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We present a data-driven research over code repositories that are data science oriented. The goal is to compare their topics of interest and evolution over the COVID-19 pandemic period by analyzing Jupyter Notebook and Python projects from a year before and during the pandemic. We employ a state-of-art algorithm to find topics based on the repositories descriptions, and compare the performance of tuning its hyperparameters for better accuracy.

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