This course is about mining knowledge from data in order to gain useful insights and predictions. From theory to practice, the course investigates all stages of the knowledge discovery process, which includes data preprocessing, exploratory data analysis, prediction and discovery through regression and classification, clustering, association analysis, anomaly detection, and postprocessing.

Advanced Topics in Data Science is a comprehensive course designed to provide students with both foundational and advanced concepts in data science and machine learning. The course begins with an introduction to programming and data analysis using Python, equipping students with essential coding and analytical skills. It covers core machine learning techniques, including regression methods, classification approaches, and anomaly detection, before advancing into deep learning with neural networks (NNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). Students will also explore dimensionality reduction with Principal Component Analysis (PCA) and learn about autoencoders for unsupervised representation learning. A key focus of the course is the practical application of these techniques, culminating in model deployment using Amazon SageMaker. This hands-on approach prepares students to develop and implement scalable machine learning solutions in real-world environments. Prerequisite:DSCI 501

This course is an introduction to data visualization. It includes data preprocessing and focuses on specific tools and techniques necessary to visualize complex data. Data visualization topics covered include design principles, perception, color, statistical graphs, maps, trees and networks, and other topics as appropriate. Visualization tools may include JavaScript D3 library, Python, and R, and commercially available software such as Tableau, etc. The course introduces the techniques necessary to successfully implement visualization projects using the programming languages studied.

Thesis credit may be earned for significant work toward the writing of a master’s thesis. This thesis may be used to fulfill the culminating project requirement.

The practicum is an opportunity to directly experience the work of a data scientist or data analytics professional. It consists of project-based learning on a significant and contributory business objective in conjunction with practicing professionals in one of many appropriate industries. May be repeated up to 6 credits.