CIS447 - Measuring Relationships and Uncertainty

Course Description

Data scientists make decisions by inferring the characteristics of a large population based on the characteristics of samples from that population. Basing a decision on samples is necessary since it would not be possible to measure every individual or unit in a population. However, it also means that data scientists need to consider the potential variability among samples before using those samples to make conclusions about the population. The variability across samples leads to uncertainty in decision-making, and understanding and quantifying that uncertainty is a key aspect of data science.

Throughout this course, Professor Basu will guide you through the nuances of understanding and quantifying the uncertainty around your results, and through making decisions in the face of that uncertainty. In data science, simulations offer a powerful framework with which to understand the uncertainty around your data, so you will learn to perform simulations in R and use a simulation-based framework to quantify uncertainty when studying the relationship between categorical variables. You will also use resampling techniques to understand numerical variables and compare their summary statistics across different levels of a categorical variable. Often, data scientists search for relationships between numerical variables and use one numerical variable to predict another numerical variable, and you will do this by building a prediction rule with linear regression while keeping the uncertainty of your results in mind. Finally, you will use the errors from linear regression to compare prediction rules and determine which prediction rules fit your data best. This course involves many hands-on coding exercises in R which will help you gain confidence in your programming skills.

System requirements: This course contains a virtual programming environment that does not support the use of Safari, Edge, tablets, or mobile devices. Please use Chrome, Firefox, or Internet Explorer on a computer for this course.

“Exploring Data Sets With R” and “Summarizing and Visualizing Data” must be completed prior to starting this course.

Faculty Author

Benefits to the Learner

• Formulate hypotheses, use simulations to test those hypotheses, and understand the level of confidence you should place in your results
• Use resampling techniques to understand the uncertainty present in groups of numerical variables
• Use linear regression to build prediction rules with one numerical predictor
• Use multiple linear regression to build prediction rules with more than one numerical predictor and compare prediction rules

Target Audience

• Current and aspiring data scientists and analysts
• Business decision makers
• Marketing analysts
• Consultants
• Executives
• Anyone seeking to gain deeper exposure to data science

Applies Towards the Following Certificates

• Data Science Essentials : Core Courses