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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
Health Workforce Research Center, and Behavioral Health Workforce Research Center. The Data Analyst will * Perform data cleaning and statistical analysis using SAS, Stata, R, Python, and SQL. * Perform
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data related to climate and energy policies • Provide statistical analysis and coding support for a broad range of Initiative research, primarily in R and Python • Manage and maintain large datasets
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cereals production practices. PhD grantors: Aarhus University (DK) & Clermont Auvergne University (FR); keywords: IoRT network optimisation, data quality PhD-B: Data collection and analysis empowered with
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: Understanding of basic statistical methods and data analysis techniques that can be deployed in real-world use case scenarios. Data visualization proficiency: Expertise in using data visualization tools like
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customer centricity Applies data visualization techniques to communicate data through graphical representations Builds a narrative around a set of data to convey actionable findings from the analysis
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advanced statistical analysis, pathway & network analysis, Integrating complex clinical, biological and molecular data. Contributing to data visualization, figure generation and interpretation
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performance management system. Physical Requirements (With or Without Accommodations): • Visual acuity to read information from computer screens, forms and other printed materials and information. • Able
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practical experience working in R and/or Python for biological data analysis and visualization …you are interested in tumor–stroma interactions, particularly immune–endothelial crosstalk …you have a
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in research design, statistical analysis, and data visualization, with a proven ability to communicate complex findings to various audiences. This position reports directly to the Associate Provost
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to institutional research, program assessment,grant proposals, and state/federal compliance. Produce dashboards, tables, graphs, and narrative analysis of data to easily display and summarize student characteristics