Analytics

Data analytics is key to long-term viability but requires more than technological investments alone. It demands leaders who can confidently build organizations that embrace and communicate through data. This course helps you emerge from the information overload and help your organization claim ownership of its data. Through in-depth case studies and interactive discussions, you'll learn tools and techniques that strategically capture, analyze, and apply data to decision-making.

In today’s increasingly complex business world, critical decisions can become ‘shots in the dark’ unless informed by relevant data. However, applications and software produce an overwhelming abundance of data that can make it challenging to focus in on answers to critical questions. To effectively leverage data, organizations need to transform it into actionable information, yet many are struggling to do that. This course introduces a repeatable process you can use to ensure you’re getting the most out of your data. Over the three-day series, you’ll learn how to transform data into robust information to be leveraged in a variety of ways to support your success. You will have the opportunity to practice this process through hands-on exercises led by Penn State, reinforced by interactive discussions with peers, ensuring that the course learnings are practical and applicable to your business setting.

Understanding how to transform data into actionable insights is increasingly important at ALL levels of an organization, whether you are making everyday decisions or informing future strategic plans. However, many decision makers lack awareness of commonly used analytics tools and techniques that can base these decisions in information and data. This course introduces students to methods for applying descriptive (What happened?), predictive (What might happen?), and prescriptive (What should we make happen?) analytics techniques to various business and operational challenges. Over four half-days, students will practice combining business data, common software tools, and various modeling and analysis techniques to quickly gain insights about operational performance, predict future outcomes, and develop optimal courses of action for business problems that might otherwise have gone unnoticed or have been deemed too challenging to approach.