Apart from gathering and analysing the data, a data scientist has to present the result of the analysis in a form that is understandable and discernible. Visualizing the data with clear representations and in a neat and concise presentation is essential to drive home the conclusion of the analysis.
As much as the analysis is important, taking time to present the final takeaway points to the appropriate persons is also equally important. A data scientist may need to explain the result of the analysis with the proof to some non-technical bosses or clients and the best way to help them understand is with data visualizations.
As Python is one of the preferred languages for machine learning algorithms, there is a lot of analysis that needs to be represented and delivered in an understandable form. Python has made this easier by having its own libraries, especially for data visualization – matplotlib and seaborn.
These libraries offer 2D and 3D visualization graphics to create quality representations of data sets with complete options of customization like themes, colours, filters, palettes and various other tools to visualize functions and other complex mathematical computations in its simplest forms.
There are generally five types of data visualization techniques and all …