# What does it mean when someone says "Most of the data science algorithms are optimization problems"

I was trying to understand the Gradient Descent algorithm from this article and the author says

Most of the data science algorithms are optimization problems

I come from software engineering background trying to get a basic understanding of data science. Can someone help me explain what this means?

• Strictly speaking, the article is wrong. An algorithm is not a problem. There are no "data science algorithms" imho of what data science is. There are algorithms that are used in data science and some solve optimization problems. "Most" is quite a statement, as it is not clear what the author means by "data science", which has no generally accepted definition. After years, I am still working on a useful one. There are machine learning algorithms, which the author might mean. Let's rephrase: "many machine learning algorithms used in data science solve optimization problems". Jan 5 at 11:39

For example, in linear regresssion, given $$(x_i, y_i)$$, we try to find $$\hat{y}_i= \alpha_0 + \sum_{j=1}^d \alpha_j x_{i,j}$$ and we we want it to be close to $$y$$. We try to minimize the mean square error in our estimation.
That is our objective function is $$\min _\alpha\frac1n \sum_{i=1}^n (y_i-\alpha_o- \sum_{j=1}^d \alpha_j x_{i,j})^2.$$