University of Delaware 3 credits BANA
This course provides an introduction to the fundamental concepts and practical applications of machine learning in data-driven environments. Students will explore techniques for preparing, analyzing, and modeling data, while gaining exposure to key methods in both supervised and unsupervised learning. Topics may include data preprocessing, classification, regression, clustering, model evaluation, and other relevant machine learning approaches. Emphasis is placed on applying these techniques to support data-driven decision-making in business contexts. The course incorporates hands-on work with programming tools and platforms commonly used in data analytics and machine learning. Prerequisite: BANA 262 (preferred) or MATH 202 , and MISY 225 or CISC 108 .
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.