GabrielReira/Data-Science-Challenges
Four challenges involving data science knowledge and process automation using Python.
This collection presents practical data science challenges demonstrating how to analyze business data and automate repetitive tasks. It takes raw business data, often from Excel spreadsheets, and produces actionable insights, automated reports, or predictive pricing tools. Business analysts, operations managers, and data scientists can use these solutions to streamline workflows and inform decision-making.
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Use this if you need concrete examples of how data analysis, process automation, and machine learning can solve common business problems like customer churn or report generation.
Not ideal if you are looking for a ready-to-use software product rather than code examples and learning exercises.
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Jupyter Notebook
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MIT
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Nov 27, 2024
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