University of Delaware 3 credits BANA
This course focuses on interpretable data modeling for insight and interpretability. Students will learn to model and represent business problems using the language of probabilistic graphical models, translate those models into a form amenable to computation, and visually present computational insights back to stakeholders in a way that communicates insight and inspires action. Major topics include: Probabilistic Models, Causal Modelling, Probabilistic Programming (e.g. Turing.jl or Stan), Sampling Algorithms (e.g. MCMC), and Probabilistic Inference. Prerequisite: BANA 345.
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.