Data Science

You are a data scientist in the Finance team. A variable is a labelled box. We put 500 in a box called amount.

You are a data scientist in the Finance team. A list holds many values inside square brackets [ ].

You are a data scientist in the Finance team. len() counts the items in a list.

You are a data scientist. Raw income and debt columns barely predict default. Credit risk analysts always reason in ratios, not absolutes.

You are a data scientist in the Finance team. sum() adds every number in a list.

You are a data scientist in the Finance team. pandas is a tool that opens tables. We call it pd for short.

You are a data scientist in the Finance team, working with payments data. describe() gives count, mean, min, max and more at once.

You are a data scientist in the Finance team, working with payments data. A scatter plot shows whether two numbers move together.

You are a data scientist in the Finance team, working with payments data. Models need numbers, so get_dummies makes a 0/1 column per word.

You are a data scientist in the Finance team, working with payments data. kde=True draws a smooth curve over the histogram.

You are a data scientist in the Finance team, working with payments data. z-score = (value − mean) ÷ spread, so columns are comparable.

You are a data scientist in the Finance team. df['region'] picks a single column out of the table.

You are a data scientist in the Finance team. .mean() works out the average of a column.

You are a data scientist in the Finance team. .max() finds the biggest number in a column.

You are a data scientist in the Finance team. A filter keeps rows that pass a yes/no test.

You are a data scientist in the Finance team. sort_values lines rows up. ascending=False puts the biggest on top.

You are a data scientist in the Finance team, working with payments data. LinearRegression learns the best straight line through points.

You are a data scientist in the Finance team, working with payments data. corr() measures how two columns move together (−1 to 1).

You are a data scientist in the Finance team, working with payments data. A decision tree asks yes/no questions to pick a label.

You are a data scientist in the Finance team, working with payments data. KMeans groups rows that are close together — no labels needed.

You are a data scientist in the Finance team, working with payments data. Plotting guesses against real values shows how good a model is.