Data Science Tutorials

Learn how to perform linear regression in Python using NumPy, statsmodels, and scikit-learn.

Explore heavy-tailed distributions where traditional statistical methods don't apply. Discover how the Cauchy distribution effectively models phenomena where extreme events occur more frequently than expected.

Discover the intricacies of the negative binomial distribution and its applications. Learn how to model count data effectively. Explore practical examples and visual aids to enhance your understanding.

Examine the mathematical principles behind random walks and explore their forms, from one-dimensional paths to biased and Gaussian models. Use Python to discover how these stochastic processes inform real-world phenomena in biology, physics, and finance.

Explore the differences between mean and median, learn their applications in data analysis, and know how to choose the right measure for different scenarios.

Discover the fundamentals of the exponential distribution and its applications in real-world scenarios. Learn how to calculate probabilities and understand its significance in various fields. Explore practical examples and visualizations.

Discover the importance of multilevel modeling in analyzing hierarchical data structures. Learn how to account for variability within and between groups using fixed and random effects. Apply these concepts to uncover deeper insights in fields like education, healthcare, and social sciences.

Discover how the Fibonacci sequence works. Explore its mathematical properties and real-world applications.

Learn about Artificial Immune Systems (AIS) and how they can be implemented in Python for tasks like anomaly detection and optimization.

Discover how propensity scores help minimize confounding in observational research, with detailed applications in real-world studies.

Discover what cardinality means and how it impacts data processing, analysis, and machine learning. Learn about practical strategies and techniques for managing high cardinality, from data preprocessing to advanced optimization tools.

Explore the fundamentals of eigendecomposition and its applications in data science and machine learning.