## 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.

## Working With Lists

The next topic I learned about in the DataQuest Data Analyst in R track is a list. I decided that in honor of music’s biggest night, also known the Grammys that took place last night, the examples in this post are going to be music focused. So without further ado, let’s get it started!

## Working With Matrices

A matrix is a collection of elements of the same data type, with the data being arranged into rows and columns. Because it consists of both rows and columns, matrices are considered two-dimensional as opposed to vectors, which are considered one-dimensional.

## Working With Vectors(Part 2)

This post is a continuation of what I learned in the DataQuest lesson, Working with Vectors. If you want to read part one, click here.

## Working With Vectors(Part 1)

For this post, I’ll discuss the working with vectors lesson on DataQuest’s Data Analyst in R track. I’m going to dive deeper into vectors, talking about indexing in R and R’s different data types. There was so much that I learned in this lesson that I decided to break this topic up into two posts. So let’s jump right in!

## 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.

## Why Data Science?

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.