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Dec 18, 2024
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ECON 2200 - Introduction to Economic Data Analysis Using Python
This course introduces programming and statistics to students from different majors and teaches techniques that apply across many disciplines. No prior programming or statistics experience is necessary. The course introduces students to Python programming language to develop the ability to apply economic analysis and prediction techniques to real-world scenarios through working with real-world data sets. Topics covered include data types, tables, sequences, visualization, causality and experiments, testing hypotheses, estimation, prediction, and inference for regression.
Credit Hours: 3
OHIO BRICKS Arch: Constructed World
General Education Code (students who entered prior to Fall 2021-22): 2AS
Repeat/Retake Information: May be retaken two times excluding withdrawals, but only last course taken counts.
Lecture/Lab Hours: 3.0 lecture
Grades: Eligible Grades: A-F,WP,WF,WN,FN,AU,I
Learning Outcomes: - Students will be able to search for economic data from online databases provided by the OU Alden library and publicly available sources.
- Students will be able to analyze real-world data sets using Python programming language.
- Students will be able to use Python to present quantitative data by constructing graphs and tables.
- Students will be able to explain economic data presented in graphs and tables that they construct.
- Students will be able to calculate new variables for economic analysis.
- Students will be able to use Python to obtain descriptive statistics.
- Students will be able to write loops and selection structures.
- Students will be able to execute basic input/output operations and assignment statements.
- Students will be able to input/output data using text files.
- Students will be able to discuss common programming errors and how to debug a program.
- Students will be able to apply basic strings and string library functions.
- Students will be able to formulate meaningful hypotheses and test these hypotheses using empirical data.
- Students will be able to apply economic analysis and prediction techniques to real-world scenarios.
- Students will be able to interpret the results of their analysis and state conclusions for their hypotheses based on the significance of results.
- Students will be able to present their findings using written reports that include graphs, tables, and words.
- Students will be able to discuss policy implications of their findings, if relevant.
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