In this article we examine: when a company may need a data scientist, whether the collected model has a right to error and how people go to data science in general.
Data scientists are people who work with data and try to find connections in it, which will help to explain some processes, reduce uncertainty in the business, as much as possible, and data science company help limit the entire process to optimize. They’re not replaceable in that regard.
A cursory googling of different definitions of data science can come across a curious picture.
It has three large areas of knowledge: mathematics, business and computer science, in Russian – computer science. If you connect these circles with each other, at their intersection a certain area of specialization is formed. For example, at the intersection of mathematics and business expertise is data analytics, at the intersection of computer science and business – software development, at the intersection of computer science and mathematics – pure machine learning. Where all three circles intersect, the field of data science emerges. A data scientist is a kind of fusion of a programmer, a mathematician and a specialist in a professional technical field.
Today, this specialty is already divided into narrower specializations, such as Data Engineer, Data Scientist, Data Analyst, etc. All of them, one way or another, at a basic level have similar competencies.
A company rarely looks for people for positions just to have them, the company always needs a profit. Just because it hires five data scientists and pays them a huge salary does not mean that the company’s profits will not increase, but they will most likely decrease. Businesses have to understand how mature they are for hiring data scientist and for what purpose they need such a specialist.
Certainly you can’t jump over certain levels where the company has an IT infrastructure and data culture. If you don’t have the data, chances are your data scientist will either fail or spend a very long time to at least get that data.
Businesses need to understand that the data scientist is not a magician. He does not possess magical properties sufficiently to look into a crystal ball and utter prophecies. If more or less accurate predictions are needed, they require a solid analytical base and lots and lots of data.
You can never have too much data. The more the better. But even if you already have the data, it can be inappropriate, wrong, or not detailed enough. It is difficult to consider everything at once, so you can start from the task and come to the data scientist just with an idea, and a minimum understanding of what you want to get in the end. He will already form the requirements for the data that he will need.