6 Data Science Coding Skills, Explained with Real Code Examples
Blog post from Zerve
The text presents six essential data science techniques, each demonstrated through code examples available in public Zerve canvases for hands-on exploration. Techniques covered include one-hot encoding for transforming categorical data into numerical formats, groupby aggregations for summarizing data efficiently, and creating custom transformers to extend scikit-learn's functionality. It also discusses SQL-like window functions using pandas, the strategic use of custom loss functions to align machine learning models with business objectives, and the importance of vectorization in optimizing code performance for large datasets. Each section emphasizes practical coding patterns that data scientists routinely use, and the Zerve platform allows users to interact with, adapt, and integrate these examples into their workflows without requiring a login.
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