cleaning methods and how to handle missing data and outliers using AI tools - Use AI tools to generate summary statistics, visualize data distributions, and detect patterns - Understand regression, classification, and clustering, and use AI tools to build and evaluate predictive models - Explore applications of NLP for text analysis and automated time series forecasting with AI tools ## 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 ## Pricing **Tuition:** $695