Getting Ahead In My Data Science Career: Why Software Engineering Skills Are Important
In my ongoing quest to improve as a data scientist, I’ve realized something essential: I need to improve my software engineering skills. It’s not just about getting more tools with this understanding. It’s about getting better at engineering so I can make solutions that work well in production settings and can grow as needed. This means I have to study engineering more earnestly to make sure my products are solid and stable enough to withstand the harsh conditions of real-world uses.
I’ve come to believe that when we learn to code, we learn a way of thinking about solving problems in a rational and logical way. I want to learn more about software and data engineering because of this belief. Getting better at these things doesn’t mean we have to take on all of these roles by ourselves; it just means adding fundamental engineering skills to our data science toolbox that make it easier for us to manage the whole data science lifecycle, from finding data and analyzing it to putting machine learning models to use in real-world situations.
A data scientist knowledgeable about software and data engineering can confidently lead the whole data process. We make our work much better and more efficient by following software engineering concepts like writing clean, modular code…
