Who is a Data Scientist and what does a Data Scientist do?

data scientist

For several years now, data specialists have been urgently sought after by companies all over the world. Initially, the demand was mainly from large financial institutions, IT and consulting companies, but today’s data expert, often titled a data scientist, is needed in almost every business.

Who is a Data Scientist?

Until recently, the organizational structures of many organizations included positions for specialists dealing with data processing and its subsequent analysis. Over time, larger companies started to develop their Business Intelligence and Big Data teams, and the progressive specialization in the data area, the increasing volume of data and the development of modern technologies led to the emergence of completely new positions. One of them is a Data Scientist. What competences does a person in this position have to possess? The answer to this question can be found directly at the source, i.e. on the websites of companies that specialize in providing services in the field of Data Science – e.g. DS Stream.

Data Science is a department within a company that aims to use the available data in a way that generates additional benefits for the organization. This is one of the reasons why the Data Scientist is a true Renaissance man. A specialist who has extensive business knowledge, excellent knowledge of mathematics and statistics, and additionally is able to program and visualize data. There is no denying that finding a candidate who meets the above requirements is extremely difficult today. This is one of the reasons why companies compete so fiercely for the best specialists and their salaries do not stop growing.

What does a Data Scientist do?

The Data Science Department of the organization has an extremely important function, hence the scope of its responsibilities is extremely wide. What the specialists working in it do on a daily basis depends largely on the nature of the business. Generally speaking, it can be said that data specialists effectively optimize business processes, predict trends, improve products, analyze the potential impact of specific changes on financial results, costs or margins, predict customer attrition rate, etc.

Regardless of the specificity and industry in which the organization operates, the activities performed by data professionals generally follow a similar pattern. What specific steps does a data researcher take in his or her analysis process?

Identifies a business problem

Business knowledge in the area of Data Science is extremely important because it allows to quickly and effectively identify business problems, recognize new opportunities for development or optimization, and then formulate appropriate hypotheses and verify them. Looking at business from the perspective of the manager, i.e. from the perspective of profit and loss account, it is much easier to identify projects that may have business justification and contribute to the growth of the organization.

Acquires necessary data

Once the problem is identified, and the hypothesis formulated, the next step is to obtain the necessary data. This is an extremely crucial and unfortunately very time-consuming process. The reason for this is the fact that in many organizations the data is very disordered and collected in a careless way or not collected at all. Holding data with a sufficiently long history undoubtedly helps a lot, but it must also be remembered that it has to be analyzed, cleaned, aggregated at the desired level and combined with other data necessary for further analysis.

Implements an optimal solution

The process of data collection and cleansing is undoubtedly the most difficult and troublesome, but it is necessary to move to the key stage of the analysis. For this purpose, a Data Scientist builds a model, visualizes data, using appropriate tools, or uses the data in another way that allows to verify the hypothesis. At this stage, technical knowledge is essential – programming skills (e.g. in R, Python or Java) or knowledge of data visualization tools (e.g. Tableau or Power BI) are useful.

Draws conclusions

Even the most advanced model and a precisely made dashboard will not be worth anything if appropriate conclusions are not drawn from them. Data Scientist must support business decisions, therefore the ability to draw conclusions and communicate effectively with business is absolutely crucial.