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.
Prerequisite: Instructor permission.
Introductory topics in calculus, optimization, linear algebra and discrete mathematics useful for data scientists. Networking concepts relevant to data analytics approached from a mathematical point of view. Mathematical programming to implement a variety of numerical methods.
Prerequisites: Graduate standing.
Introduction to data analysis in Excel, Tableau, and Python; execute queries to extract data from a relational database; Data Science Life Cycle; tools and techniques to perform all the phases of the data science life cycle; Introduction to Machine Learning concepts.
Prerequisites: CIS 512 or DSA 512 or equivalent.
Introduction to Machine Learning Techniques for Data Science; mathematical methods; algorithms; application to academia, industry and business problems. Fundamental concepts and terms; methods, tools, and techniques. Supervised and unsupervised learning; identification of learning problems; data sources; analytical approaches; algorithm implementation; interpretation and reporting.
Prerequisites: Graduate status. Background of educational research; selection and development of research problems; sources of information and data; methods, tools, and techniques; collection, treatment, application, and interpretation of research data; organizing and writing a research report. Special Note: For all teacher candidates seeking initial certification at the master’s level, this course will include readings and projects with a special focus on exploring the history, philosophy, and role of education and the rights and responsibilities of stakeholders in the schooling process on a national and international scale.
Equivalent Courses: BME 601, MUL 689
Prerequisites: Graduate standing, instructor permission, and 3.0 minimum GPA.
Internship and team project (a.k.a. professional labs). Internships acquaint students with specialized resources in industry. Professional labs allow students to apply skills to real-world challenges.
Theories of effective communication; face-to-face communication; group problem solving; public speaking; power and leadership in organizational settings; persuasive messages and campaigns that public relations practitioners design for a variety of publics. Designed for graduate students interested in improving their workplace communication skills.
Prerequisite: Instructor permission.
Practical hands-on introduction to Data Science and Data Analytics tools and acquiring, storing, manipulating, and exploring data - both big and small. Examples from bioinformatics (e.g., genomics), health care informatics, urban and regional planning, astronomy and data journalism. Extensive writing of formal reports.
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.
Prerequisites: Graduate standing.
Current practices in project management as applied to math and science projects. Hands-on experience with the skills, tools, and techniques required in different phases of a project's life cycle, including project selection, project planning, project staffing and organization, task scheduling, project scope management, budgeting and progress reporting, risk management, quality management, project communications, and use of appropriate project management software tools. Techniques for communicating and motivating teams throughout the project life cycle. Emphasis on team building and practicing project management techniques through the use of science-based cases.
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