Data science is undoubtedly one of the fascinating fields today. Following significant breakthroughs in machine learning a couple of decade ago, data science has surged in popularity inside the tech community. Annually, we witness increasingly powerful tools that when seemed unimaginable. Innovations akin to the Transformer architecture, ChatGPT, the Retrieval-Augmented Generation (RAG) framework, and state-of-the-art computer vision models — including GANs — have had a profound impact on our world.
Nevertheless, with the abundance of tools and the continued hype surrounding AI, it may be overwhelming — especially for beginners — to find out which skills to prioritize when aiming for a profession in data science. Furthermore, this field is very demanding, requiring substantial dedication and perseverance.
As we understood from part 1, the most important data science areas will be divided into three large categories: maths, software engineering and machine learning. In this text, we are going to deal with software engineering skills that learners have to master to turn out to be an information scientist.
