In previous posts, I’ve talked about the importance of building on fundamental computer science concepts for data scientists and bootcamp grads (and anyone working or wanting to work in tech). Those concepts form the ecosystem that so much of data science and web development exist on top of, and while you rarely “need” to know those fundamentals to achieve project goals, it has been said that knowing those fundamentals and being able to leverage them to write better code is the difference between average and exceptional coding.
“There are 2 types of software engineer: those who understand computer science well enough to do challenging, innovative work, and those who just get by because they’re familiar with a few high level tools.
Both call themselves software engineers, and both tend to earn similar salaries in their early careers. But Type 1 engineers progress toward more fulfilling and well-remunerated work over time, whether that’s valuable commercial work or breakthrough open-source projects, technical leadership or high-quality individual contributions.”
––Oz Nova and Myles Byrne, Teach Yourself Computer Science
Web development, while a higher level process than those core fundamental concepts, are important for data scientists to grasp too, especially “full stack” data scientists, the ones who do their own web scraping, which, even with a library like Beautiful Soup, is so much digging through HTML.