Science And Data: The New Era

Data Science

This is the age of technology and information. Every moment millions of data are created and this makes a dedicated study of data necessary.

The study of data to be applied to optimize processes in order to have an impact on the market.  It is a combination of information technology, statistics and marketing. Data is collected to predict future behavior and extract essential insights. It is the renewable energy of computer science.

Future And Data

Artificial Intelligence (AI) and Software Engineering are examples of tools used in data science. The future of humanity is entirely connected to data. The world will be more interconnected.

Every major company in the technology business uses data to extract insights and solve problems using one or more computational tools, so understanding minimal data leverages any business model.

Big Data

Huge amounts of data. On the Internet there is a huge flow of data from everywhere to everywhere and based on this data companies like Google, Facebook and Microsoft find patterns and use this knowledge to their advantage, and this process characterizes Business Intelligence.

Big Data has revolutionized computer science by uniting various fields. For example: With several images (representing data) of tumors a computer program can increase the probability of diagnosing tumors through images.

In this case there is union between data science and medicine. The same is true in many other areas of knowledge. Big Data connects us to the future.

The core of data science

In data science the process consists of considering a sample of data, collecting, analyzing, identifying patterns and solving a problem as a function of the patterns.

  • Collection

In the world of technology, collection is characterized by the major influence of Software Engineering, where engineers are responsible for creating methods of collecting information for certain specific purposes.

  • Analysis

In analysis the data is handled in several ways and this is where the data analyst works most: it organizes the data into categories, cleans, models and infers patterns in a statistical way. It is where data becomes meaningful.
Analysis methods in the context of Big Data vary according to the priorities in each project.

  • Application

At this stage the data is already converted into meaningful information. In companies, the marketing sector creates mechanisms to convert knowledge into profit, in research programs, such as Machine Learning, in this phase scientists use the information to improve a certain system or make decisions about certain problems.

5 Main Areas of Data Science

Below are five of the main areas of data science accompanied by brief definitions:

  • Data Engineering
Area responsible for converting mere data into information of useful value.
  • Data mining
Area responsible for analyzing data statistically in order to predict phenomena.
  • Business Intelligence

Area responsible for using data to make decisions that increase profits in companies.

  • Cognitive Computing Development

Area responsible for using data to automate systems. Artificial Intelligence and robotics are examples of this sector.

  • Data Visualization

Area responsible for presenting data in a clear, objective and appealing way when necessary.

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