Python & SQL: universal tools for all data matters?
Posted: Mon Jan 27, 2025 4:50 am
Python has long been an integral part of the toolbox of data scientists/data engineers and is now increasingly of interest to other user groups who have already made great demands on drag-and-drop or no-code tools in the past and have also ventured into SQL and VBA programming in the Microsoft Office world. So is it worthwhile for companies to promote the use of Python for data analysis in addition to sound Excel and SQL knowledge in order to increase the data competence of their employees, e.g. among so-called power users?
The video for this article is available here .
Data professionals are a algeria telegram screening precious commodity and in many companies, the daily demands exceed the available resources and the capacities of the data teams. At the same time, data analysts are reaching the limits of what business intelligence tools can do for them and are looking for ways to perform more advanced analyses. The use of Python could be the key to success here, because in recent years the Python community has developed new frameworks and packages that make the language for advanced analyses, machine learning and application development accessible to non-professional developers. Popular examples are NumPy , an open source Python library for numerical data [1] , Prophet , an open source prediction software released by Facebook's core data science team [2] and H3 , a project started at Uber to process geospatial data [3] .
The video for this article is available here .
Data professionals are a algeria telegram screening precious commodity and in many companies, the daily demands exceed the available resources and the capacities of the data teams. At the same time, data analysts are reaching the limits of what business intelligence tools can do for them and are looking for ways to perform more advanced analyses. The use of Python could be the key to success here, because in recent years the Python community has developed new frameworks and packages that make the language for advanced analyses, machine learning and application development accessible to non-professional developers. Popular examples are NumPy , an open source Python library for numerical data [1] , Prophet , an open source prediction software released by Facebook's core data science team [2] and H3 , a project started at Uber to process geospatial data [3] .