Stupid or Solid?

Reading both of these articles has been greatly insightful for me, and beneficial for my professional development. As a Data Science major, the curriculum goes to great lengths in exposing us to data gathering techniques and what to do with such large data-sets. There is not much focus on writing SOLID code and the many factors that are at play when trying to produce code that everyone can maintain. The Data Science curriculum is good at showing students how to manage the vast amount of data that is out there, and how to extract meaningful information and make predictions or decisions from it. We mostly use built in Python libraries or other machine learning tools such as scikit-learn or Pandas. My first two programming classes somewhat gave me good experiences in writing neat and readable code, however the main focus of my degree was data management, and finding insights from large data sets. These articles opened up a new world for me and showed me that writing good code is as much an art form as it is a technical skill. There is a great amount of effort that goes into writing SOLID code as the article describes. One must be aware and mindful of the people that will have to read and maintain the code down the road. It is not enough to just write functioning code, a good developer writes code that his team and possibly other people in the world will be able to comprehend and add to. One important concept that I was not aware of before reading these articles was that when writing classes, you want high cohesion and low coupling; meaning that you want to keep code together that is related in function but you also do not want to design your classes in such a way that many of them depend on another. The goal is to have your code work towards a common goal but different parts be independent if possible. I also learned that it is not ideal to prematurely optimize your code before it is even a working product. Working code is far better than optimized code that does not do what it is intended to. Both articles exposed me to concepts that I was not completely aware of. These software engineering principles will assist me in my career and make me a more versatile data scientist. It is always a good idea to continue your education and broaden your horizons especially in the fast growing and constantly changing field of technology and software.