12 courses with the subject IAN, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Managing, manipulating, and analyzing structured/unstructured data to understand relationships and generate useful insights. Principles such as programming for analytics, data visualization, statistical modeling, database design, high performance computing are discussed.
Introduction to fundamental statistical techniques for data analytics such as hypothesis testing, data transformation, estimation, confidence intervals, regressions models, ANOVA, multivariate analysis, non-parametric methods, and design of experiments.
Students are exposed to current approaches, techniques and best practices for collecting, cleaning and normalizing data, processing, storing, managing, securing and preparing structured and unstructured big data sets for analytics.
Introduction to machine learning and predictive analytics for Big Data. Some key components include deep learning, supervised, unsupervised models, regression, inductive learning, and time series analysis.
Data are analyzed to answer questions. Students are exposed to concepts and techniques to understand analytics results and appropriately infer relationships to answer questions and visualize results using contemporary techniques.
How data analytics is used to solve applied problems in varied contexts. Students will learn how to choose appropriate methodologies, manage data, conduct analyses and report results.
Students collect and analyze unstructured text data using web/API scraping methods, and then analyze their corpus using text mining and natural language processing. Additionally, students conduct a survey of relevant issues pertaining to privacy rights and intellectual property rights for web scraping and text mining methods.
A variety of concepts required to analyze biological data will be discussed. Sample topics include biological databases, sequence alignment, ontologies, reproducible science, etc.
Students collect remote/mobile data using a microcomputer (Arduino, Raspberry Pi) or mobile phone, and then analyze that data by creating a dashboard visualization of their data. Additionally, students conduct a survey of relevant issues pertaining to surveillance and privacy rights for remote/mobile data collection projects.
Introduction to healthcare data, data systems, and different kinds of applications of analytics for health care, including clinical and research applications.
Students engage relevant ethical and legal issues pertaining to datafication and intellectual property for informatics and analytics. Students conduct ethics and intellectual property research for a particular domain and then produce reports and presentations using reproducible methods.
Capstone course. Students complete a self-directed informatics and analytics project under the supervision of the course instructor, and they also complete career preparation training curriculum.
Source: University of North Carolina at Greensboro's catalog, linked per course · table learning_unit · CourseShelf publish 61
