At Springboard, we provide data scientist training with our mentored workshops, one in the Foundations of Data Science, and one in Data Science Intensive workshop as well. Data scientist training is something that we’ve gotten very good at providing with personalized mentoring from industry experts who work at companies such as Facebook, Uber, and LinkedIn. We’re constantly taking in new students who are undergoing data scientist training — and we’ve gotten very good at judging which data roles they should aim for, and what skills they should strengthen to get there.
That was the motivation for us to build a free guide to data science jobs with several examples of what it takes to make it as a data scientist from our former students.
We’ve prepared a quiz from that knowledge that will help you determine where you fit in the data spectrum. It will help you determine what data role you want to play, and it will give you a set of guidelines at the end and learning resources to help you reach your full potential. It’ll test your technical abilities broadly, and bring you to the right data science career path. Get started by clicking below!
We hope this quiz helps you seek the data scientist training you need. Here are a few useful links if you find yourself looking for more learning resources.
General: This primer on deep learning papers will help you immerse yourself into the complex field of artificial intelligence. This Github repository has a curated flow of some of the best resources to follow and consult on data science. This set of podcasts and newsletters will help you become part of different data communities.
Mathematics: This reddit thread contains a list of free online courses on the mathematics of data science, critical reading for anybody serious about data scientist training.
Statistics: This KhanAcademy resource on the basics of probability and inferential statistics makes for a great foundation as you explore the statistics you need to do through data analysis. This free course from Datacamp helps introduce programmers to statistical concepts through the R programming language.