Materials from a 90-minute Hands-on Workshop at WUSS in Sacramento, California, on 05SEP2024.
Materials provided:
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Examples and Exercises as an interactive Google Colab Notebook
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Solutions to all Exercises as an interactive Google Colab Notebook
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Solutions to all Exercises as a PDF file
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Slides as a PDF file
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Mock Analysis Request Form as a PDF file
Congratulations! It's your first day as an intern for the Research Center.
A researcher wants to perform a retrospective cohort study to assess the risk of heart failure following exposure to one of two drugs. As the analyst, you're tasked with creating the analytic dataset and performing the analysis described in the provided Analysis Request Form.
This will include the following steps:
- Importing, exploring, and cleaning raw data
- Combining and manipulating datasets to create variables for analysis
- Statistical analysis, table creation, and output delivery
Along the way, we'll also look at equivalent SAS code for context.
This scenario is completely fictional, and all data were created using the Python package faker.
A Google account will be needed to interact with code examples in https://colab.research.google.com/
If you don't already have a Google Account, you can create one for free at https://accounts.google.com/signup
This Hands-on Workshop is aimed at SAS programmers of all skill levels, including those with no prior experience using Python or JupyterLab. The equivalent SAS code includes DATA steps and procedures like PROC EXPORT, PROC FORMAT, PROC FREQ, PROC IMPORT, PROC LOGISTIC, PROC SGPLOT, PROC SORT, PROC SQL, PROC SUMMARY, and PROC TABULATE.
We also recommend a relatively new computer with a broadband internet connection and a modern web browser.
After successfully completing this workshop, we will be equipped for the following:
- Using Google Colab for Python script development.
- Using Python for common data-analysis tasks, including importing files, exploring dataset contents, correcting data anomalies, combining datasets, saving datasets, and building statistical models.
This project is licensed under the MIT License. See the LICENSE file for details.
This project is in no way affiliated with SAS Institute Inc.