Learn about the evolution of Data Science as a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations.

Discover various methodologies used to prepare data for analysis and processing, perform advanced data analysis, and present the results to reveal patterns and enable stakeholders to draw informed conclusions.

Explore ways to extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models

Explore ways to extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models

Explore the evolution of these disciplines at the intersection of data science, health information technology, health information management, and data analytics focused on improving health and healthcare by bringing theory into practice through enabling technologies. Learn about the application of informatics in areas of public health, including surveillance, social media analytics, prevention, preparedness, and health promotion.

Develop an understanding of the concepts and methods of statistics and statistical machine learning, such as linear/logistic regression, support vector machines, random forests, etc. Learn how statistics and statistical machine learning methods are used to analyze and interpret data with R/Python.

Introduce some commonly used statistical methods used in clinical research, biometrics, epidemiology, and other health-related research applications. The lectures will include examples with complete data sets, design layouts, and SAS code. The goal is to bridge the gap between the analytic approaches in statistical courses and the algorithmic nature of applying statistical software.