Data Scientist and Machine Learning Engineer: what do they do? Let’s delve into these new professionals among the most in-demand in the market today.
Table of Contents
Data Science, what it is and what it is for
Data Science is the set of multiple scientific disciplines (from mathematics to statistics, from information science to computer science) thanks to which it is possible to extract useful resources and value from a large heterogeneous amount of data.
These data may come from different sources (data warehouse, sensors, web, etc…) and inherent to a given field or sector of intervention.
The ultimate goal then is to analytically solve complex problems. And to develop solutions and models that are also useful for business and commercial purposes.
Data Scientist: what is responsible for
The Data Scientist is the expert figure responsible for interpreting and extracting knowledge and valuable information from data during the analysis phase proper to the Data Science discipline.
The tasks of the Data Scientist range from analyzing the problem or a given market, cleaning and validating the collected data. Thus activating the famous algorithms that extract the data deemed relevant to interpreting and communicating it.
In practice, this figure possesses hard skills such as data mining, data analysis software, statistical methods and predictive models, data visualization, etc.. As well as soft skills (communication, problem solving, etc.). Which are necessary for the reworking of large data sets, from big data to enterprise databases.
These reprocessings are essential to intercept trends and process reports that managers and entrepreneurs will use. This, in order to direct business developments and investments (data-driven approach).
Machine Learning, what it is and what it is used for
Machine Learning (ML) is a branch of artificial intelligence in which algorithms have the aim of identifying recurring patterns in data.
The ML algorithms build a model based on sample data (training data). With the goal of making predictions or decisions without having been explicitly programmed to do so.
The applications of Machine Learning are very common. Today we are surrounded by intelligent applications and very often do not even realize it.
From recommendation systems (which guide us to buy products similar or related to those we have in our shopping cart or have bought), to automatic optical recognition (used for example by speed cameras on the streets) and facial recognition.
What does a Machine Learning Engineer do
The Machine Learning Engineer is the person in charge of designing and creating artificial intelligence algorithms capable of learning and making predictions governed by Machine Learning.
An ML engineer typically works within a larger team. Thus collaborating with data scientists, administrators, data analysts, data engineers, and data architects. Or, even IT, software development, sales, or Web development, depending on the size of the home organization.
Unlike the data scientist, who will analyze data and gather related insights, a machine learning engineer will work on writing code and on deploying machine learning products.
The skills necessary for this profession are mostly highly specialized and hard (mathematics, advanced statistics, software engineering and programming). But as is now increasingly the case, analytical skills, problem solving and teamwork always come in handy.
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