# Why is linear regression in Data Science Important for University?

Asked by Sanat Rajpoot almost 2 years ago

J.P. Morgan || LSR’ 19

As much as I, you also know that linear regression is used to quantify the relationship between two or more variables. I mean it is also good for adjusting confoundation but it’s hardly the focus point. Since data science is very common with relationships between two or more variables, linear regression automatically becomes important here.

Linear regression is basically a powerful supervised machine learning algorithm. Abroad universities include this in their data science program to model linear relationships between two variables. If you don’t use this feature, it’ll be tough to even start in your data science course curriculum. Because it’s the very first thing in the course to explore data and build more complex models.

If talked technically, **when you try to find relationships between variables in machine learning and statistical modeling. And as mentioned, it is mostly used to predict the outcomes of events**. This dictates that to understand the elements in data science topics, you will need linear regression.

In simpler words the goal here is to predict values of dependent variables based on an independent variable. It can’t get any simpler than this. If you know enough about data science courses, I believe I don’t need to explain any further.

Let me explain it in simplest terms with an real life example -

*“In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict baseball runs by using linear regression.”*

This is a direct quote from Harvard University. Here you can see how **linear regression can be implemented to determine outcomes that are best to predict**. Otherwise it would just be a game of probability without logic, commonly known as luck. So for the universities, the course curriculums mean much more than it to you. Because you have options to choose for data science courses, some universities abroad need certifications from the educational ministry there. If there is no methodology to utilize a program what’s the benefit?

Having said that, I can understand if you need a different viewpoint on this matter. As data science is filled with various components, maybe one general opinion isn't enough. If you want some specific details linear regression, let me know in the comments.

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