Glossary¶

Use this page to record terms and ideas that help you understand professional analytics projects.

This project covers characterizing machine learning problems: identifying the task type, selecting a target, and understanding what kind of model would be appropriate.

Pro-tip: Expand the VS Code Outline view (below the navigator on the right) to see this file organization at-a-glance.

Project Organization¶

source code¶

Source code are instructions that tell the computer what to do. In a Python project, source code lives in files ending with .py.

module¶

A module is one Python file that contains related code. A module may include constants, functions, imports, and a main() function. A project may have many modules working together.

package¶

A package is a folder of related Python modules. A package usually includes an __init__.py file. The init file allows code in that folder to be imported and reused across a project.

notebook¶

A notebook is an interactive file used to combine code, output, notes, and narrative. Notebooks are useful for exploration, experiments, and explaining analysis step by step.

Reuse and Workflow¶

reusable function¶

A reusable function is a named block of code that performs one clear task and can be called more than once. Good functions make projects easier to read, test, debug, and modify.

dependency¶

A dependency is an external package or tool that a project needs in order to run. Dependencies are listed in pyproject.toml and the environment can be easily recreated using uv.

workflow¶

A workflow is an ordered process for completing work. In a project, a workflow might include running code, checking results, making changes, testing again, and saving progress with Git.

Machine Learning Concepts¶

machine learning¶

Machine learning is a way of building programs that learn patterns from data rather than following hand-written rules. The program improves its predictions by seeing examples.

supervised learning¶

Supervised learning is a type of machine learning where the training data includes a target column - a known answer the model learns to predict. You choose the target; that choice defines the problem.

unsupervised learning¶

Unsupervised learning is a type of machine learning where there is no target column. The model explores structure in the data without being told what to look for. Clustering is a common unsupervised approach.

target variable¶

The target variable is the column a supervised model is trained to predict. Choosing the target is an analyst decision that shapes the entire modeling approach.

features¶

Features are the input columns used to make a prediction. The analyst decides which features to include, transform, or remove.

classification¶

Classification is a supervised ML task where the target is a category. The model learns to assign inputs to one of a fixed set of classes. Predicting whether an email is spam or not spam is a classification problem.

regression¶

Regression is a supervised ML task where the target is a continuous number. The model learns to predict a quantity rather than a category. Predicting a house price from its features is a regression problem.

clustering¶

Clustering is an unsupervised ML task where the model groups similar observations together without being given labels. The analyst interprets what each group means.

discrete variable¶

A discrete variable takes one of a limited set of distinct values. Category columns such as species, color, or rating are discrete. A discrete target usually means a classification problem.

continuous variable¶

A continuous variable can take any numeric value within a range. Columns such as temperature, price, or weight are continuous. A continuous target usually means a regression problem.