Statistics & Mathematics for Data Science in Python

Load data from URL pages with pandas read_html, then summarize categorical data using counts to reveal trends. Concatenate data frames to build unified datasets and apply value_counts for quick exploration.

Master measures of center and essential statistics in Python using pandas. Import data from a URL, convert types to numeric, and compute mean, median, std, var, and describe.

Identify key features of the uniform distribution, where every outcome is equally likely, explore shape, generate data that fits it, and visualize it with histograms and kernel density estimate plots.

Explore the normal distribution, a bell-shaped model for continuous data, and learn to use mean and standard deviation to describe, simulate, and visualize data in exploratory data analysis.

Explore continuous distributions with real data using fitter to compare fits and identify the best distribution by least-squares error. Understand how non normal data and outliers affect distribution choice.

Explore box plots for quantitative data, identify outliers, and compare distributions with Titanic age and iris species examples to reveal interpretation and limitations.

Explore scatterplots to compare two quantitative variables, interpret patterns and correlations, and use regression lines with R-squared and confidence intervals, plus customization options.

Explore confidence intervals and hypothesis testing, interpret statistical significance, and apply normal approximation techniques and visualization techniques; compare groups with anova, chi-squared, and bootstrap for non-normal distributions.

Explore how to compute and interpret confidence intervals and hypothesis tests in data science with Python, including means vs proportions, margin of error, and p-value for statistical significance.

ANOVA compares means across more than two groups by analyzing variances and uses the statsmodels and the Pinguin library to assess normality, equality of variances, independence, and perform pairwise comparisons.

Learn how regression and predictions drive data science, including cleaning and preparing data, building predictive models, and hyper tuning to compare models and optimize predictions from multiple quantitative variables.

Explore polynomial regression, its pros and cons, implement it with polynomial features of degrees 3 and 5 on a data set, and compare performance via r-squared and model summary.

Explore ridge regression, its regularization strength alpha, and built-in cross-validation to balance bias and variance when predictors exceed observations; compare to linear regression and interpret outputs.

Explore elastic net regression, compare its bias-variance tradeoffs with ridge and lasso, interpret outputs, and assess predictive performance for diamond prices using cross-validated alpha and L1 ratios.

Apply grid search to tune a random forest regressor and optimize model performance using cross-validation, examining estimators, max depth, and min samples for split to maximize r-squared.

Learn how logistic regression classifies data by predicting probabilities between zero and one, with multinomial extensions for multiple groups, and evaluate performance using confusion matrices and classification reports.

Learn how the random forest classifier classifies data, trains an RF model, and compares its accuracy against logistic, SVM, and Naive Bayes using a confusion matrix.

Compare logistic regression, k-nearest neighbors, support vector classifier (svc), and random forest using a model comparison tool with visualizations of accuracy across cross-validation.

Load text data into a Google Colab notebook, analyze textual features, visualize text frequency, and apply naive bayes with cosine similarity across four texts.