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The difference between data science and software engineering is often misunderstood, sometimes by engineers. 

A few years back, many engineers transitioned to data science when the AI craze was at an all-time high. Excited by the potential machine learning (ML), they eagerly loaded unexplored CSVs onto off-the-shelve ML models. After a series of experiments, they were bored, disillusioned, and underwhelmed. 

What is the objective of data science?

Data science requires a curiosity for the subject matter, intimacy, a timid conversation, and a delicacy in each step. The results are unknown. There is nothing to win by rushing or arriving at a fast conclusion. It is consistent discovery, moving targets, a dance, or at least a hopeful embrace with data. 

The objective is a formula we humans could not have designed ourselves. 

What is the objective of software engineering?

In software engineering, on the other hand, we already have the formula, and the goal is to build a code structure that will execute such procedures. Engineering is result-driven and iterative to increase efficiency and simplicity. You might have heard “brute force first,” a strategy to have a solution that works devoid of elegance. 

The objective does not change. Like a puzzle with fixed pieces, the configuration is the art form.  

Who makes the best data scientists?

I’ve heard that physicists make great data scientists, which makes sense since there is an intrinsic curiosity in physics for how things work. But, curiosity is critical, rigor and discipline for exploration are essential, and the rest are tools anyone can learn. 

I’ve seen individuals from marketing and urban planning become exceptional data scientists. You never know. 

More will be revealed,


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Olivia Ramos is the CEO of Deepblocks, an AI-powered software that automates the site selection process for developers and investors. 
See how quickly you can find your next development site here (our record is 3 minutes).