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| AGR 33300 - Data Science For Agriculture |
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Associated Term:
Spring 2024
Learning Outcomes: 1. Construct a research question that helps address a decision. 2. Describe different types of experimental designs and discuss the differences between observational and experimental studies. 3. Identify data needed to address various research questions. 4. Identify how these data sources are used in data analysis: agronomics, machine data, maps, spreadsheets, sensor data. 5. Describe how various data sets are acquired. 6. Describe how the following impact data ethics: ownership, storage, access. 7. Assess data quality and utility. 9. Identify potential limitations of a dataset. 10. Describe the following aspects of data wrangling: data formats, data compatibility, mobility. 11. Describe the following aspects of data management: storage, curation, metadata, FAIR (findable, accessible, interoperable, reusable). 12. List reasons for filtering, cleaning, and pre-processing data. 13. Describe tools for data cleaning. 14. Integrate disparate data sets. 15. Describe uses for the following in data visualization: bar charts, line charts, maps, tables. 16. Use the following tools to analyze data: correlations, mean generation, confidence intervals, simple model building, R Python. 17. Make decisions based on data outcomes. Required Materials: Technical Requirements: |
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