Data Sciences (DATA)

An interdisciplinary introduction to how data is organized, managed, visualized and analyzed across STEM disciplines. Ethical collection and use of data, with real-world test cases. Fundamentals of the tools and applications used in the analysis of data sets.

This is an introductory course on machine learning with focus on regression and classification methods. The objective is to familiarize students with some basic models and algorithms for machine learning and prepare them for research or industry application of machine learning techniques. Several software libraries and publicly available datasets will be used to illustrate the application of these techniques. The emphasis will be on machine learning algorithms and application, with some broad explanation of the underlying principles.

This course introduces students to the principles and tools of data science. It provides a foundation for properly collecting and analyzing data to draw insights and to answer data-driven questions. By using real-world examples, this course prompts students to think critically about applying these understandings to their workplace. Students get a broad overview of data warehousing and querying as well as data exploration, visualization and analysis. This course also introduces students to the principles of ethics, privacy, fairness and accountability in collection and management of databases and in making decisions based on data.

This course will introduce students to industry-standard graphic and data design techniques to create effective visualization and data storytelling. Students will also learn about the ethical implications of data visualization and evaluate the reliability and validity of data sets.

This course will emphasize the core underlying principles, models and applications specific to machine learning and systems in production. In addition to gaining conceptual understanding of the mathematics underlying various machine learning models, students will gain hands-on experience in applying various machine learning models to real datasets using production-ready software platforms and programming languages.

This course is designed to cover two pivotal areas: social and geospatial network analysis. It is tailored for students and professionals who are interested in applying data science techniques to environmental and societal challenges. Through this course, participants will learn to harness the power of network analysis in the context of social structures and geospatial data, enabling them to uncover patterns, relationships and insights critical for addressing complex issues in our society and environment.