Talk presented at Code.Talks Hamburg & Global DevSlam Dubai
Recording: https://youtu.be/LbBmvOj4o7M?si=gLF3Gs2Lk_qJSGe_
It’s been 15 years since the term data scientist has become one of the most sought-after professions. Nevertheless, if you ask a lot of data scientists what their profession is you will get very different answers, which will mostly depend on the kinds of companies they work for.
So how does one learn Data Science when the definition of the field is open to interpretation? Or, when putting together all the job descriptions, in order to become a Data Scientist one would need to know all the theory, and new approaches and be able to use hundreds of tools.
In this talk, we will explore the fundamentals needed to be a data scientist, from the perspective of theory, tooling, and approaches. We will talk about some of the common misconceptions people starting to learn data science have. And about some of the reframing that I have seen successful learners have done on their path to data science.
And what role do large language models play in the future of learning data science?