Spezialisierung Analytics Project Ideation

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

Prescriptive Analytics Project Ideation is about the art of transforming data into actionable optimal strategies. This course begins with an overview of prescriptive analytics, exploring key methods like optimization and simulations. You'll then delve into real-world applications across various industries, with applications in investing and staffing optimization. The course also includes a comprehensive sprint phase for problem identification and solutions mapping, culminating in a final project plan review and insights from industry experts.

Learning objectives: - Examine how prescriptive analytics methods are used by businesses to make optimal decisions. - Formulate optimization models to solve portfolio investment and staffing decisions - Analyze business needs and define a prescriptive analytics project plan