The evolution of digital life has generated a new wealth for the business world: data.
The record of information that is produced at all times, whether by people or machines, which can be transformed into powerful business tools for companies.
But for these data to become sources of knowledge, there must be someone who studies them and correctly analyzes them, which is where the area of data science comes in.
What is data science?
Data science is a comprehensive and multidisciplinary field of study, comprising data, algorithms and technologies with the ability to extract valuable insights from both structured and unstructured data.
The extraction of this information aims to find answers to complex problems and situations, identify trends and generate insights through different types of analysis.
The information obtained from data science, in most cases, is used to make important decisions, such as the creation of new products or services, product updates, business changes and even what the future of an organization will be. .
What is Data Science for and how does it work?
The main function of data science is to transform data, structured or not, into knowledge for a company or project. This is because isolated or disorganized data and without analysis are just point information. Therefore, they need to go through a process, such as data science, to be a source of knowledge, which can serve as a basis for actions and improvements that provide companies with a competitive advantage.
What are the processes in Data Science?
The practice of data science has some fundamental steps to reach the answers that a project or a company needs.
Below we see the sequence of these steps.
Data collect.
The process begins with data collection. Data sources may vary depending on each company, but CRM systems, ERPs, mobile devices, IoT devices, cloud data, among other diverse sources, can be used.
Transport and data protection.
In this part of the process, the data collected from the various sources that the company has are moved to corporate networks, where they are centralized so that the responsible professionals can continue the process.
Data storage and processing.
After transport, the data needs to be stored in infrastructure that is capable of processing and validating it properly.
Data analysis and sharing of results.
After finishing the more “operational” stages, the main stage, data science, begins: data analysis.
This is one of the most important points in the data scientist's work, where algorithms, calculations, formulas and analysis models are applied to obtain the desired answers and insights.
Tools for data science.
The day-to-day of those who work with data science can involve different tools, according to the specificity of the professional's role and the type of company.
Among the most used programming languages in the area, we can mention R and Python, both open source, easy to use and accessible to different levels of professionals.
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