In this Specialization, you’ll strengthen critical thinking and decision science by combining model thinking, computational problem solving, evidence-based reasoning, and practical GenAI prompting. You’ll learn how different models explain the same situation in different ways—so you can choose better assumptions, spot tradeoffs, and test strategies before you act. You’ll also practice turning messy problems into clear steps a computer could execute, using problem identification, decomposition, pattern recognition, abstraction, algorithms, and evaluation.
Across lessons on statistics, the law of large numbers, correlation, experiments, prediction, cognitive biases, and logic, you’ll build habits for judging claims you see at work and in the media. You’ll then use GenAI as a thinking partner for customizing a Critical Thinking Framework, selecting and guiding models with clear constraints, and improving reliability by asking for evidence, alternatives, and checks.
By the end of this Specialization, you will be able to:
Apply model-based reasoning (e.g., networks, games, randomness) to explain outcomes and compare decisions.
Decompose real-world problems into algorithms and evaluate solutions against goals and constraints.
Judge evidence using statistical and scientific reasoning, including experiments vs. correlations and uncertainty.
Build and reuse GenAI prompts that support critical thinking with explicit assumptions, sources, and evaluation steps.
Projet d'apprentissage appliqué
You'll practice through quizzes, activities, and a four-part final project. In Model Thinking, you’ll complete 10 quizzes plus a midterm and final exam, with short in-video knowledge checks.
In Problem Solving Using Computational Thinking, you’ll use a fillable graphic organizer in three case studies—airport surveillance, epidemiology (Vaccinated/Susceptible/Infected/Recovered groups), and human trafficking—and then attempt related quizzes. Your Final Project requires you to choose a location and natural disaster, write a context paragraph, submit an iterated organizer (problem ID, decomposition, pattern recognition, abstraction) with a justification, and create a visual algorithm depiction for an executable plan.
Mindware adds pre-lecture prompts, interactive activities, and graded quizzes. The GenAI course includes various multiple-choice graded assignments and a Progressive Prompting Exercise that builds a prompt using Active Prompting Elements and a Critical Thinking Framework.
