Microsoft Excel: Essential Statistics for Data Analysis

This is a hands-on, project-based course designed to help you learn and apply essential statistics concepts for data analysis and business intelligence. Our goal is to simplify and demystify the world of statistics using familiar spreadsheet tools like Microsoft Excel, and empower everyday people to understand and apply these tools and techniques - even if you have absolutely no background in math or stats. We'll start by discussing the role of statistics in business intelligence, the difference between sample and population data, and the importance of using statistical techniques to make smart predictions and data-driven decisions.

Next we'll explore our data using descriptive statistics and probability distributions, introduce the normal distribution and empirical rule, and learn how to apply the central limit theorem to make inferences about populations of any type. From there we'll practice making estimates with confidence intervals, and using hypothesis tests to evaluate assumptions about unknown population parameters. We'll introduce the basic hypothesis testing framework, then dive into concepts like null and alternative hypotheses, t-scores, p-values, type I vs.

type II errors, and more. Last but not least, we'll introduce the fundamentals of regression analysis, explore the difference between correlation and causation, and practice using basic linear regression models to make predictions using Excel's Analysis Toolpak. Throughout the course, you'll play the role of a Recruitment Analyst for Maven Business School.

Your goal is to use the statistical techniques you've learned to explore student data, predict the performance of future classes, and propose changes to help improve graduate outcomes. You'll also practice applying your skills to 5 real-world BONUS PROJECTS, and use statistics to explore data from restaurants, medical centers, pharmaceutical companys, safety teams, airlines, and more. COURSE OUTLINE:Why Statistics.