This foundational course provides an introduction to the key concepts of data science, machine learning, and image analysis. Students will learn the basics of handling image data, applying traditional machine learning techniques, and building their first image classification models.

This course focuses on using deep learning techniques for advanced image classification tasks. It covers more sophisticated architectures such as deeper CNNs and transfer learning to improve performance on complex image datasets.

This course focuses on predictive modeling techniques applied to clinical data, which may include electronic health records (EHR), medical histories, lab results, and sensor data to predict disease outcomes and patient risk factors.

This course provides an in-depth understanding of statistical methods, focusing on their application in data science. Students will learn fundamental and advanced statistical techniques for analyzing data and drawing meaningful conclusions. Emphasis will be placed on the use of statistics to guide decisions based on real-world data.

This course is focused on teaching students how to create insightful and effective visualizations using Python. Students will learn how to use popular Python libraries (such as Matplotlib, Seaborn, and Plotly) to explore, analyze, and communicate data in visually compelling ways.