Who do you think is the naughtiest character in South Park? You’ll know the answer by the end of this article—and I’m sure you’ll be surprised! In the previous article of this series, I showed you how to use R to analyze South Park dialog. We mostly focused on the show overall. This time, I’ll take a closer look at the most famous South Park characters. We’ll see how much they talk and how their sentiments change across the show.
Have you ever liked a TV show so much that simply watching it wasn’t enough anymore? Read on to discover how I used R to analyze South Park dialog and ratings! South Parkis an American TV show for adults that’s well known for being very satirical—the series has made fun of nearly every celebrity and isn’t afraid to be provocative. I literally watch the show every day. I also do lots of data analysis in R every day!
Information technology is one of the hottest industries in the world and offers thousands of job in each major city around the globe. If you’re a student or professional who wants to get IT skills and find your first job in the industry, Vertabelo Academy can help you get started. The range of IT jobs available is stunning—from software engineers to system administrators and data scientists, IT rules the job market.
One of the oldest jokes in the business world goes like this: The CFO asked the CEO, “What happens if we invest in developing people and they leave us?” The CEO answered, “What happens if we don’t and they stay?” If you’re like the CEO and want to help your employees grow, this article will explain how you can do so with Vertabelo Academy. Vertabelo Academy has been around since 2015.
R and Python are two of the most popular data science languages, but which one is better? And will Python replace R in the near future? Let’s find out! R vs. Python: the Basics First, some history. R first appeared in 1990; it was derived from the language S, a statistical programming language developed for statisticians. It was (and still is) commonly used in educational settings and is a favorite among biostatisticians.
Programmers commonly have many questions about R, a popular programming language in data science and analysis. R is used all over the world by professionals in the fields of data science, data visualization, data mining, and statistical analysis. But what exactly is R? Where did it come from? And why is it being used specifically by data science professionals? This article attempts to answer all these questions, including the most important of them all: Should you be learning R as well?
Have you ever wondered how you can deal with an overwhelming amount of data? How do you use it? How do you understand what it’s saying? And last but not least, how do you present your data to the world such that everyone understands your point? In this article, we’ll explore these questions to understand the importance of data visualization. Where are the data? When I want someone to understand my perspective, I try to visualize it precisely so I can communicate my thoughts.
Will robots replace humans in the near future? As machine learning and artificial intelligence continue to grow in popularity, this question becomes all the more relevant. Which jobs will become extinct, and what will society looks like in the future? If you’re a data analyst whose worried about their job security, don’t worry—there’s still hope for you! In this article, we’ll take a look at the skills a data analyst can acquire to become a data scientist and rise above these pesky robots 🙂
Unlock the potential of data! With this course, you’ll learn about data analytics, data science, statistical analysis, and functions in the R programming language. This course is perfect for people who have no prior knowledge of computer science or R programming. With Introduction to R, you’ll learn to work in the R programming language as you enter the promising world of data science. Why R is so famous According to the TIOBE programming community index, which ranks the popularity of all programming languages, R is one of the hottest programming languages of 2018.
In this article, we’ll take a look at guidelines you should follow to create compelling visuals. Our goal is to learn how to effectively convey information through graphics. Have you ever looked at raw data—spreadsheets of stray numbers—and struggled to make sense of it? We’ve all been there, but it’s no surprise—because the human brain processes visualizations and images 10,000 times faster than raw data. In fact, 80% of the information we absorb comes from visuals, and the remaining 20% is text.