Spring 2025

DATA 543 Risk, Data Science and AI  [Syllabus]

Course Description: This three-credit-hour course introduces students to advanced techniques in data sciences, machine learning, and artificial intelligence and their application to the management of natural hazard and financial risks. Students will learn to discover, process, and visualize high-quality environmental and financial data. By the end of the course, students will be able to identify the financial impacts of emerging natural hazards and design risk management strategies to mitigate negative outcomes.  Students will be introduced to basic Python programming with the goal of applying data analysis and machine learning techniques to multi-objective socio-environmental systems. Structured case studies and in-class assignments will help students build a library of analytic tools to be used in a semester-long group project. Group projects will be a significant portion of each student’s grade and take the form of a Github repository with data, code, and instructions for reproducing results. Coursework will be split between lectures on applying data science techniques to risk management problems and group-based, in-class programming exercises. 

Class Time and Location: Tuesday and Thursday, 11:00 AM to 12:15 PM at Gardner Hall-Rm 0001

Fall 2024

DATA 140 Introduction to Data Structures and Management   [Syllabus]

Course Description: This foundational course is designed for students beginning their journey in Data Science as part of a BA program. It offers a comprehensive introduction to critical data science concepts through a structured, bottom-up approach. The course is divided into three core modules, each building upon the previous to deepen students' understanding and skills.

• Module 1 lays the groundwork by covering essential topics such as data types, data organization, attributes, and data quality. These are the building blocks that every data scientist must master.

• Module 2 advances this knowledge by delving into key data operations, including sorting and searching algorithms. This module also introduces more complex data structures like B-trees, which are vital for efficient data management.

• Module 3 takes the concepts from the first two modules and applies them to modern challenges in big data. Students will learn about contemporary data structures and techniques essential for handling large datasets. Additionally, this module covers critical topics in data visualization and introduces foundational machine-learning techniques.

By the end of this course, students will have developed a solid understanding of data structures and their applications, providing a strong foundation for more advanced coursework and real-world data science challenges.

Class Time and Location: Tuesday and Thursday, 12:30 PM to 1:45 PM at Greenlaw Hall-Rm 101