Coding your road to Data Science: mastering key development skills
Data science is undoubtedly one of the most fascinating fields today. Following significant breakthroughs in machine learning about a decade ago, data science has surged in popularity within the tech community. Each year, we witness increasingly powerful tools that once seemed unimaginable. Innovations such as 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.
However, with the abundance of tools and the ongoing hype surrounding AI, it can be overwhelming — especially for beginners — to determine which skills to prioritize when aiming for a career in data science. Moreover, this field is highly demanding, requiring substantial dedication and perseverance.
As we understood from part 1, the main data science areas can be divided into three large categories: maths, software engineering and machine learning. In this article, we will focus on software engineering skills that learners need to master to become a data scientist.