Discover hidden relationships and opportunities to improve financial performance. Learn how to analyze what’s happened and forecast what may happen. Understand performance and make effective decisions Data mining and modeling are valuable tools for understanding the drivers of current and past performance to help determine what might happen. Get an introduction to key data concepts This course will teach you not just the definitions of data mining and data modeling, but also what it takes to analyze large amounts of data to generate insights, build projections, and make better, more informed strategic decisions. Recognize data mining and modeling approaches You’ll learn how to better understand key data mining and modeling approaches, as well as how they can be used and evaluated. You’ll also learn how data analytics can be used to: - Help mitigate risk - Streamline business operations - Personalize customer experiences Real-world examples This course is tailored specifically to accounting and finance professionals. It uses a mixture of real-world examples and case studies relevant to your role to give you different ways to understand and apply knowledge learned throughout the course. This course is part four of the four-part Data Processing and Analysis Certificate. Who Will Benefit - AICPA & CIMA members who want to understand current and past performance to determine what might happen in the future - Global accounting and finance professionals - Controllers - Finance directors - CFOs - CPAs Key Topics - Understanding the role of data mining and data modeling in a data analytics project - Understanding data mining tools and techniques - Understanding data modeling tools and techniques - Understanding associated risks and considerations Learning Outcomes - Distinguish between data mining and related terms. - Recognize the data analytics framework’s role in a data mining project. - Identify the goals and benefits of data mining. - Recognize the tasks associated with building a data mining model. - Identify the concept of clustering as a descriptive data mining technique. - Identify the appropriate data mining techniques and algorithms for predictive analytics. - Recognize how clustering techniques can be applied in practice. - Recognize how regression techniques can be applied in practice. - Recognize tools and programs that can be used for data mining and modeling.