Data science allows you to prepare data, build models, visualize results and ultimately make better decisions.
There is much talk about the abundance of data and the analysis that could be done with it. Just turn on the news and see the number of numbers that are presented as a journalistic summary. For example, the percentage of people with covid-19 active, recovered, number of tests applied, traceability, etc.
In television channels and online press, this also happens in supermarkets, the financial world, education, everywhere in reality. The reason? Data analysis allows you to make better decisions.
The data exist in abundance, and it is projected that by the year 2025, the amount will be ten times what we currently have in the world. That is the importance of having tools to process them to give timely responses and make better decisions.
What is data science?
There are many definitions to show the different disciplines that make up data science. The basis is to consider scientific methods, processes, and algorithms to extract value from various information sources.
Common tasks include preparing data, building models, and visualizing results.
The following is the simplified way to understand step by step what is done with the data.
An interesting aspect to highlight is the multidisciplinarity, which allows data analysts to work in teams since it is important to understand the nature and origin of the information.
Why use data science
Not all that glitters is gold. Data must be processed before analysis to obtain results with a higher degree of confidence. Certainly, a large part of the projects that involve information analysis often fails because they do not understand the nature of the data. The computerlecture.com case shows that data processing is extremely important.
For example, if we analyze the data of a person who does not have a credit card, this does not imply that a piece of information is missing because a person is not required to have one or a landline or mobile phone number.
The data must be stored, and only then is it ready to be used by analytical methods.
Here you will find various opportunities: techniques to analyze texts, processes, images, genomes, social networks, finances, etc.
Who uses it
Applying data science allows generating better analysis and also predictive models.
Where is data science applied? It is widely used by organizations that need to understand more about their business to take corrective measures, improve processes or evaluate new expansion opportunities.
Among the analyses stand out the segmentation of customers in commercial stores and banking, social network analysis to personalize ads, fraud detection, observation of constellations, classification of patients in health areas, and many other examples and areas.
Data Science Tools
There are many tools for data processing. However, the most used today are open-source ones. Among the most used is Apache Hadoop, which allows solving problems of high computational complexity due to its characteristics.
There are paid tools like RapidMiner that provide a simple way to apply data mining models. In addition, there is always the possibility of using a programming language. The trend is largely set in this area by Python and R, with a large community providing libraries with algorithms already implemented.
What's coming
Why study data science? What and what does a data scientist do? The future in this area still has a long projection because the data will continue to increase. Therefore, it is necessary to train professionals with analytical skills.
As the positive evidence from data analysis increases, organizations will want to integrate data analysts into their workgroups. In this sense, there will be increasing pressure from companies to incorporate new technologies and analytical methods to better respond to market demands.
