|MAT 646||Online Synchronous||T/R 4:30-5:45||2723||
Dr. Chaitali Ghosh
|PSM 602||Online Synchronous||T 6:00 - 8:40||1847||
Dr. David Jacome
|DSA 601||Online Synchronous||W 4:30 - 7:10||3131||
Matt Nagowski and
|DSA 610||Online Synchronous||M 4:30 - 7:10||3498||Betsy McCall|
|DSA 650||Online Synchronous||T 6:00 - 8:40||3039||Dr. Joana Gaia & Jessica Copeland|
|GEG 584||Online Synchronous||W 7:30 - 8:55||2376||Dr. Wende Mix|
|GEG 585||Online Synchronous||R
|Dr. Wende Mix|
|HEA 730||Online Asynchronous||4314||Dr. Patrick McDonald|
|DSA 690||Online Asynchronous||4109||Dr. Wende Mix|
If you have any questions or issues with registration please contact the Program Coordinator Heather Camppbell at firstname.lastname@example.org.
For instruction on how to search for classes, register or withdraw click this link and look under the GET HELP section on the right hand side.
Prerequisite: Instructor permission.
Descriptive statistics, probability concepts, discrete and continuous probability distributions, sampling distributions, interval estimation and hypothesis testing of one and two population means, proportions and variances, non-parametric tests, simple linear regression and correlation, one-way analysis of variance. Offered every fall semester.
PLUS COURSES (BUSINESS)
Prerequisites: Graduate-level standing.
Intend to develop strategic thinking about communication of quantitative information and improve writing, presentation, and interpersonal communication skills for mathematicians and scientists in a variety of settings (i.e. industrial, managerial, academic, research). Includes a review of “best practices” or guidelines that have been derived from both research and experience. Students will put those guidelines into practice, using a workshop format that will rely heavily on discussion and in-class exercises.
Prerequisites: MAT 126, MAT 311, CIS 512, or Instructor permission
Applied introduction to building predictive, machine-learning models for real-world problems; learning Python computing environment, basic data analysis, management; data visualization and reporting using machine learning methods, including k-nearest neighbor, linear models, naive Bayesian models, decision trees, random forests, and neural networks. Sample data sets from across industry professions.
Prerequisite: Graduate status.
Introduction to a “big picture” understanding of data flow for strategic, data-driven decision making, including data storage, data organization, data gathering and preparation, exploratory data analysis, and meaningful visualizations and communication. Includes hands-on practice. Offered occasionally, beginning spring 2020.
Prerequisites: Instructor permission
Elements, methods and tools of an organization’s data strategy and its governance. Components of a data strategy for each phase in the data lifecycle, tools for executing the strategy, and aligning the data strategy with the emerging needs of the organization. Policies, procedures, standards, and training for establishing authority over the ownership and use of data assets and its security.
Prerequisites: Instructor permission, programming experience required
Introduction to Python programming focusing on the development of Python scripts and custom tools for processing and analysis of geospatial data. Automating geoprocessing workflows, creating custom analysis tool, and customizing user interfaces.
This course will cover the fundamentals of effective data-driven storytelling. Students will learn how to analyze data, detect stories within datasets and communicate findings in oral, written, and interactive visual delivery modes for various audiences.
Prerequisites: Graduate status.
This course will have 3 components; internship, professional lab, and seminar. The internship will acquaint the student with the specialized resources of various external organizations and will assist the student in understanding the nature of employment activities in offices/agencies that employ data scientists. The professional lab will provide the student with an opportunity to apply the skills and knowledge gained in the classroom to actual problems and tasks while still in an academic environment.
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