Tools Used By Data Scientists

Data Scientists are professionals who are responsible for analyzing and extracting valuable information from the data collected by companies...

Data Scientists are professionals who are responsible for analyzing and extracting valuable information from the data collected by companies. To carry out their work, they use a variety of tools and techniques, which allow them to manipulate and analyze large data sets. Some of the more popular tools, taught in training such as data science boot camps, used in data science include.

• SQL: SQL (Structured Query Language) is considered the holy grail of data science. You won't get very far in this field without knowing this important tool. SQL is a specific programming language used to manage data. It is designed to allow access, management, and retrieval of specific information from databases. Since most companies store their data in databases, mastering SQL is essential in the field of data science. There are several types of databases, such as MySQL, PostgreSQL, and Microsoft SQL Server.

• Apache Spark – Spark is a powerful analytics engine. It is one of the most popular and widely used data science tools. It was specially created to perform stream processing and batch processing of data.

• MATLAB: MATLAB is a powerful tool for AI and deep learning. It works by replicating "neural networks": computer systems that emulate the biological activity of the brain.

• BigML – BigML is a leading machine learning platform and one of the most widely used data science tools. It features a completely intractable and cloud-based graphical user interface (GUI) environment. BigML uses cloud computing to offer standardized software across different industries.

• SAS: SAS is a statistical software tool. In the field of data science, large organizations use SAS for data analysis.

• Excel: Most people have heard of Excel as it is a widely used tool in all business sectors. One of its advantages is that users can customize the functions and formulas according to the requirements of their task, something that is used in the business communication master's degree in order to optimize their work. Although Excel is not suitable for large data sets, it can manipulate and analyze them quite effectively when combined with SQL.

• Tableau – Tableau is distinguished by its geographic data visualization feature. With this tool, you can plot longitudes and latitudes on a map.

• Scikit-Learn: This is a Python-based library that you can use to implement machine learning algorithms. It is a convenient tool for data science and data analysis as it is simple and easy to implement.

• Apache Hadoop – Apache Hadoop works by dividing data sets into a cluster of thousands of computers. Data scientists use Hadoop for high-level computations and data processing.

A data science bootcamp is an intensive training program in data science. These programs typically last several weeks or months and are designed to help participants acquire the skills necessary to work as data scientists. Data science boot camps typically teach practical skills, such as using programming languages ​​such as Python and R, manipulating and analyzing large data sets, and implementing machine learning models. In addition, they also often offer advice on how to get a job in the field of data science. Overall, boot camps in Madrid are an effective way to acquire the skills needed to work in this growing field.