COURSE: CSCE 155T

Introduction to Computer Science I: Informatics

Course Description:

This 16-week introductory course offers a foundation in computer science and data science, equipping students with essential programming, algorithmic thinking, and problem-solving skills. Using Python as the primary programming language, the course covers fundamental programming constructs, data structures, file handling, and an introduction to data visualization techniques. By applying these skills to real-world datasets and problems, students gain practical experience and develop interdisciplinary connections. Designed for students without prior programming experience, the course aims to create a supportive learning environment that inspires students to further hone their computational abilities and critical thinking skills. ---

Module Topic:Designing Methods to Estimate US Poverty Data Using Spatial Interpolation and Ethical Considerations

--- Note: This module focuses on the technical aspects and hands-on exercises related to spatial interpolation and data-driven decision-making. It builds upon the foundation established in previous lectures, where guest a speaker from the Department of Philosophy provided an introduction to ethical considerations in computer science applications. Acknowledgment: This module was co-developed by Colton Harper and Zachariah Wrublewski.

Module Overview:

In this module, students work with an interactive Python notebook that guides them through the process of designing, coding, and evaluating a method to estimate US poverty data using spatial interpolation techniques. The primary focus is on exploring the ethical considerations involved in designing and implementing data-driven solutions, as well as understanding the implications of these decisions on various stakeholders. ---

Learning Objectives:

Through hands-on coding exercises, collaborative problem-solving, and reflective discussions, students will: 1. Enhance students' understanding of the ethical considerations in using spatial interpolation methods for estimating poverty data in real-world scenarios. 2. Encourage students to think critically about the potential consequences of their methodological choices for various stakeholders, including direct and indirect ones. 3. Reinforce students' ability to apply spatial interpolation techniques while considering the ethical dimensions of their work.

Key Questions:

- What ethical considerations should be taken into account when using spatial interpolation methods to estimate poverty data? - How do the choices made in designing a method impact various stakeholders, both directly and indirectly? - How can students balance the need for accurate estimations with the ethical implications of their work?

Key Concepts:

- Ethical dimensions in spatial interpolation and data-driven decision-making Identifying and addressing the needs of direct and indirect stakeholders - Evaluating trade-offs and potential consequences of methodological choices in a design/development context - Balancing accuracy, fairness, and transparency in data-driven solutions - Developing a responsible and informed approach to the design and implementation of algorithms and data analysis methods - Navigating ethical dilemmas and complexities in computer science applications [Jump To Scenario](#scenario)