Interesting article posted recently by Ferris Jumah, Data and Products guy at LinkedIn. The author concludes that data scientists typically do the following:
My belief is that this type of conclusion applies to only one type of data science: what I call low-level data science, that is tactical - as opposed to strategic - data science. The article is definitely worth reading and very interesting, it also features top skills ranked by importance: data mining, machine learning, R, Python, Data Analysis and so on. It reminds me vaguely about another article on highest paying programming skills. I also produced a similar list a while back, and it looks pretty similar to Ferris' list. However, I believe that there is another dimension to data science, which is the decisional aspects. It is not captured by LinkedIn because data scientists rarely list these business skills in their LinkedIn profile.
For instance, most of the skills that I use, as a data scientist, are different: domain expertise, business acumen, data intuition, use of vendor dashboards, finding the right data, making conclusions and applying results to my decision process to run a business. The systems that I develop (computational marketing, growth hacking) rely on a few principles: data-driven rather than model-driven, simplicity, robustness, scalability, efficiency, fast implementation. Some processes do not involve coding, but instead making tools communicate together, for instance
My article on 10 types of data scientists brings a different, fresh perspective to this.
Finally, everything you can learn from a textbook (R, Python, Machine Learning, and so on) is at risk of being outsourced or automated. That's what vendors are trying to do, and myself as well with my data science research lab. So if your skill-set consists only of stuff available in textbooks, your career prospects don't look too good.
The picture is from the original article.