Wiki, Math, and What’s Going On?
The wiki FAQ* lists much more math than typical bootcamps provide. Why is that? Is it as a result of bootcamps are for entry degree positions and to advance, it’s important to study the mathematics by yourself? Or are you able to choose up the mathematics at work?
Additionally, the wiki lists just a few threads, however they appear to be at the least 5 years previous. Are they nonetheless related?
Aspect query: how is math used at work anyway? My solely publicity to information science was by means of Weka, so the mathematics was hidden from me. Do information scientists tweak the algorithms or do they write new ones from scratch?
*[https://www.reddit.com/r/datascience/wiki/frequently-asked-questions/](https://www.reddit.com/r/datascience/wiki/frequently-asked-questions/)
**differential, integral, and multivariable calculus; linear algebra; chance; statistics
Comments ( 2 )
>Is it because bootcamps are for entry level positions and to advance, you have to learn the math on your own? Or can you pick up the math at work?
Bootcamp’s goal is to help you be up to speed with the latest tools and software to get you to do a job quickly with little onboarding. At best, the bootcamp will help you be a Data Analyst. Despite the title “Data Science”, there’s no way a bootcamp can cram that much math into few weeks. Math is useful in Machine Learning research centric roles.
To answer your side question: it really depends, you barely need any math for dashboarding, but you do need high-level linear algebra and calculus for ML research. Both can be labelled “data science”.
Having a really good grasp of high-school level algebra certainly helps in even the most basic roles and you will have a hard time understanding what most algorithms do without basic calculus and linear algebra.