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6 Essential Tools That Make A Data Scientist Life Easier
The tremendous hard work by the Data Science community has helped in making the open source tools and libraries become more trusted, reliable and scalable. As a result, these open source tools are now playing a crucial role in the Python Data Science environment by providing robust, cost effective and accessible technologies that help in supporting the data-driven initiatives.
Here below is the list of tools that are extensively used in Data Science & make the Data Scientist life easier.
Anaconda for Package Management-
Anaconda is a software distribution which helps in the application of package management for machine learning. Anaconda has been built extensively with pre-compiled packages for classic data mining. These pre-compiled packages simply eliminates the need pip install each library. It also comprises of a special package called Conda which is used for creating your virtual environment and for installing additional libraries.
PyCharm for your Python IDE-
PyCharm is a widely popular IDE for Python programming. It comprises of optimised code assistant and code intention tools. In addition to general features that are showcased by other IDE’s like code completion, type hints and syntax highlighting it also comprises of special features like debugger, suggesting fixes and improvements to your code aligned with PEP8 guidelines and numerous built in testing frameworks.
It also comprises a useful local history feature that enables the function to auto save your local changes even when they are not committed.
Keras For Your Deep Learning Projects-
If you are getting to work in the domain of Deep Learning then Keras is a great place to start. With Keras experimenting with intuitive interface for developing models will seem quite easier. Also prototype ideas can be implemented quickly.
Cloud Platforms for flexibility and scalability-
Cloud platforms such as AWS provide a more scalable development environment. Different instant types in AWS will be a lot helpful in supporting compute-intensive applications and provide faster performance.
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