# AI for Data Analytics Canonical URL: ## Overview Revolutionize your data analysis with artificial intelligence (AI) in this hands-on course. Learn to use AI tools that automate data collection, preprocessing, analysis, and visualization, allowing you to extract insights with minimal coding. By the end, you'll be proficient in applying AI-driven analytics across sectors like finance, marketing, and healthcare, and presenting your findings with advanced visualizations and reports. The course begins with an introduction to AI tools for data analysis, followed by training in data collection, cleaning, and preprocessing techniques. You’ll then explore exploratory data analysis, predictive modeling, and advanced AI methods such as natural language processing and time series forecasting. The course culminates in a capstone project, where you'll apply your skills to a comprehensive data analysis task, showcasing your 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 - Aug 19, 2026 – Aug 20, 2026 — Live Online - Oct 29, 2026 – Oct 30, 2026 — Live Online ## Instructors ### Bruce Gay — Instructor Bruce is an engaging trainers and program manager who brings 25+ years practical experience to deliver effective and experiential training to students. Able to engage adult learners with a range of backgrounds and professional experiences. Successful at building effective stakeholder relationships and coordinating multi-disciplinary teams for solution delivery. Bruce has over 25 years of project and program management experience across multiple industries. He has a Masters degree from The George Washington University and a B.A. from the University of North Carolina Chapel Hill. Bruce currently runs his own freelance training and consulting business, helping project managers and team leaders improve their business skills, become better leaders, and achieve professional greatness. Bruce is a well-received speaker in the areas of design thinking, project management, cross-team collaboration, and AI tools for projects, and has presented at regional and international conferences. ### Steve Pesklo — Instructor Steve is an energetic trainer who focuses on applying technical concepts to everyday work practices. He is the founder and president of SoftLake Solutions, a company that specializes in providing data and AI applications to identify fraud for Internal Audit, Criminal Investigations, Forensic Accounting, Privacy, and Compliance. Steve brings a large amount of experience across multiple industries and government agencies. He is an expert in implementing large data analysis projects across the world, including Inland Revenue in the UK and Argentina, New Zealand, Africa and across Europe. Previously, he was the manager of Data Architecture and Data Services for a large mortgage company. He is a frequent speaker on data analytics and project management topics and speaks fluent German. He has been teaching at the Graduate School for over 10 years. Steve has an M.B.A. from the University of St. Thomas and a B.S. in Computer Science from California Lutheran University and the Universität Salzburg in Austria. He is certified as a Certified Fraud Examiner (CFE), Project Management Professional (PMP), and a Certified ScrumMaster (CSM). ### Brian Simms — Instructor Brian Simms is a seasoned educator and training leader with extensive experience developing and delivering innovative learning programs in project management and emerging technologies. Over the course of his career, he has designed adaptive learning models that combine instructor-led sessions, live online experiences, and self-paced study, ensuring professionals can access training in flexible and effective ways. His work has emphasized the integration of artificial intelligence into professional development, helping organizations and individuals understand how AI can be applied to solve real-world challenges in leadership, project execution, and decision-making. In addition to his instructional expertise, Brian has guided curriculum development, led large training initiatives, and advanced the use of collaboration tools that improve learner engagement and retention. His depth of experience and forward-looking perspective make him uniquely equipped to prepare federal professionals to navigate both the complexities of project management and the transformative potential of AI. ## Pricing **Tuition:** $695