Custom Functions

This post will go over DataQuest’s Writing Custom Functions lesson. I decided to continue with the movie-theme in honor of the Academy Awards that took place on Sunday. I will continue to use the data set from my previous vectorized functions post. The data set comes from Kaggle and was composed by a Kaggle member with the user name of kikun1234. It explores award nominations of movies released between 2000-2018. I loaded the data set into a data frame called oscars_2018. So without further delay, let’s get started!

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Working With Data Frames

Basketball has been heavy on mind lately. One reason being the All the Smoke podcast that I listen to with Matt Barnes and Stephen Jackson every week. I’ve been listening to the podcast since day one. Another reason has been the recent passing of Kobe Bryant, his daughter Gianna and the seven other lives that were taken in that helicopter accident. When I think of Kobe, I think about my brother and the kids I went to school with. They would shout ‘KOBE’ anytime they threw something. 

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Intro to R Programming

R is a language I’ve been curious about for some time. I started learning a bit about Python when I kept reading and hearing about R. Python and R are the two programming languages most commonly used in data science. Out of curiosity and a bit of boredom, I decided to learn a bit of R syntax. After that experience, I decided to continue learning R. I decided that I would learn more R using DataQuest’s Data Analyst in R track.

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Why Data Science?

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Photo by Jon Tyson on Unsplash

For the past several years, I’ve explored different career paths in tech. I’ve played around with web development but found that was not a good fit for me.  For a long time, I wanted to be a UX designer which later turned into UX writing. I spoke with people who worked in UX, attended meetups and different events that were UX focused. Many of the classes I took as part of my graduate program were design-focused.

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