Must-have tools to master with working on data science projects. In this article, I will mainly focus on some of the most important tools to have and work with, tools which allow better and cleaner codingand faster way of collaboration.
When working on a data science project, many skills might be required from theoretical to
technical ones. In this article, I will mainly focus on some of the most
important tools to have and work with, tools which allow better and
faster way of
The summary is as follows:
The daily tasks of a data scientist are mainly about coding. For this reason, it is important to have a nice editor capable of encapsuling many other tools. VS code does that! It is a tool developed by Microsoft and considered to be one of the most used editors. It allows the run, edit and debug of the your code plus many other useful extensions such as:
Python: “A Visual Studio Code extension with rich support for the Python language, including features such as IntelliSense, linting, debugging, code navigation, code formatting, Jupyter notebook support, refactoring, variable explorer, test explorer, snippets, and more!”
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
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Need a data set to practice with? Data Science Dojo has created an archive of 32 data sets for you to use to practice and improve your skills as a data scientist.
A data scientist/analyst in the making needs to format and clean data before being able to perform any kind of exploratory data analysis.