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.

Learn how to organize raw data using cases, variables, data matrices, and frequency tables — the essential building blocks for data analysis and machine learning.

Master the difference between descriptive and inferential statistics with simple examples, Python code, and real-world ML cases. Ideal for beginners in data science.