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Goal The objective of this post is to demonstrate how Polars performance is much better than other open-source libraries in a variety of data analysis tasks, such as datacleaning, datawrangling, and data visualization. ? Contributions welcome ! ?Acknowledgments
Step 2: Numerical Computation in MATLAB Once the data is cleaned, you can use MATLAB for heavy numerical computations. You can load the cleaneddata and use MATLAB’s extensive mathematical functions for analysis. Load the cleaneddata from the CSV file, and perform statistical tests or models like linear regression.
Here are some project ideas suitable for students interested in big data analytics with Python: 1. Kaggle datasets) and use Python’s Pandas library to perform datacleaning, datawrangling, and exploratory data analysis (EDA). Analyzing Large Datasets: Choose a large dataset from public sources (e.g.,
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