Computer science and data science may seem like similar fields to the casual observer, but they can be exceedingly different in practice, and making an educated decision about which you pursue as a career can have a huge impact on your academic progress and occupational happiness.
While computer science has its origins in the very first computers over two hundred years ago, the rapid advancement of technology has caused the field to expand exponentially, and there are any number of specializations in the field. What all these specializations have in common is that they cover how the hardware and software that makes computers do what we ask them to operate. This field can cover everything from manipulating the countless programming languages available to mastering the fundamentals of cyber security.
Data science is much younger, and that’s largely because it’s a synthesis of two existing disciplines. Data scientists are statisticians at heart, but they’re able to take advantage of the computational power of modern software and hardware to complete calculations on a scale that would be impossible by more conventional means. Data scientists must possess the ability to understand numbers in their most complex forms and be able to work their way around the platforms and programming languages that are used for data manipulation, but much of their job is about analyzing the patterns available and learning how to pick out the most important components.
While there’s a not insignificant amount of overlap in the skills necessary to work in the fields of computer and data science, they can attract significantly different personalities. In practice, computer science more accurately represents an engineering discipline. Most computer science jobs will task you with building new things, and the general process behind this comes down to communicating with the computer through a programming language.
Generally, computer scientists can function comfortably with less mathematical knowledge than data scientists, and their skills are more focused on achieving concrete objectives. By contrast, data scientists are more focused on the traditional scientific process of proving a hypothesis and discovering things that are already true. While they require a fundamental understanding of the software and languages they’ll use during their work, their needs are more focused on analyzing and manipulating numbers, and the satisfaction they receive comes from discovery rather than creation.