Content from the Data Science Handbook, covering statistical methods, data analysis techniques, and practical data science workflows.
A comprehensive guide to the Central Limit Theorem covering convergence to normality, standard error, sample size requirements, and practical applications in statistical inference. Learn how CLT enables confidence intervals, hypothesis testing, and machine learning methods.
A comprehensive guide covering descriptive statistics fundamentals, including measures of central tendency (mean, median, mode), variability (variance, standard deviation, IQR), and distribution shape (skewness, kurtosis). Learn how to choose appropriate statistics for different data types and apply them effectively in data science.
A comprehensive guide to probability theory fundamentals, covering random variables, probability distributions, expected value and variance, independence and conditional probability, Law of Large Numbers, and Central Limit Theorem. Learn how to apply probabilistic reasoning to data science and machine learning applications.
Master data classification with this comprehensive guide covering quantitative vs. qualitative data, discrete vs. continuous data, and the data type hierarchy including nominal, ordinal, interval, and ratio scales. Learn how to choose appropriate analytical methods, avoid common pitfalls, and apply correct preprocessing techniques for data science and machine learning projects.
A comprehensive guide to the Sum of Squared Errors (SSE) metric in regression analysis. Learn the mathematical foundation, visualization techniques, practical applications, and limitations of SSE with Python examples and detailed explanations.
A comprehensive guide to standardization in machine learning, covering mathematical foundations, practical implementation, and Python examples. Learn how to properly standardize features for fair comparison across different scales and units.
A comprehensive guide to multiple linear regression, including mathematical foundations, intuitive explanations, worked examples, and Python implementation. Learn how to fit, interpret, and evaluate multiple linear regression models with real-world applications.
Learn about multicollinearity in regression analysis with this practical guide. VIF analysis, correlation matrices, coefficient stability testing, and approaches such as Ridge regression, Lasso, and PCR. Includes Python code examples, visualizations, and useful techniques for working with correlated predictors in machine learning models.
A comprehensive guide to Ordinary Least Squares (OLS) regression, including mathematical derivations, matrix formulations, step-by-step examples, and Python implementation. Learn the theory behind OLS, understand the normal equations, and implement OLS from scratch using NumPy and scikit-learn.
A complete hands-on guide to simple linear regression, including formulas, intuitive explanations, worked examples, and Python code. Learn how to fit, interpret, and evaluate a simple linear regression model from scratch.
A comprehensive guide to R-squared, the coefficient of determination. Learn what R-squared means, how to calculate it, interpret its value, and use it to evaluate regression models. Includes formulas, intuitive explanations, practical guidelines, and visualizations.
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