When using data analysis to improve organizational performance, it's vital to employ the tools that bring the data to life and keep people engaged in the process. Organizations in both the public and private sectors often use tools and frameworks to deliver the data, and the information the data might suggest, to its staff. This intermediate-level course will explain some of these measures and tools, describe some specific measurements, and explain the relationship between assessment and strategy. Summarizing the data with the correct tool can be the gating factor to reaching staff and effecting changes that spur performance improvement.
How are data-driven decisions put into practice in the real world? How do these decisions differ when applied to different sectors, such as health care, education and government? This intermediate-level course will provide answers to these questions as well as recommendations for decision-making based on data analytics for each sector. The course will begin with an introduction of Big Data, then continue into a deeper dive on its implications within each sector. Industry case studies make the concepts applicable in the real-world.
Whatever your profession. Whatever your field. As a professional, and certainly as a leader, you will be asked to make a decision based on data. This course will introduce the different types of decisions made in an organizational setting, why quantitative analytics is important, and how data quality can affect decision making. Since quantitative analytics is used in various settings, this intermediate-level course also offers insight into how research is used in different sectors. From a management perspective, the course highlights appropriate quantitative methods and ways to ensure quality and accuracy through research design.
There are a number of statistical tools and techniques that are commonly used by organizations to inform decision-making. These tools span numerous business functions and support many different objectives. This intermediate-level course describes, evaluates, and analyzes different statistical techniques and their real-world limitations and benefits. The course features crossover analysis, break-even analysis, cluster analysis, decision tree analysis as well as an introduction to regression.
This course is designed to help learners develop a solid understanding of the basic concepts and techniques that they will encounter as practitioners in the web analytics field. Learners in this course will expand their knowledge through games, videos, case studies, quizzes, and other engaging content. Topics of major concern that are discussed in Module 1 include a summary of web analytics concepts and important terms, along with the organizations and personnel who use web analytics. The course defines key performance indicators and discusses how they are chosen and implemented. A discussion of segmentation follows, with strategies for how to categorize website visitors. Students will also learn how to plan and assess website business strategies using web analytics.