Data science and computer science often go hand-in-hand, but what makes them different? What do they have in common? After holding several different roles in data science departments at various companies, I have discovered some general qualities common to the data science process, along with how computer science is incorporated into that process as well. Anyone who currently works in or who is interested in entering either field should note the differences between these two disciplines, as well as when one requires concepts and principles from the other. Usually, a data scientist will benefit from learning computer science first and then specializing in machine learning algorithms.

The goal of this article is to give a general overview of the top Data Science tools and languages. I have either used these the most frequently out of others or have worked with others who have commonly used them as well. There are a few unique tools that are quite beneficial that not everyone may not know about additionally that I will be discussing later on. I will give some use cases for my examples so you can see why these tools and languages are so valuable. I have previously written about some of these tools and languages, so in this article, I will add more information as well as new information.