Overview
* Write a 10-page professional business analytics report analyzing 10 years of stock performance data for a publicly traded company operating in a single industry.
* Business analytics techniques facilitate decision making by transforming large amounts of raw data into meaningful information. Many businesses rely on analysis of relevant historical data to make key strategic and operational decisions. In business analytics, you often need to draw conclusions about a population of interest, but gathering data on the entire population may not be feasible. In these cases, data are gathered from a sample or subset of the population, and analyses done on the sample are used to draw inferences regarding the overall population—a process referred to as inferential statistics. The smaller group (the sample) provides measurements (the statistic) that serve as estimates for the larger group (the population) and its true measurement (the parameter). Rather than restricting estimates to single numbers that would be exactly correct or incorrect, analysts calculate confidence intervals—ranges of possible values within which there can be a certain percent confidence that the true population parameter falls.
* Creating meaningful information from raw data involves two essential steps: first, representing data effectively in graphical format and calculating required statistics; second, interpreting those graphics and statistics to apply them in the business context. In this assessment, you will sharpen your analytics skills by downloading real-life stock data, creating graphical representations (scatterplots and histograms), calculating descriptive statistics (mean, median, mode, standard deviation), and connecting these interpretations explicitly to business implications. The statistical techniques covered in this course are parametric techniques, which require certain assumptions about underlying distributions for results to be reliable. Nonparametric techniques, which require no assumptions about underlying distributions, are often used when parametric assumptions are not met, though these are beyond the scope of this introductory course.