Math for Data Science Beginners: Functions

Mathematics remains a major hindrance for beginners trying to get into data science

Benjamin Obi Tayo Ph.D.


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Most beginners interested in getting into the field of data science are always concerned about the math requirements. Data science is a very quantitative field that requires advanced mathematics. But to get started, you only need to master a few math topics. In this series of articles, we will dive deep and discuss the essential math topics that must be reviewed before embarking on a data science journey. We will begin with functions. The topics to be covered in the series are:

For other published articles in the series, please click on the links above.


Most of basic data science is focused on finding the relationship between features (predictor variables) and a target variable (outcome). The predictor variables are also referred to as the independent variables, while the target variable is the dependent variable.

The importance of functions is that they can be used for predictive purposes. If one can find the function that describes the relationship between X and y, that is y = f (X), then for any new value of X, one could then predict the corresponding value for y.

For simplicity, we will assume that the target variable takes on continuous values, that is, we will focus on a regression problem. The same principles discussed in this article will apply for classification problems in which the target variable takes only discrete variables, for example 0 or 1.

1. Linear Functions

A. One predictor variable

Let’s assume we have a one-dimensional dataset containing a single feature (X) and an outcome (y), and let’s assume there are N observations in the dataset:

The goal is then to find the relationship between X and y. The first thing to do would be to generate a scatter plot that can inform us about the type of…



Benjamin Obi Tayo Ph.D.

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