Lecture Topics
The following lectures are listed by topic and will occur in the duration of the summer program:
| General Topic | Description |
|---|---|
| The evolution and relevance of data science in the world today | 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. |
| Scientific methodologies for Biomedical Informatics projects and the key role the data science team plays | 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. |
| Data engineering and data modeling practices | Explore ways to extract insights from data using predictive analytics and artificial intelligence (AI), including machine learning and deep learning models. |
| Biomedical Informatics and Genomics | Examine the link between big data and health in our society through the lens of computational and traditional methods in biology and medicine and on research in genomics, proteomics (the large-scale study of protein), pharmacology, and other disciplines that cut across medical disciplines. |
| Health and Public Health Informatics | Explore the evolution of these disciplines at the intersection of data science, health information technology, health information management, and data analytics, which are 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. |
| Statistics and Statistical Machine Learning | Develop an understanding of statistics and statistical machine learning concepts and methods, 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. |
| Statistical Methods for Clinical Research using SAS | 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. |
| Data Science Industry Case Studies | Tell—and illustrate—stories that convey the meaning of results to decision-makers and stakeholders from various industries and explain how these results can be used to solve business problems. |