Exploring and Producing Data for Business Decision Making

This course provides an analytical framework to help you evaluate key problems in a structured fashion and will equip you with tools to better manage the uncertainties that pervade and complicate business processes. Specifically, you will learn how to summarize data and learn concepts of frequency, normal distribution, statistical studies, sampling, and confidence intervals. While you will be introduced to some of the science of what is being taught, the focus will be on applying the methodologies. This will be accomplished through the use of Excel and data sets from different disciplines, allowing you to see the use of statistics in a range of settings. The course will focus not only on explaining these concepts, but also understanding and interpreting the results obtained. You will be able to: • Summarize large data sets in graphical, tabular, and numerical forms • Understand the significance of proper sampling and why one can rely on sample information • Understand why normal distribution can be used in a wide range of settings • Use sample information to make inferences about the population with a certain level of confidence about the accuracy of the estimations • Use Excel for statistical analysis This course is part of Gies College of Business’ suite of online programs, including the iMBA and iMSM. Learn more about admission into these programs and explore how your Coursera work can be leveraged if accepted into a degree program at https://degrees.giesbusiness.illinois.edu/idegrees/.

• Course Orientation
• You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
• Module 1: Introduction and Summarizing Data
• Data is all around you, but what is the data telling you? The first step in making better decisions and taking action is to get a good understanding of information you have gathered. In this module we will learn about some of the tools in statistics that help us achieve this.
• Module 2: Descriptive Statistics and Probability Distributions
• We all have heard the phrase that a "picture is worth a thousand words," but you certainly don’t want one of those to be "what exactly am I looking at?" So, now that you know to use "pictures" to summarize your data, let’s make those pictures easier to understand.
• Module 3: Sampling and Central Limit Theorem
• You are charged with analyzing a market segment for your company. You and your team have figured out what variables you need to understand; you also have an idea what factors might be influencing these variables of interest. Now you are ready to do your analysis. But, wait! Where is the data? How do you begin to get the data? In this module we will review the means by which you can begin to produce data – the concepts of sampling and Central Limit Theorem – and will help you understand how to produce "good" sample data and why sample data will work.
• Module 4: Inference
• You have sample data and have done the analysis – you think you can say something about the population based on your sample study. But, do you have a sense of what are the chances of you being right or wrong? How can you be surer? What else should you have considered? In this module, you will learn how to find the answers to these questions.