This blog post will go over a few Python methods that have not been covered in the curriculum thus far. The following methods will be explored with example code provided: linspace, map, lambda, and enumerate methods.
I am currently writing this post with two days to go until my module 1 project, Linear Regression Analysis of a housing data set, is due. First, I wanted to start off with saying how much of a struggle, yet great experience this was. The project is the most fun I never want to have again. I have learned so much in just a week and a half. The project was a wake up call; in a good way. I honestly did not know how much of the information we were learning was just being regurgitated, or if I was fully grasping all of the concepts.
Recently we learned of the package seaborne. With this wonderful package came the method sns.displot() which returns two visualizations we have already learned in matplotlib.pyplot. This method combines together a histogram along with a KDE (Fit and plot a univariate or bivariate kernel density estimate). Along with a rugplot if you really want to dig deep into the data’s distrubutions. I found this to be a very interesting visual technique because it brough together so many methods wev’ve learned thus far, all in a single line of code.
Why Data Science? There are plenty of other tech heavy cash cow indsutries? Why not web development or a developer in a ancient field like Cobalt? Data Science is the future in many ways. Just imagine a world where AI robots are running around your house helping you do daily activites like laundry or dishes. Now lets take a step back, picture your daily life right now. You may be asking yourself why I just ran you through these exercises. Let me tell you. The biggest commonality between these two worlds is data. Data? Yes, data. Data was being collected constantly by the robots sweeping and mopping your floors. Whether it is the dimensions of the room being swept. Data is everywhere and it sure is going no where.