/ Students will explore the foundational aspects of data analytics in this course learning fundamental concepts such as data types, structures, and processes, with a focus on practical data visualization. Introductory skills are developed in data exploration, summarization, visualization, and effective data presentation through hands-on projects, while learning practical skills in spreadsheet, visualization, and applied statistical coding software. Lastly, students will gain introductory exposure to ethical considerations associated with working and analyzing data. This course provides a solid entry point for understanding the essential principles of data analytics. No prerequisites.
Students will learn to program in R and Python, engage in exploratory data analysis techniques, data visualization, and the basics of data wrangling (data cleaning) along with foundational data modeling techniques. Prerequisites: DATA 101, CSCI 110, and MATH 221 or MATH 228.
Students will learn foundational concepts and techniques in statistical learning focusing on building and evaluating models for regression and classification problems. Topics will include data preprocessing, error/accuracy metrics, data splitting, cross-validation, and model selection. These topics will be presented through the use of linear/logistic regression, penalized regression, k-nearest neighbors, and tree-based models. Additional topics may include, but are not limited to, clustering, neural networks, PCA, PCR, support vector machines, and splines/smoothing. Students will work on projects requiring them to select and use appropriate tools for solving open-ended problems. Communication of results to various audiences will be emphasized. Prerequisites: DATA 201, and MATH 203 is recommended.
Students will gain exposure to real world data analytics through the successful application of their theoretical and practical skills to solving problems in science and industry in this capstone course. This course focuses on the application of content learned throughout the major to a large-scale data project with an additional emphasis on ethics, social responsibility, and the communication of the results. Prerequisites: DATA 301, CSCI 330.
Students will engage in the data endeavor process in this course, exploring the key stages of acquisition, preparation, analysis, and action in the context of real-world problem-solving. Students will engage in all aspects of applied data analysis in a project-based environment. Statistical, visualization, and database software will be utilized throughout this process with a particular emphasis on exposing students to SQL, in addition to expanding modeling capabilities such as forecasting techniques, applied regression, and machine learning algorithms. Prerequisite: MATH228 or MATH221.
