27. Data Science

Statistics for data science, worked through in Python.

The Statistics and Machine Learning notebook covers the groundwork I use in data-science projects:

• Representing and visualising data: line plots, pie charts, histograms, box-and-whisker, bar, violin and QQ plots.

• Descriptive statistics: central tendency, dispersion, the inter-quartile range, histogram bins and entropy.

• Normalisation and outliers: z-scores, trimming, Euclidean distance and min-max scaling.

• Probability: sampling variability, probability mass functions, the law of large numbers, conditional probability and the central limit theorem.

The notebook is shown with its stored results.