Each year, I try to arrange some kind of birthday surprise that’ll exceed my husband’s expectations. This year, I’ve got an excellent idea—I’m going to organize a ski trip for us and a few friends with the help of R and the apply family of functions! Overview My husband’s loved snowboarding ever since he learned it in college, and he’s always wanted to find some time to visit our local mountains in the winter.
There I was—at Nvidia Deep learning & AI, the most prestigious deep learning event, waiting for my hands-on training to begin. It felt great to be there! But as I waited for things to start, observing the others who sat around me, I realized something: most of the attendants were men! In a crowded hall where around 200 people were waiting for a lecture, less than 10% were women. Where were the rest, and why was I one of the few female representatives who attended this conference?
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.
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 🙂
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.
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.
Earth’s 7.5 billion people together use several billion different devices, generating an annual global IP traffic of more than one zettabyte. Out of these impressive numbers rises a new field above the others — “data science”. Is data science an inevitable reality, or will it yet be dismissed as just another “wave of the future” that never came to be? A few days ago I signed a new employment contract with my company.
When it comes to information management, duplicates present one of the most common challenges to data quality. In this article, I’ll explain how it is possible to find and distinguish duplicate names with the help of the SQL data programming language. I really like my maiden name. The reason I like it so much is because it’s rare. My maiden name (first with last) provided a unique identifier on platforms such as LinkedIn, Facebook, Twitter and similar.