University of Arizona 3 credits Mathematics
This course, a core course in the Professional Masters Degree Program in Data Science and Applied Statistics, introduces the statistical concepts that drive modern data science -- summarizing and visualizing data, its associated technical and ethical issues, the axioms of probability and Bayes' theorem, random variables, their distributions and properties of expectation, families of random variables commonly seen in data science, law of large numbers and Monte Carlo simulation, central limit theorem and delta method, method of moments, maximum likelihood, and Bayesian approaches to estimation, hypothesis testing focused on likelihood based approaches in t-tests, proportion tests, contingency tables, analysis of variance, linear and logistic regression.
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.