Data-driven Decision Making

Yes, but the hands-on work is business-focused rather than coding-heavy. You'll complete an interactive simulation where you make decisions in a fictional client scenario, and you'll finish with a peer-reviewed project that asks you to create a data analysis plan for a business problem. That practice helps you apply the framework to realistic situations instead of only learning the concepts in the abstract.

You'll cover the main ideas behind data analytics in business, including how organizations use data, how different data types affect analysis, and why big data matters. The course also introduces a repeatable analytics framework, common approaches from descriptive work to predictive methods, and the role of visualization and tools such as Excel and Python. Overall, it's about learning how to frame a business problem, connect data to decisions, and communicate useful insights.

After finishing, you should be able to explain how data and analytics support business decisions and use a simple framework to work through a business problem. You'll be able to distinguish common data types, discuss where big data fits, and recognize which analysis approaches or tools may suit a situation. For example, you could outline a data analysis plan for a case and connect your recommendations to the outcome the business is trying to improve.

It's more concept-first than hands-on. Most of the course is about understanding how data, analytics, and decision-making fit together, with quizzes, a simulation, and a final project used to reinforce those ideas rather than replace them. It's a better fit if you want business context and guided application more than tool-by-tool practice.