Math in the World Around Us

Linear regression is a fundamental tool in statistics and data science for modeling the relationship between different parameters. It can be used for prediction, forecasting and error reduction by fitting a predictive model between a response variable and a collection of explanatory variables based on an observed data set. Through linear regression analysis, we can quantify the strength of the linear relationship between the response and different explanatory variables, and we can identify parameters that may contain redundant information.

This workshop introduces the basics of simple and multiple linear regression. We will present both mathematical theory and applications in the context of real data sets — ranging from survey results collected by the US National Center for Health Statistics (NHANES), to real estate listings in Sacramento, CA. After the talk, the R code used will be provided, so attendees can revisit examples of how to apply this foundational modeling method.

The integrated use of data science and machine learning in healthcare has grown in popularity in recent years with many applications becoming engrained in our healthcare systems. Recent advancements in digitalization of healthcare data, production of masses of data from both operational activities in a healthcare setting and at a patient level from sensors and scans etc, has enabled many more applications and research.

In this session we will discuss data science applications in the healthcare industry as well as some of the ethics and considerations required when delivering Data Science solutions in the industry.

What key principles of design and data viz do you need to know to create effective and clear graphs? This talk will cover preattentive attributes, Gestalt principles, and principles of color use. It will provide the key concepts from design and data viz research that you need to know to communicate data effectively. The talk will include examples to demonstrate applying the concepts and comparing data viz effectiveness.