Predictive analytics turns data into a crystal ball, empowering your organization to anticipate trends, seize opportunities, and stay ahead of the curve with every decision. In this course, we will begin with an overview of predictive analytics models, such as decision trees, kNN, and neural networks, and explore their business applications. Following this, we will examine a case study about customer churn to learn how to use a design sprint framework for brainstorming a predictive analytics project plan.

Learning objectives: - Examine how predictive analytics principles can be applied to address business challenges. - Examine advanced ML/AI models for predictive analytics - Analyze business context and construct an issue tree for a predictive analytics project - Select a solution approach and define a predictive modeling project

Organizations can harness the power of causal inference through randomized field experiments, uncovering the true effects of interventions and enabling data-driven decision-making. In this course, we will delve into how companies are currently utilizing A/B testing. Additionally, we will explore the ethical considerations of conducting experiments and the econometric methods for analyzing causal relationships in observational data.

Learning objectives: - Examine how companies are using field experiments for causal inference. - Analyze the ethical dimensions of field experiments. - Construct an issue tree for conducting randomized experiments about designing online referral bonus programs - Select a solution approach and define an experimental setup