The term data scientist has undergone a severe semantic makeover over the past few years. Experts have tried their best to convince people that the ‘scientist’ in ‘data scientist’ has to be earned. It is not a job that you land, but a life that chooses you. These theories, however true, have gone under the bus and we have found a renewed definition of the role of a data scientist. A data scientist is a person who is tasked with helping an organization make sense of data. In the course of this article, we will look at some activities that occupy a data scientist’s average workday.
Despite all the developments in the field of data science, professionals still spend 45% of their time in loading and cleaning data. This part of a data scientist’s job is often compared to plain drudgery. So, why do they need to spend so much time on this? The primary factor that contributes towards elongating the data cleaning process is the diversity of the sources and formats of data that a data science professional has to work with.
If the data format is incompatible to the tool used to process it, it has to be prepared before processing. That aside, maintaining compliance with regulations and meeting the IT security requirements also necessitate data cleaning.
Perhaps, data scientists will not have to spend so much time on data preparation after a certain point in the future, but right now, that is a huge part of their workday.
A study conducted by Anaconda shows that data scientists spend around 11% of their time in model selection. What does this even mean? The goal of data science is to answer certain questions by looking into an ocean of information. In order to do that the data has to be sent through a certain statistical model. You can think of the model as a set of questions that the data is supposed to answer. The data scientist has to choose a model based on the problem he or she is trying to solve.
Training and scoring
Just like a human worker has to receive training in order to perform a special task so does a machine or an algorithm. The data scientist trains the algorithms by exposing it to training data. Training data is a data set that is specifically labeled to build sensitivity among the algorithms for similar data.
When the trained algorithms are exposed to new, test data, they come up with predictive values or scores. This procedure is called scoring.
Deployment of a data science model refers to its use to process new data from the world (customer) to perform analytics. This part involves customers or audiences. This takes around 11% of the data scientist’s time. And this is pretty much the moment of truth for a data science model. So, as you can see, data science has its boring parts too. But as a data science professional or a machine learning engineer, you will be excited by the possibilities the discipline holds. When you find yourself a data science course in Bangalore or a machine learning course in Gurgaon, remember, you will be spending half of your time on data management, so do not skip those parts.