You can sit down with your boss or client and walk them through the charts and graphs so they can conclude on their own why you are recommending a certain course Shadow Making of action. But you already knew that, right? Because you are a data scientist. Summary There are different definitions of data scientists. Some are very strict and require a working knowledge of various types of coding and database management. However, if you look at most definitions, they all converge on Shadow Making the three points we've discussed here today: An understanding of data, what it is, how it is collected and what it means. The ability to manipulate it to derive information from it. Connect data insights to real value and communicate it clearly to non-data people.
My final proof is that you already realized the title promised 10 reasons, and I've only covered nine of them. 10. Recognize the gaps This is a crucial skill for data scientists and search marketers because sometimes the data just isn't right. They were either corrupted by a hiccup in Shadow Making the system or partially deleted accidentally because the spreadsheet changed hands so many times. You need to have a critical eye to notice these things, otherwise you'll spend your whole afternoon spinning your wheels on data that will never reveal proper information because it's incorrect. This is something Shadow Making only a human can do - computers can only work with the data given to them. Unless they have instructions on how to check the validity of the dataset, they will just continue to parse the wrong information. So go get yourself a lab coat. Because you are… well, you know Shadow Making what you are. The opinions expressed in this article are those of the guest author and not necessarily of Search Engine Land. Staff authors are listed here.
If you can't clearly articulate the rationale for your conclusions, in a sense, your conclusions aren't valid. The main reason data science is the sexiest job of the 21st century is that those who do it aren't like the classic quants of yore who talk like computers and can't communicate Shadow Making with anyone. either in the office. The data scientist is not someone who sits on the other side of the building and is never involved in key decision making. These are the people leaders like to have in the boardroom who can contribute to the conversation and bring data to the table. I learned early in my career that almost Shadow Making anyone can learn to analyze data. But the ability to apply it in real-world situations and explain it to others in a way that will make sense to them is what separates a data scientist from an analyst. But you're good at it, right?