We speak not only in our native language
And compiled them into this two-part guide (part 2).
Do you know that 21% of people open an app 50+ times per day? Yes, you heard that right. With the rapid development of technology, the mobile app now made many things possible, which was previously unthinkable.
And that's why there is an incredible increase in the number of mobile users. According to a recent mobile app development stat, the number of mobile users worldwide is projected to increase to 6.95 billion by the end of 2020.
In the last couple of years, the mobile app development industry has grown manifold, changing how businesses function around the world. If you are planning to jump into mobile app development, then choosing the right programming language will be the most significant challenge.
There are more than 600 programming languages, and each one has its own perks and popularity. Are you pondering which language would be best for developing a stunning app?
Several factors come to mind when making this choice, but the most important one is the language's demand. Here in this blog, I have listed the best programming language for mobile apps in terms of popularity and demand. Let's start!
I’ve searched high and low in an attempt to find current trends and recent changes in the English language, but have faced only articles about what has changed since the time of Shakespeare. So, I’ve decided that I’d rather present the data I’ve gathered myself throughout years of teaching by method of observation.
Modern-day agile English teaching has come to take the place of rigid, cut-and-dried lessons that are fast becoming a thing of the past.
Let me clarify what I mean by agile teaching that is bound to substitute conventional teaching.
Some decades ago and up until recently it was perfectly valid to choose a certain textbook and go through it module by module together with your students (be it a group or individual learners). Given the abundance of high-quality materials readily accessible online and offline, it is completely unthinkable to proceed with this outdated approach.
They say, each professional developer must have done at least three pet projects: a sophisticated logging utility, a smart json parser, and an amazing programming language. Once we have both logger and parser accomplished, we finally decided to reveal our desperate success in creation one of the most innovative programming languages named Silverfish.
I have been studying English using various methods and resources over five years. Language learning is not my greatest talent but I have achieved B2 level (from A2) using only my smartphone and PC. I found a set of features that really helps you study a foreign language. Some of them are crucial, others are just useful. Under the cut you will find a rating of the language learning apps that I composed by analyzing these features, As Objective As Possible.
While a great deal of data researchers will discuss the customary shortcomings like data wrangling in R or data representation in Python, ongoing improvements like Altair for Python or R have adequately reacted to these shortcomings.
So which one would it be a good idea for you to decide for your next data investigation venture?
R has been ruling this space for a long time now. This bodes well as this programming language was explicitly intended for analysts.
Figure 1: Top 10 programming languages hosted by GitHub by repository count
One of the necessary challenges that GitHub faces is to be able to recognize these different languages. When some code is pushed to a repository, it’s important to recognize the type of code that was added for the purposes of search, security vulnerability alerting, and syntax highlighting—and to show the repository’s content distribution to users.
Linguist is the tool we currently use to detect coding languages at GitHub. Linguist a Ruby-based application that uses various strategies for language detection, leveraging naming conventions and file extensions and also taking into account Vim or Emacs modelines, as well as the content at the top of the file (shebang). Linguist handles language disambiguation via heuristics and, failing that, via a Naive Bayes classifier trained on a small sample of data.
Although Linguist does a good job making file-level language predictions (84% accuracy), its performance declines considerably when files use unexpected naming conventions and, crucially, when a file extension is not provided. This renders Linguist unsuitable for content such as GitHub Gists or code snippets within README’s, issues, and pull requests.
In order to make language detection more robust and maintainable in the long run, we developed a machine learning classifier named OctoLingua based on an Artificial Neural Network (ANN) architecture which can handle language predictions in tricky scenarios. The current version of the model is able to make predictions for the top 50 languages hosted by GitHub and surpasses Linguist in accuracy and performance.
I’m an active user of Google Chrome. That’s why I’ve come up with this list of useful extensions that will help English learners to enhance writing, spelling, listening skills, and extend their vocabulary.
One one hand I don't want to be the final authority, but on the other hand, I'd like to share my point of view on how to learn English. The English language is not secret knowledge; it is just a lot of hard training. One of the most important bullets is constantly improving English. You should do it from day to day if you want to approach result. It must not loathe torture for you, It means that you should find out something interesting in that process.