It needs businesses to redefine their talent-related strategies and contemplate reskilling and upskilling programs for a more harmonic implementation. Anyone that complies with that requirement will surely see how AI can boost in-house talent. when I started in systems engineering, data science was a fringe term used by a small pool of academics. I remember my professors referencing it as a sort of fad that would eventually go away. And wow, that opinion didn’t age well. It’s almost been two decades since then, and data science as we know it has become one of, if not the, highest growing field in computer science.
The statistics from CALU are impressive
o say the least, with a job growth rate job function email list of over 650% since 2014. Delve in data science long enough and you’ll feel like it’s a divided house. On one side, you’ll see scientists doing data analysis with R since the last century. On the other, you have those who defend Python as the one true savior of our people.
Yes, there is another path
Relying on statistical software like by using agile methodologies and project management platforms MatLab, Stata, or SPSS. But every data scientist will need to spread their wings sooner or later. Since we are usually working with unstructured data, we need to build custom solutions that stock software is simply not able to provide. And yes, the obvious answer is to learn both, but that’s only a real answer if you have enough time on your hands to play around with two different coding languages.
For the purpose of this discussion
Let’s assume that, due to time email data constraints, our hypothetical newbie data scientist can only pick one. The answer, as usual, isn’t simple. Let’s get one thing out of the way: both languages are perfectly suitable for data science, as both can cover the basics wonderfully: data manipulation, ad-hoc analysis, and exploration. So, instead of focusing on the basics, our time is better spent going over what makes them different.