From 35475f15c85a200d71d596e79bcf104c4941f223 Mon Sep 17 00:00:00 2001 From: Ash <121186850+AshKitturGitHub@users.noreply.github.com> Date: Tue, 27 Feb 2024 10:54:43 -0500 Subject: [PATCH] Week 7 Reflection --- week7.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/week7.md b/week7.md index e69de29..c18f5ae 100644 --- a/week7.md +++ b/week7.md @@ -0,0 +1,8 @@ +https://towardsdatascience.com/full-stack-visualizations-for-complex-solutions-for-data-scientists-5afc488f60d + +While my previous reflection focused on examining the principles of data visualization within the context of front-end development, this article delves into the +implications of a full-stack approach to software engineering. I think that it is interesting that the visualization capabilities for backend projects is much more +complex than front-end, allowing techniques such as GIS mapping, for example. They also show the vast library of visualization tools that Python is able to interface +with, such as Matplotlib, and compatibility with libraries in R, for example. Furthermore, the authors of the article show how using Flask within Python is primarily +utilized with an object-oriented programming approach. Overall, my general impression is that there are different implications on the output of data visualization +techniques depending on the scope of the project.