# AI for Data Analytics Course Canonical URL: ## Overview Master AI-powered tools to improve your data analysis workflow, from data cleaning through prediction and stakeholder communication. In this hands-on workshop, participants work with real datasets, use current AI tools, and build professional artifacts they can apply immediately in reporting, evaluation, and decision-making. Verification and critical evaluation are integrated throughout the course so participants develop trustworthy habits from their first AI interaction. Along the way, they learn how to clean, explore, analyze, visualize, and report on data with AI while maintaining accountability through validation and traceability. ## What you'll learn - Use generative AI tools to clean, explore, analyze, visualize, and report on datasets. - Write effective analytical prompts using a practical checklist and analyst-specific prompting patterns. - Verify AI outputs with a 7-step validation checklist and evaluate results for issues such as confounders, Simpson’s Paradox, and overfitting. - Build and evaluate predictive models through natural language, including regression, classification, and clustering. - Maintain an AI Traceability Document for professional accountability and reproducibility. - Defend AI-assisted findings and redesign a real workplace workflow with built-in verification. ## 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 ## 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 the complexities of data, project management, and the transformative potential of AI. ### Clarissa J. Corbin — Instructor Clarissa J. Corbin is an accomplished corporate trainer, project manager, and business consultant with over 25 years of experience designing and delivering impactful learning experiences. As President and CEO of Projections Training Solutions, she partners with federal agencies, private corporations, and international organizations to provide results-driven training in leadership, project management, business analysis, and emerging technologies. Clarissa has trained more than 10,000 professionals worldwide, serving clients such as the DoD, NASA, FEMA, Microsoft, Citibank, PNC Bank, Del Monte, and Symantec. Her expertise has taken her across the globe, leading initiatives in Singapore, China, Japan, South Korea, Africa, Jamaica, Trinidad & Tobago, and St. Thomas, USVI. Known for her ability to engage diverse audiences and create interactive, high-impact sessions, Clarissa equips participants with practical solutions they can apply immediately. At Graduate School USA (GSUSA), Clarissa is regarded as one of the most versatile and trusted instructors. She teaches across multiple programs. She played a pivotal role in redesigning the flagship “Managing for Results” course, while also contributing to the development and review of numerous others. Her contributions have earned her two GSUSA Customer Excellence Awards and a two-year appointment to the GSUSA Instructor Advisory Council, underscoring her commitment to innovation, quality, and learner success. ### Natalya H. Bah — Instructor Natalya Bah has been a part-time instructor at the Graduate School USA for over fifteen years. Natalya teaches across multiple curricula, including Leadership and Management, Project Management, and Human Resources. She has created a curriculum for the school, including Change Management Workshops and project management courses. She has served as an action learning coach, instructor, and facilitator for government leadership programs in the Center for Leadership and Management. Natalya also provides self-assessments and dynamic team-building sessions on behalf of the Graduate School USA. Outside of Graduate School USA, Ms. Bah is a self-employed business owner providing executive coaching, training, and consulting services to the public and private sectors. She created the Define and Achieve Your Goals Process™ and is a certified Birkman Method© Consultant. She received her Master of Science degree in Project Management from George Washington University’s School of Business, where she served as a teaching assistant and received the Project Management Award. She is also a certified Project Management Professional (PMP). ### Michiel Pruijssers Michiel is a Principal Designer and Forward Deployed Engineer at Microsoft's Industry Solutions Engineering Division, where he partners with strategic enterprise customers to architect and ship production AI systems. With over 15 years of experience, he operates at the intersection of user research, design, engineering, and cloud architecture, anchored by deep expertise in machine learning. His career spans senior roles at Snorkel AI, Determined.ai, and earlier Microsoft teams behind foundational AI products including LUIS.ai, Azure Bot Service, and Azure Stack, where he led both customer discovery and platform design for some of the industry's most technically ambitious ML offerings. He pairs rigorous qualitative research with hands-on building, shipping his own production SaaS products in Python while also teaching students through cohort-based AI courses. His instruction is grounded in real practice: what it takes to design, build, and operate AI systems that actually hold up in the wild. ## Pricing **Tuition:** $695