This module explores the limitations and misconceptions surrounding data usage, focusing on how data is often influenced by bias, randomness, and practical constraints. Learners will gain an understanding of how to critically evaluate data and recognize the importance of strategic decision-making. The content highlights real-world examples to illustrate how data can shape but also mislead business and scientific decisions.

This module explores the principles of decision-making using data, with a focus on causality, counterfactual thinking, and managing uncertainty. Learners will develop frameworks to evaluate decisions and minimize regret in complex situations. The content emphasizes practical applications of data analysis in real-world scenarios.

This module explores structured frameworks for using data effectively in business decisions, focusing on how data integration works in CRM and ERP systems. It addresses common misconceptions about data and highlights the importance of structured approaches in real-world applications.

This module covers the fundamentals of performance measurement, focusing on key performance indicators (KPIs), outcomes versus outputs, and techniques for setting realistic targets. Learners will gain skills in evaluating data-driven decisions and designing effective dashboards for performance tracking.

This module explores the process of hypothesis validation as a strategic tool for making informed, forward-looking decisions. It teaches learners how to articulate, test, and refine hypotheses to drive actionable insights and improve decision-making in dynamic environments.

This module explores the role of evidence in hypothesis validation, examining different types of evidence and their strengths and limitations. Learners will gain insights into how to critically assess data, recognize biases, and make informed decisions based on evidence. The content also highlights the importance of balancing certainty with practical constraints in real-world scenarios.

This module focuses on identifying common pitfalls in descriptive analysis, understanding the role of variables and units of analysis, and addressing biases and uncertainties in data. Learners will gain skills in evaluating historical data, recognizing omitted variable bias, and interpreting time-series and noisy data. The module equips students with the tools to improve the reliability of their data-driven insights.

This module equips learners with the skills to critically evaluate scientific evidence, identify common pitfalls such as selection bias and confounding variables, and understand how to design and interpret controlled experiments for reliable causal inferences.

This module explores how data and analytics drive operational efficiency, reduce costs, and enhance coordination through structured processes and machine learning. It covers the application of data in business operations, the role of AI in automating decision-making, and the balance between machine and human involvement in complex workflows.

This module explores how hypothesis validation connects performance measurement with operational enablement, highlighting the importance of data-driven decision-making and organizational effectiveness. Learners will understand how these elements interact in real-world scenarios and how to apply them in business contexts. The module also addresses the role of technology in supporting these processes.