Without a doubt, documentation is an essential part of working in Data Science projects. If your work involves reading up latest research or coming up

Writing Math Equations in Jupyter Notebook: A Naive Introduction

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2021-06-14 12:30:04

Without a doubt, documentation is an essential part of working in Data Science projects. If your work involves reading up latest research or coming up with new algorithms to solve problems, then it is especially important and useful.

However, the latter kind of work often involves writing Math equations in digital form. Except for people familiar with LaTeX, this is often an unfamiliar territory.

In this post I’ll show you, with examples, how to write equations in Jupyter notebook’s markdown. I have selected these equations such that they cover the most recurring types of symbols and notations which you might encounter (at least I do). I also give links to useful resources I refer to. And then, there are many bonuses along the way :)

BONUS 2: A model is linear, if the relationship between dependent variable (Y) and independent variables (X) is linear in parameters (betas). The hat on betas just mean that they are values estimated from data (and we hope that they are close to true values).

The weird looking thing against each name is the markdown syntax to write hat, subscript, sum, limits and beta in the equation of linear model. Note that rest of the things are just alphabets and numbers viz. Y, 0, j, 1, p, and X, which need no special syntax apart from, at times, being enclosed inside {}.

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