Учебники по Data Science

From ancient Egyptian builders to modern data science, discover how a^² + b^² = c^² revolutionized mathematics and continues to shape our technological world.

Learn how to fix Pandas' SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame.

Explore how linear search works and why it’s ideal for small, unsorted datasets. Discover simple Python implementations, including iterative and recursive methods, and learn when to choose linear search over other algorithms.

Understand how to derive the characteristic equation of a matrix and explore its core properties. Discover how eigenvalues and eigenvectors reveal patterns in data science applications. Build a solid foundation in linear algebra for machine learning.

Discover the essentials of depth-first search for navigating graphs and trees. Implement DFS in Python using recursion and iteration, and see how DFS compares to breadth-first search and Dijkstra’s algorithm.

Discover the power of the geometric mean in finance, biology, and data science. Learn how to calculate it, when to use it, and why it's useful for analyzing growth rates.

Discover how breadth-first search systematically explores nodes and edges in graphs. Learn its level-by-level approach to ensure the shortest path in unweighted networks. Apply BFS across data science, AI, and networking fields.

Learn how to model and solve optimization problems using Pyomo, a powerful Python library. Explore practical examples from linear and nonlinear optimization!

Explore the arithmetic mean's role in data analysis. Learn its formula, applications, and how it compares to other kinds of means and other statistical measures, and understand when each is most useful.

Uncover the impact of multicollinearity on regression models. Discover techniques to detect multicollinearity and maintain model reliability. Learn how to address multicollinearity with practical solutions.

Learn everything you need to know about synthetic data generation. Discover the techniques and tools that make synthetic data essential for AI and machine learning with practical Python code examples to help you get started!

Discover the key differences, applications, and implementation of normalization and standardization in data preprocessing for machine learning.