Hypotheses-Driven Problem Solving Approach for Data Science
The ever-increasing computational capacity has enabled us to acquire, process and analyze larger data-sets and information. We increasingly want to take a data-driven lens to solve business problems. But business problems are inherently 'wicked in nature' - with multiple stakeholders, different problem definition, different solutions interdependence, constraints, amplifying loops etc.
There is no one trick to solve them. What is required is learning a structured approach to problem-solving that can be applied to a large set of these problems. One possible way is to use a Hypotheses Driven Approach - problems definition, scoping, issue identification and hypothesis generation - as a starting point for this. In this workshop, you will learn how to apply a hypothesis-driven approach to any business problem through seven pragmatic steps:
The focus will be to learn the principles through an applied case study and using an iterative and agile methodology.
- how to apply a hypothesis-driven approach to any business problem
Enterprises and Startups wanting to build Data Science Culture