We need the best tools to leverage techniques that can turn data into insights by way of reporting or visualization. There are many prominent programming languages such as C, C++, Java and Javascript for making meaning out of data. But popular languages like R and Python offer good working environment to perform all the requied analysis. So here we look into the basics of python required for data science.
Why python is preffered over other data science tools is because; -Easy to understand & simple to learn -Open source & community -Consists of vast libraries -Graphics and visualization
Use Jupyter Notebook(ANACONDA) to perform all the basic python operations and data analysis with different ML algorithms.
Download the Anaconda Distribution from the below link (Python 3.7) https://www.anaconda.com/distribution/
or
Use Google Colab, Colaboratory is a free Jupyter notebook environment provided by Google where you can use free GPUs and TPUs for better processing.
To start working with Colab you first need to log in to your google account, then go to this link below https://colab.research.google.com.