Data science

glosario ciencia de datos

1. What’s data science?

Data science is an interdisciplinary field that focuses on the study and analysis of data to extract insights and obtain valuable information. It combines elements of statistics, mathematics, computer science, and domain-specific knowledge to analyze data sets and support decision making. At its core, this discipline seeks to discover patterns, trends, and relationships within data to gain meaningful insights.

2. What’s the function of data science?

Data science has its uses as a multifaceted discipline with the purpose of extracting meaningful knowledge from large data sets. It works as an analytical engine, employing advanced statistical techniques and algorithms to discover patterns, trends and relationships within the complexity of information. That is why it offers decision makers a deep understanding of the context, allowing them to foresee results and evaluate potential scenarios. In addition, it contributes to the optimization and automation of processes in various industries, improving efficiency, reducing costs and optimizing the use of resources.

In commercial environments, data science drives the personalization of experiences through the implementation of recommendation algorithms, adapting products and content to the individual preferences of users. Its ability to anticipate future events, based on the analysis of historical data, provides an invaluable tool in fields such as finance, public health, and inventory management. In addition, it allows us to discover hidden information, revealing correlations that are not obvious to the naked eye and opening new avenues of research. For example, a very simple application of data science would be to use PowerBI with data loaded on clients, users, products and/or services of a company, study the variables of that data and visualize them to compare them, in this case the Business intelligence or “Business Intelligence (BI)”

3. Data science fields

  • Predictive analysis.
  • Data engineering.
  • Data mining.
  • Data visualization.
  • Machine Learning.
  • Deep Learning.
  • Business Inteligence (BI).

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