# Notes on data science

This is a collection of small notes on data science, slowly sprawling
into “notes on math” as I see need. They are here
because I’ve found myself explaining some things to students over and
over again. These notes are more informal than what you may be
comfortable with, and should by no means replace actual textbooks on
probability, statistics, data mining, and machine learning.

I have found that students often lack the intuition that ties these
fields together. These notes try to explain some of the concepts
in a way that I hope will be useful.

## Basics

## Supervised Learning

Methods which use data to make predictions about new, unseen data.

## Unsupervised Learning

Methods which, given a dataset that has a complex representation,
create simpler versions of the dataset.

## Linear Algebra

## Other

## Data visualization

- B-Splines. Intro to B-Splines.
- Linear Function Spaces. Brief
intro to using linear function spaces and reconstruction kernels to
represent continuous functions in a computer.
- ODE integration. Intro to numerical integration
of ordinary differential equations.