Data Science Tutorials

Learn ensemble learning with Python. This hands-on tutorial covers bagging vs boosting, Random Forest, and XGBoost with code examples on a real dataset.

Accuracy isn't enough. Learn the difference between precision and recall, understand the trade-off, and choose the right metric for your model.

Learn what cost functions are, and how and when to use them. Includes practical examples.

Understand how CFA tests theoretical models by linking observed indicators to latent constructs. Learn the steps, assumptions, and extensions that make CFA essential in measurement validation and structural equation modeling.

Learn how to calculate space complexity using asymptotic notation, how memory components like recursion, data structures, and auxiliary space add up, and how to reduce space through in-place techniques.

Understand how Facebook Prophet models trends, seasonality, and special events for accurate and interpretable forecasts.

Understand how uncertainties in measurements affect calculated results and learn formulas and methods to quantify them across various mathematical operations.

Learn how UMAP simplifies high-dimensional data visualization with detailed explanations, practical use cases, and comparisons to other dimensionality reduction methods, including t-SNE and PCA.

This guide explains the mathematical properties of Softplus, its advantages and trade-offs, implementation in PyTorch, and when to switch from ReLU.

Understand discrete probability distributions in data science. Explore PMF, CDF, and major types like Bernoulli, Binomial, and Poisson with Python examples.

Learn when and how to use Python's reduce(). Includes practical examples and best practices.

Learn the mathematical foundations, parameter estimation techniques, and diverse applications of this probability distribution across engineering, medicine, and environmental sciences.