Bifalgorithm post

The big difference between apply data science and data science to me is that data science is about theories, methods, technologies, skills, but applied is about the whole process. How do you integrate that and use all of those theories, methods and skills responsibly, ethically and powerfully to build a solution for a real problem? And that's what our degree focuses on. We teach you methods, skills and theory, but we teach it in a way that allows you to leverage it to solve real world problems. And we find that this is what employers are looking for in new grads. To me, some of the neatest courses in our program come at the end and they're the applications courses, whether it's learning analytics, sports analytics, health analytics or other courses, you're able to express yourself in your passions and perhaps your background while building that full end-to-end data science pipeline and building solutions that are meaningful for the world. A lot of people coming into our degree have some concerns about their technical abilities. Is this degree right for me? Do I have the technical chops to make it? Well, we do require some basic technical knowledge, but we're here as teachers to guide you through that. And what we find is a lot of people coming in who don't have maybe deep technical experience already gain that technical experience and they go into leadership positions at their company. They run teams of data scientists or groups that involve data scientists, statisticians and computer scientists because they have that holistic view of the whole pipeline of the people, of the problems and of what the solution needs to do.

The big difference between apply data science and data science to me is that data science is about theories, methods, technologies, skills, but applied is abou…