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Fall 2022
Apr 25, 2024
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Information Select the Course Number to get further detail on the course. Select the desired Schedule Type to find available classes for the course. The Schedule Type links will be available only when the schedule of classes is available for the selected term.

AGEC 20201 - Introduction To Data Analytics For Agricultural Business
Credit Hours: 3.00. Overview of data analytics in agricultural business. Students are exposed to an overview of software tools with a primary focus on Excel, introduction to relational databases, and data visualization. Focus is in using financial, accounting, and economic data to make management decisions. Laptop required.
3.000 Credit hours

Syllabus Available
Levels: Undergraduate, Graduate, Professional
Schedule Types: Distance Learning, Lecture

Offered By: College of Agriculture
Department: Agricultural Economics

Course Attributes:
Credit By Exam, Lower Division

May be offered at any of the following campuses:     
      West Lafayette

Learning Outcomes: 1. Create and format a basic Excel worksheet. 2. Apply formulas and functions to calculate and analyze data. 3. Create charts and insert sparklines to represent data visually. 4. Manage and analyze large sets of data. 5. Analyze data by creating subtotals, PivotTables, and PivotCharts. 6. Use decision-making tools such as Range Names, Data Tables, and Goal Seek. 7. Manipulate data using date, logical, statistical, and financial functions. 8. Employ statistical functions to analyze data for decision making. 9. Demonstrate data management and error control within workbooks containing links and 3-D formulas. 10. Import, format, and manipulate data from external sources. 11. Manipulate data using text, database, and lookup functions. 12. Demonstrate how to design templates, insert comments, create macros, and use VBA. 13. Read an ERD Diagram. 14. Learn elements of Structured Query Language including the ability to create tables, retrieve, update, and delete data. 15. Understand usage of primary and foreign keys. 16. Create a relational database. 17. Understand different data types. 18. Create and format visualizations. 19. Create visualizations for nominal, ordinal, interval, and ratio scale data. 20. Create map visualizations.



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