Better still, it becomes a common platform to use different Python libraries. It also facilitates co-creation of knowledge and sharing of experience. First, it does not require local data storage facilities as all processing can be done at the CPU and GPU provided by Colab and the files can be saved at the Google Drive / Cloud or Github. That’s is the reason why I start to use Google Colab to try doing data analytics tasks. I just have a case that involves using ArcGIS to geocode a big dataset, then using Excel’s Power Query and pivot table to tabulate and extract the required data, and then importing into EViews to carry out econometric analysis. Sometimes we have to use several different software to accomplish just one goal. It can cause a lot of communication and learning problems when one has to learn and link so many different software and apps. Figure 2 Examples of different software and apps for different data analytics processes
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