Exploratory data analysis (EDA) on complex datasets requires tools that reveal deep, hidden patterns, including overlapping distributions and outliers. Open-source Python data visualization libraries like…
Iván Palomares Carrascosa is a leader, writer, speaker, and adviser in AI, machine learning, deep learning & LLMs. He trains and guides others in harnessing AI in the real world.
Discovering hidden, multi-dimensional patterns in large datasets doesn’t necessarily demand costly enterprise analytics software if you get acquainted enough with the right open-source tools.
This article shows three examples of using NumPy array operations — known as vectorized operations — to run simulations like Monte Carlo or Bootstrap models on large datasets nearly instantly.
Compare Q-Q plots, Shapiro-Wilk, and Anderson-Darling tests to check data normality before running statistical analyses.
Learn why small sample sizes produce misleading results through false negatives, low power, and misread non-significance.
Learn how degrees of freedom affects spread calculations, probability curve shapes, and model overfitting.
Explain confidence intervals to non-technical stakeholders using three intuitive, math-free analogies.
Cluster mixed datasets in Python using the K-prototypes algorithm with step-by-step code and examples.
Prepare mixed datasets for clustering in Python by encoding categorical features and scaling numerical ones.
Understand how Gower’s distance measures dissimilarity between data points with mixed variable types.