Are you interested in data science and want to know which tools are used? In this article we will introduce you from the most used tools that every analyst should know to the most advanced ones.
Python is one of the most popular programming languages in data science due to its simplicity and versatility. It offers a wide range of libraries that facilitate tasks such as data manipulation, statistical analysis and visualization.
Key libraries:
“R” is another programming language widely used in data science, especially in academia and among statisticians.
Key libraries:
SQL (Structured Query Language) is essential for managing and querying relational databases. It is essential for extracting and manipulating large data sets.
Excel. Microsoft Excel remains a fundamental tool for data analysis, especially for basic manipulation and visualization tasks.
Git and GitHub. Git is a version control system, and GitHub is a platform for hosting and collaborating on development projects.
Power BI. is a business analytics suite from Microsoft that allows you to connect, visualize and share data.
Jupyter Notebooks. Jupyter Notebooks is an interactive environment that allows you to create and share documents containing live code, equations, visualizations and explanatory text.
Google Cloud Storage is a Google Cloud Platform object storage service that provides a scalable and durable infrastructure.
Microsoft Azure Blob Storage is a Microsoft Azure cloud object storage service designed to store large amounts of unstructured data.
IBM Cloud Object Storage is a cloud object storage service that provides scalable, durable storage.
Oracle Cloud Infrastructure (OCI) Object Storage. is an Oracle Cloud object storage service that provides scalable and secure storage for unstructured data.
At Ubiqum we offer three programs focused on three different student profiles. In each of them the student gets a solid foundation in Python programming and in the use of the libraries mentioned above.
Apache Hadoop. Hadoop is a framework for storing and processing large volumes of distributed data in server farms around the world.
Key components:
TensorFlow and Keras. TensorFlow and Keras are open source libraries for the development of machine learning and deep learning models and the application of advanced neural networks.
