Учебники по Data Science

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.

Learn how to calculate and interpret the likelihood of multiple events occurring simultaneously. Discover practical applications in predictive modeling, risk assessment, and machine learning that solve complex data science challenges.

Learn the mathematical foundations, discover multiple types and their applications, and explore how standard error enhances statistical inference and decision-making.

Learn how to use Cramer's rule to solve systems of linear equations through determinants, with practical examples.

Learn how to calculate cumulative probabilities and probability density using NORM.DIST in Excel. Understand its syntax, key arguments, and real-world examples.

Learn how to evaluate your model’s accuracy using mean absolute error. Understand when and why to use MAE to make your data-driven decisions more reliable.

Discover the math and meaning behind power laws. Learn how they model rare events, reveal scale-free patterns, and show up in everything from earthquakes to AI.

Understand how to model the probability of first success in repeated trials, explore its unique memoryless property, and discover practical applications across industries from quality control to customer acquisition.