In recent times, data analysis has been the best way to make good decisions about the future. And with a future increasingly supported in the definitions of the BANI world, in which everything can change literally from one day to the other, the market has been in need of professionals with the skills of a data scientist.
Although data science is an independent field, it can be applied to several objectives and business models. Then the profession of data analyst is not only well requisited, it is also essential for the health of the market as a whole.
If you intend to work with data, a training focused on theories can be the first step. But, besides technical skills, we will also pay attention to the skills of a data scientist that are not exclusively theoretical. We have listed the most important ones below:
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Increasingly valuable, this ability has an impact in all professions. With it, a data scientist can elaborate questions to understand the results of each information raised about the business and what will influence the next actions taken.
In a nutshell, critical thinking leads to objective analyses of questions, hypotheses and results, allowing to understand which resources are essential to solve a problem and how it can be seen in more than one perspective.
Again, it is a necessary ability for any professional, no matter the level of hierarchy to which they belong. When dealing with data, we need to know how to explain each discovery to lay and technical audiences.
A good communication involves interpreting and organizing information so that it is understood by everyone who is part of the objective of the discovery. Therefore, data scientist needs to know how to explain information and how it contributes to business, communicating the ways to reach it and the possible solutions to be applied.
Speaking of a solution, proactivity will help to identify opportunities and the best ways to reach the objective. Therefore, it is impossible to be a data scientist without the natural will to solve problems.
The desire to investigate the cause of the problem should be equivalent to the knowledge of the right approach to solve it.
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Intellectual curiosity allows a professional to go beyond in their investigations and find more and more complete results. Not to mention that curiosity is a key factor for creativity, another ability related to the resolution of complex problems.
An skilled data scientist knows that only a response to the problem investigated may not be sufficient, so they use their intellectual curiosity to find, in the data, questions that have not been done yet. Enough, for this professional, should never be enough.
The more it is understood that specific needs of the business, the more it is known what problems the company needs to solve and the reasons why. Among the skills of a data scientist, commercial sense is used to transform information into results that benefit the organization.
Just dominating data may not be enough for this professional, since they also need to connect them to the reality of the company in which they work.
Bonus: it can be focused on technical skills
It’s all right to worry about what will be seen in your curriculum and professional history. Non-technical skills are fundamental to align theoretical knowledge when solving complex problems, so they do not dispense training or certificates and do not exclude their importance.
So, take note of the technical skills that you can work and that are increasingly high in the teams of data scientist.
Data preparation for effective analysis: it means to get the data ready for analysis. A data scientist needs to discover, transform and do data cleaning, relating these tasks with their workflows within the tool that they use.
Using tools for self-service analysis: the self-service analysis platforms help shape information and also share the results with people of the most varied technical levels. Data scientists, using their communication and critical thinking, should transform this channel into a source of parameters for all audiences.
Generation of efficient and easy to maintain codes: data science uses a large amount of languages and programming systems. This is why it is important to know and apply those that are related to the function, sector and challenges of the company.
Adequate application of concepts: as well as programming, mathematical and statistical concepts are part of the routine of a data scientist and need to be applied according to objectives and problems solidly identified.
Employment of intelligent technology: the use of machine learning and AIs (Artificial Intelligences) generates several benefits for companies and improves the work of the data scientist. And it is with them that the professional may be able to explain the ways that guided their predictions.
If you need to start developing the skills of a data scientist via the technical part, know that you can count on the MBA USP/Esalq in Data Science and Analytics. Enroll now!
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