Data science is hot right now. If you want to learn more about it, where should you go? Online, of course! Check out our favorite data science sites. Whether you’re a beginner or a pro, these are the sites you should know. Not so long ago, if you wanted information on a topic like data science, you had to look for it—either at your local library or at a university.
How do you visualize the results of an SQL query? You usually need to use multiple tools to achieve that. Why not use a tool that communicates with SQL directly and can perform any data analysis task? R is a data-oriented programming language that uses packages (libraries) to help you with nearly every imaginable data analysis task and more. Some of these packages allow R connect to an SQL database so you can query it and perform any desired action on the result set.
Unfortunately, data isn’t always available in the exact structure you prefer. And there’s nothing more frustrating than having inconsistent, untidy data that produces biased results. Let’s take a look at how the Tidyverse can help. What is Tidyverse? Before you can conduct any analyses or draw any conclusions, you often need to reorganize your data. The Tidyverse is a collection of R packages built around the basic concept that data in a table should have one observation per row, one variable per column, and only one value per cell.
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?
Within organizations, Scrum promotes efficient time and process management along with better team building and leadership. In order to implement Scrum, you’ll need to follow a few simple rules. Introducing Scrum Today, we have the power to collect precise data both quickly and in vast quantities. In fact, 90% of the data available today was collected in the last two years alone. The rise of big data has greatly increased demand for data scientists, but the profession is one where few candidates possess the right skills.
Brush up on your data science and SQL skills with Vertabelo Academy’s interactive courses. Why Vertabelo Academy? You get instant access to lessonsthat teach various concepts of SQL, data science, and programming in R (soon also in Python!). Our courses are appropriate for people who have no prior knowledge of computer science or programming. The only requirement is a web browser. No need to install databases, download example tables, or spend time inventing exercises for yourself.
Here’s a reality check: Big Data has hit us like a speeding truck on the highway of business intelligence. In today’s digital age, we’re generating data about ourselves that were once considered private, and we’re doing it willingly! From what we eat and wear to where we are at all times, nearly everything is now public knowledge. The data generated is a potential diamond mine for everyone in business, from one-person companies to Fortune 500 A-Listers alike, all thanks to the Internet.
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.
In today’s data-driven world, a good visualization goes a long way in helping people make sense of numbers. Every day at the office, we’re working hard to create programming and data science content that is accessible to everyone. We aim to produce content that is easy to understand, primarily for people with no IT background. And you know what? Ironically, this stuff ain’t easy even if you’re an IT specialist!
Show, don’t tell! Share data insights in stunning color and display with ggplot2, a wonderful R package for visualizing data. Ggplot2: Grammar of Graphics The end of qualitative data analysis should be clear—beautiful data visualizations. We are visual beings, after all, and a picture tells us far more than raw numbers! Among the many visualization tools, one in particular stands out : ggplot2—a free, open-source, and easy-to-use package that has become a favorite among many R programmers.