New York University 4 credits Data Science
This course provides a rigorous foundation in the theory and application of linear regression. The course covers key concepts in model selection, estimation, diagnostics, and interpretation through different lenses: descriptive, predictive, and causal. Additional topics include shrinkage methods, basic time series, and logistic regression. Students will implement these methods in Python. Counts toward the minor in Data Science but not toward the major (including any of the joint majors with DS-UA). Prerequisites: DS-UA 112 or MATH-UA 185.
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.