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of the following: Excel, Python, R, SAS, SPSS, SQL, Stata, or StatCrunch. In addition to focusing on the above courses, the Quantitative Tutoring Lab supports DATA-412/612. Some candidates may be
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of the following: Excel, Python, R, SAS, SPSS, SQL, Stata, or StatCrunch. In addition to focusing on the above courses, the Quantitative Tutoring Lab supports DATA-412/612. Some candidates may be
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Assist with processing and analyzing large document corpora using Python, LLMs, and AI-assisted tools such as Claude Code. Support the development and execution of data pipelines for qualitative and
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of the following: Excel, Python, R, SAS, SPSS, SQL, Stata, or StatCrunch. In addition to focusing on the above courses, the Quantitative Tutoring Lab supports DATA-412/612. Some candidates may be
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(such as providing support for PEER Center events). The intern must be an undergraduate or graduate student with an interest in economics and coursework or other experience in coding with Python, R, and/or
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insights from complex data sets. Advanced proficiency in Excel and other data analysis and collection tools such as Python, R, or SQL. Familiarity with data visualization techniques and tools (e.g., Tableau
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application submission. Statistics and statistical analysis tools, including RSQL, Excel, Python. Expertise in FOIA, PACER, and other public-records resources. Server management, especially AWS. Database
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and Connexion, Islandora, ArchivesSpace, Quartex, and Figshare. Experience with Python or other programming languages or tools. Familiarity with BIBFRAME, linked data principles, and generative AI
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such as ERP, CRM, data warehouses. Proficiency in SQL with the ability to write complex queries; familiarity with JavaScript, REST APIs, web services, and scripting in Python or R. Demonstrated technical