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Coursera

Data-Driven Leadership with Analytics and AI

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Overview

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Build practical skills in AI, circular economy, ESG, and climate-risk analysis to turn sustainability goals into actionable business strategies. Designed for learners focused on sustainable business transformation, this course explores how agentic AI, intelligent automation, IoT, and real-time environmental data can optimize energy and resources, reduce carbon emissions, strengthen ESG monitoring and reporting, and improve sustainable operations. You’ll apply circular-economy principles across value chains, product design, material recovery, reverse logistics, refurbishment, remanufacturing, product-as-a-service, sharing, and resource-efficient business models. You’ll also evaluate physical and transition climate risks and their effects on credit, investment, insurance, real estate, supply chains, asset values, and corporate resilience. The course uniquely connects AI-powered sustainability with circular business-model innovation, climate risk, ESG regulation, disclosure, transition finance, and compliance. By the end, you’ll be able to formulate scalable, compliant, AI-enabled, circular, and climate-resilient strategies that improve lifecycle value, resource efficiency, and long-term sustainability performance.

Syllabus

  • Building the Data-Driven Decision Toolkit
    • This module develops the foundations of data-driven decision-making through analytics taxonomy, statistical measures, performance metrics, data questioning, hypothesis testing, and A-B testing. Learners will transform broad business concerns into measurable inquiries and evaluate evidence within the correct context.
  • Psychology, Bias, and Better Decisions
    • This module examines how human psychology, heuristics, framing, and cognitive biases affect evidence-based decisions. Learners will diagnose decision traps such as confirmation bias, loss aversion, selection bias, groupthink, narrative fallacy, and recency bias.
  • From Bias Awareness to Intelligent Systems
    • This module connects cognitive debiasing and behavioral interventions with operational AI. Learners will apply human-in-the-loop safeguards, ethical decision frameworks, model monitoring, feedback patterns, validation methods, and continuous retraining strategies.
  • Scaling Feedback and Decision Intelligence
    • This module focuses on secure feedback infrastructure, reinforcement learning from human feedback, industry feedback loops, continuous improvement, AI governance, edge computing, model evaluation, and scalable decision-intelligence platforms.

Taught by

TSV Learning

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