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    1. Industry Resources - Strategy 835 - LibGuides at Duke University

      Marketline Advantage - Contains full text market research reports focusing on companies and broad industry sectors: consumer goods, energy, (...)

    2. Passport - Risk - LibGuides at Duke University

      You might find other useful assessments of risk in other reports in Passport, so feel free to explore. Charts and analysis in the (...)

    3. Interest Rates - Financial Markets - LibGuides at Duke University

      LSEG Workspace  - LSEG Workspace contains market data, company financials and filings, ESG data, earnings estimates, (...)

    4. Bonds - Financial Markets - LibGuides at Duke University

      LSEG Workspace  - LSEG Workspace contains market data, company financials and filings, ESG data, earnings estimates, (...)

    5. Interest Groups - PUBPOL 301 - LibGuides at Duke University

      Login and registration on the Guidestar site is not required for Duke user access; but you must create a personal Guidestar account to download (...)

    6. Projection Quick Reference Guide - ArcMap Desktop Introduction - LibGuides at Duke University

      Formats Glossary & Tools Common Procedures Toggle Dropdown Adding attribute data (join; spatial join) Addreses & coordinate (...)

    7. Overview - Bloomberg Professional Introduction - LibGuides at Duke University

      The Bloomberg Excel Add-in is a tool that delivers Bloomberg data directly into an Excel spreadsheet for custom analysis and (...)

    8. Overview - Bloomberg Professional Introduction - LibGuides at Duke University

      The Bloomberg Excel Add-in is a tool that delivers Bloomberg data directly into an Excel spreadsheet for custom analysis and (...)

    9. Executives & Employees - Company and Industry Research - LibGuides at Duke University

      Industry data sets include key ratios, benchmarks, constituents, financial metrics, etc. by sub-industry or geography (see Markets tab).

    10. Digital Humanities – Duke ScholarWorks

      Text analysis : Which methods for computational text analysis are appropriate, and what results might they yield?

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