Categories of Data Scientists
When a professional begins his or her work in the field of data science, there happen to be a varied amount of roles that they need to follow and to succeed in. These various roles are most often overlapping with data science and various other disciplines like machine learning, deep learning, AI, Statistics, IoT, operations research, applied mathematics and so on. It is a widely accepted fact that data science is a broad discipline and there are a number of functions a data scientist would come across as a part of a data science setting. Read on to know the intricate difference between such very functions of a data scientist. First of all, there is no one type of data scientist, there are actually as many as nine types and could most definitely be more. There is the classic data scientist, then there is the data architect, the data engineer, the statistician, the analyst and then there are those who although work with data but don’t really have the job specific titles, like the founders of the companies and top professionals and so on. Moving ahead lets discuss the various roles of a data scientist in particular two disciplines, machine learning and deep learning. While the former refers to a set of algorithms that are supposed to train on a data, in order to predict or take actions which will happen in accordance with optimization of systems. For some the concept of deep learning usually refers to a set of neural networks. This basically
means that any function which takes place within this discipline would be a kind of a machine learning technique but on a much deeper level and with a much deeper layer as well. While machine learning could be referred to as the whole, deep learning is most often referred to as a subset of the same. The latter has seemingly become quite popular as opposed to the former as it involves a particular mathematical model that is similar to a set of blocks, where a little adjustment will give out quick results. When it comes to the different roles that are supposed to be performed in machine learning and data science, the former usually refers to a kind of a learning. Here in many types of processes are involved for instance regression, naĂŻve Bayes and clustering and so on. On the other hand when we talk about the roles that are supposed to be followed in the field of data science, then here it is usually a statistical technique that is put to use and the aim is to detect clusters and cluster structures without any sort of prior knowledge of an algorithm set to help the classification process. Some techniques used here are hybrid, whereas some are semi supervised and so on. While these many roles and functions of a professional data scientist may be different, but all of them have to undergo the same educational procedure. Where some choose to do individualistic study of data science, whereas others choose the route of professional training institutes like Imarticus Learning.