Glossary

Every concept in this sheet, plus the smaller terms it uses. Select a concept to open its card.

CSV, JSON and Parquet: CSV is plain-text rows with no types; JSON holds nested records, often one per line (JSON Lines); Parquet is a compressed, columnar binary format that stores the schema.

Derivatives and gradients: A derivative is the rate at which a function changes as its input changes.
Descriptive statistics: Numbers that summarise data: mean and median for the centre, variance and standard deviation for spread, percentiles for shape, and correlation for the linear relationship between two variables.

Gradient descent: Repeatedly moving the parameters a small step against the gradient: w = w - lr * grad.
groupby, merge and reshaping: groupby splits rows into groups and aggregates each one; merge joins tables on keys like SQL; pivot_table and melt switch between wide and long formats.

Hypothesis tests and p-values: A test asks how surprising the data would be if there were no real effect (the null hypothesis); the p-value is that probability.

Jupyter notebooks: Interactive documents that mix code cells, output, charts and notes, run by a kernel that keeps variables alive between cells.

Matplotlib charts: The base plotting library for Python; seaborn adds statistical charts on top.
Matrix multiplication and shapes: Multiplying an (n, k) matrix by a (k, m) matrix gives an (n, m) matrix, where each entry is the dot product of a row and a column.

NumPy arrays: N-dimensional arrays holding one data type, with a shape and a dtype, stored in contiguous memory and operated on by fast compiled code.

pandas DataFrames: A table of labelled columns, each a typed Series, with a row index.
Plain Python for data work: Before NumPy and pandas come lists, dicts, comprehensions, functions, f-strings and reading files with with open(...).
Probability and Bayes' theorem: Conditional probability P(A|B) is the chance of A given that B happened.
Probability distributions: Descriptions of how likely each value is: normal (the bell curve, set by mean and standard deviation), Bernoulli and binomial (yes or no outcomes), Poisson (counts per interval), uniform, and skewed ones such as log-normal.

The chain rule: The derivative of a function of a function is the product of their derivatives: if y = f(g(x)), then dy/dx = f'(g(x)) times g'(x).

Vectorisation and broadcasting: Vectorisation applies an operation to whole arrays at once instead of looping in Python.
Vectors, matrices and the dot product: A vector is an ordered list of numbers, such as one row of features; a matrix is a grid, such as a dataset with n rows and d columns.