The Decision Superhero Masterclass is more than a course—it’s a professional transformation. Designed for forward-thinking professionals, this immersive 24-hour program (delivered in 8 x3hr sessions) empowers executives, analysts, and consultants to master the art and science of making better, faster, and more confident decisions. Through six hands-on, insight-packed modules, you’ll gain the frameworks, tools, spreadsheet modeling and Julia coding skills to turn complexity into clarity, data into direction, and uncertainty into opportunity. Whether you’re leading strategy, solving operational challenges, or advising clients, this masterclass will equip you with the superpowers of modern decision science—giving you and your organization a decisive edge.
 
Who It’s For
Executives & Managers looking to cultivate a high-performing, data-centric culture
Analysts & Consultants wanting advanced hands-on methods in modeling and analytics
Teams ready to adopt Decision Superhero frameworks for everyday challenges
Readers who want to implement all concepts from the Decision Superhero book
 
What You Get
24 Hours of Interactive One-on-One Training & Discussion with Eric Torkia (Onsite sessions are availble)
Workbooks & Tools (digital resources for templates, frameworks, and cheat-sheets)
Real-World Case Studies to exemplify each concept in action
Implementation Roadmap to ensure rapid adoption of decision science
Signed Copy of Decision Superhero for each participant (optional add-on for group sessions)
 
Module 1: Foundations of Decision Superhero
Introduction to the Decision Superhero mindset
Identifying the biggest decision challenges across industries
Understanding the fundamentals of decision science, data-driven thinking, and the role of analytics
Key Outcomes:
Clarity on the Decision Superhero philosophy and why it matters
Baseline knowledge of data literacy, analytics tools, and frameworks
Actionable next steps for building strong decision-ready teams
 
Module 2: Data Strategy & Modeling Fundamentals
Laying out an effective data strategy for decision science applications
Reviewing essential modeling and analytics principles (such as deterministic vs. probabilistic models)
Introduction to relevant tools (Excel, Julia, Python, Power BI, etc.)
 
Key Outcomes:
Understanding how to structure data to fuel decisions
Ability to choose the right modeling approach for the challenge at hand
Basic hands-on exercises in building or refining data models
 
Module 3: Simulation & Optimization
Deep dive into simulation: Monte Carlo simulations, risk modeling, scenario planning
Introduction to linear and non-linear optimization
Real-world case studies on using simulation and optimization to solve complex problems
Key Outcomes:
Confidence in setting up and interpreting simulation models
Fundamental optimization techniques for resource allocation, capacity planning, and decision-making
Best practices for quickly validating or invalidating assumptions
 
Module 4: Multi-Criteria & Risk-Based Decision Making
Approaches to handling multiple objectives or conflicting priorities
Quantifying and incorporating risk tolerance in decision-making
Tools such as decision trees, scoring models, and utility theory
Key Outcomes:
A framework for multi-criteria decision-making tailored to organizational needs
Strategies for weighting options and systematically including risk
Stronger communication around uncertainty and decision trade-offs
 
Module 5: Implementation & Stakeholder Buy-In
Putting decision science to work in a live environment
Overcoming organizational resistance and silos
Creating clear communication strategies to align stakeholders
Key Outcomes:
Steps to embed decision science frameworks into existing processes
Templates and methods for effective stakeholder engagement
Tips on how to measure and articulate the impact of data-driven decisions
 
Module 6: Advanced Topics & Future-Proofing
Cutting-edge trends (machine learning, AI, generative analytics)
Continuous improvement loops and feedback channels
Sustaining a decision-driven culture—what’s next?
Key Outcomes:
Anticipating the next wave of technology and its impacts on decision science
Hands-on exploration of advanced analytics or AI-driven decision-making
Strategies for continuous learning, upskilling, and staying agile