Learn how Pearson’s r measures the strength and direction of linear relationships between variables. Includes formula breakdown, Python code, and machine learning relevance.

Understand the difference between marginal and conditional proportions using examples and contingency tables. A must-know concept for analyzing categorical data and preparing ML datasets.

Learn how to analyze relationships between variables using correlation techniques like contingency tables and scatter plots, based on whether your data is categorical or quantitative.

Walk through a complete real-world statistics example using test scores: calculate mean, median, range, IQR, standard deviation, z-scores, and visualize it with dot and box plots.

Understand z-scores and standardization — how far a value is from the mean, and why it's a powerful tool in comparing values across distributions and machine learning tasks.

Learn how variance and standard deviation measure the spread of your data — with formulas, worked examples, and relevance to data science and machine learning.

Understand how data spreads using range, interquartile range (IQR), and box plots — with clear examples and why they matter in machine learning.

Learn how to calculate and choose between mean, median, and mode — key measures of central tendency in statistics and machine learning, with Python code and visual examples.

Learn how to create frequency tables in Python for both categorical and numerical data using Counter, pandas, and numpy — and visualize them with bar charts and histograms.

Learn which graph to use for categorical or quantitative data, and how bar charts, pie charts, and histograms help in understanding your data — especially in machine learning.