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.
In my previous articles, I explained how you can check for associations between two continuous and two discrete variables. This time, we’ll check for linear dependencies between continuous and discrete variables. You can do this by measuring the variance between the means of the continuous variable and different groups of the discrete variable. The null hypothesis here is that all variances between the means are a result of the variance within each group.
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.
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 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.
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 this article, we’ll take a look at some of god-awful pie charts and hopefully learn a thing or two about good data visualization. March 14th is also known as PI Day. Mathematicians rejoice! π is a constant — the ratio of a circle’s circumference to its diameter — and it’s used in many different formulas. Baking and eating pies is super popular on this day — ’cause, you know, people just love their homophones.
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!
In my previous article, we looked at how you can calculate linear dependencies between two continuous variables with covariance and correlation. Both methods use the means of the two variables in their calculations. However, mean values and other population moments make no sense for categorical (nominal) variables. For instance, if you denote “Clerical” as 1 and “Professional” as 2 for an occupation variable, what does the average of 1.5 signify?
In descriptive statistics, the first four population moments include center, spread, skewness, and kurtosis or peakedness of a distribution. In this article, I am explaining the third and fourth population moments, the skewness and the kurtosis, and how to calculate them. Mean uses the values on the first degree in the calculation; therefore, it is the first population moment. Standard deviation uses the squared values and is therefore the second population moment.
My previous article explained how to calculate frequencies using T-SQL queries. Frequencies are used to analyze the distribution of discrete variables. Today, we’ll continue learning about statistics and SQL. In particular, we’ll focus on calculating centers of distribution. In statistics, certain measurements are known as moments. You can describe continuous variables (i.e. a variable that has a large range of possible numbers, such as household incomes in a country) with population moments.