As of 2024, JavaScript and HTML/CSS were the most commonly used programming languages among software developers around the world, with more than 62 percent of respondents stating that they used JavaScript and just around 53 percent using HTML/CSS. Python, SQL, and TypeScript rounded out the top five most widely used programming languages around the world. Programming languages At a very basic level, programming languages serve as sets of instructions that direct computers on how to behave and carry out tasks. Thanks to the increased prevalence of, and reliance on, computers and electronic devices in today’s society, these languages play a crucial role in the everyday lives of people around the world. An increasing number of people are interested in furthering their understanding of these tools through courses and bootcamps, while current developers are constantly seeking new languages and resources to learn to add to their skills. Furthermore, programming knowledge is becoming an important skill to possess within various industries throughout the business world. Job seekers with skills in Python, R, and SQL will find their knowledge to be among the most highly desirable data science skills and likely assist in their search for employment.

The most popular programming language used in the past 12 months by software developers worldwide is JavaScript as of 2024, according to ** percent of the software developers surveyed. This is followed by Python at ** percent of the respondents surveyed.

Introduction

Most Popular Programming Languages Statistics: Programming languages allow us to communicate with computers, enabling the creation of scripts, programs, and applications. Each language has its syntax, symbols, and keywords for writing code. Even a small mistake, like a misplaced comma, can cause the code to fail. These languages are also essential for building websites.

Each language has specific advantages and disadvantages when it comes to application, but with the right skills and techniques, coding can be enjoyable. Let's take a look at the most recent statistics for the most popular programming languages.

JavaScript and Java were some of the most tested programming languages on the DevSkiller platform as of 2024. SQL and Python ranked second and fourth, with ** percent and ** percent of respondents testing this language in 2024, respectively. Nevertheless, the tech skill developers wanted to learn the most in 2024 was related to artificial intelligence, machine learning, and deep learning. At the same time, the fastest growing IT skills among DevSkiller customers were C/C++ and data science, while cybersecurity ranked third. Software skills When it came to the most used programming language among developers worldwide, JavaScript took the top spot, chosen by 62 percent of surveyed respondents. Most software developers learn how to code between 11 and 17 years old, with some of them writing their first line of code by the age of 5. Moreover, seven out of 10 developers learned how to program by accessing online resources such as videos and blogs. Software skills pay In 2024, the average annual software developer’s salary in the U.S. amounted to nearly ** thousand U.S. dollars, while in Germany, it totaled above ** thousand U.S. dollars. The programming languages associated with the highest salaries worldwide in 2024 were Clojure and Erlang.

With the development of science and technology, there are more and more electronic games on the market. The types of electronic games have also become more diversified. At present, there are many programming languages on the market that can be used to develop games. As a beginner of game development, it is difficult for us to choose an appropriate programming language to develop specific types of games. So we investigate some famous game and the programing languages they use.

A table listing common programming languages used in data science, their purpose, and key capabilities.

Context

Well, I was looking for a Most Popular Programming Languages dataset for my YouTube channel video and couldn't find anything decent. So, I collect it for my use and share it.

Content

This dataset contains data about the Most Popular Programming Languages from 2004 to 2024. All Programming Languages values are in percentage form out of 100 %

Acknowledgements

The data was pulled from https://pypl.github.io

If this dataset is useful for you then don't forget to upvote.

The most popular programming language software worldwide in 2022 was CSS, with a market share of ** percent. Other noteworthy programming languages include ASP.NET, Lua and PHP. The source indicates that programming languages are formal coding languages that specify various sets of instructions that can be used to produce a wide variety of outputs.

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

The most demanded programming languages by recruiters in 2025 were Python, JavaScript, and Java, with around ** percent of recruiters looking to hire people with these programming skills.

Data Statement for CommitBench

EXECUTIVE SUMMARY

CURATION RATIONALE

DOCUMENTATION FOR SOURCE DATASETS

LANGUAGE VARIETIES

SPEAKER DEMOGRAPHIC

ANNOTATOR DEMOGRAPHIC

SPEECH SITUATION AND CHARACTERISTICS

PREPROCESSING AND DATA FORMATTING

CAPTURE QUALITY

LIMITATIONS

METADATA

DISCLOSURES AND ETHICAL REVIEW

ABOUT THIS DOCUMENT

Analysis of ‘Most Popular Programming Languages Since 2004’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/muhammadkhalid/most-popular-programming-languages-since-2004 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Well, I was looking for a Most Popular Programming Languages dataset for my YouTube channel video and couldn't find anything decent. So, I collect it for my use and share it.

Content

This dataset contains data about the Most Popular Programming Languages from 2004 to 2022. All Programming Languages values are in percentage form out of 100 %

Acknowledgements

The data was pulled from https://pypl.github.io

If this dataset is useful for you then don't forget to upvote.

--- Original source retains full ownership of the source dataset ---

Each person who files bankruptcy is required to attend a meeting of creditors and respond to questions under oath from the trustee and creditors. The meetings are held nationwide. In those locations where the room is controlled by the USTP, if a participant (debtor or creditor) has limited English proficiency, an interpreter is provided free of charge via a conference phone. The number and type of languages interpreted, along with the location where the service was provided, is collected monthly by the USTP for oversight, billing, and statistical purposes. Data are provided in delimited text files. Each entry represents one interpreting session, which may include more than one case.

Poland Individuals: Writing Code in a Programming Language: 55-64 data was reported at 2.200 % in 2024. This records an increase from the previous number of 1.700 % for 2023. Poland Individuals: Writing Code in a Programming Language: 55-64 data is updated yearly, averaging 0.700 % from Dec 2015 (Median) to 2024, with 7 observations. The data reached an all-time high of 2.200 % in 2024 and a record low of 0.500 % in 2017. Poland Individuals: Writing Code in a Programming Language: 55-64 data remains active status in CEIC and is reported by Statistics Poland. The data is categorized under Global Database’s Poland – Table PL.G040: Individuals Carrying Out Software Related Activities: by Age.

This is a repository for codes and datasets for the open-access paper in Linguistik Indonesia, the flagship journal for the Linguistic Society of Indonesia (Masyarakat Linguistik Indonesia [MLI]) (cf. the link in the references below).To cite the paper (in APA 6th style):Rajeg, G. P. W., Denistia, K., & Rajeg, I. M. (2018). Working with a linguistic corpus using R: An introductory note with Indonesian negating construction. Linguistik Indonesia, 36(1), 1–36. doi: 10.26499/li.v36i1.71To cite this repository:Click on the Cite (dark-pink button on the top-left) and select the citation style through the dropdown button (default style is Datacite option (right-hand side)This repository consists of the following files:1. Source R Markdown Notebook (.Rmd file) used to write the paper and containing the R codes to generate the analyses in the paper.2. Tutorial to download the Leipzig Corpus file used in the paper. It is freely available on the Leipzig Corpora Collection Download page.3. Accompanying datasets as images and .rds format so that all code-chunks in the R Markdown file can be run.4. BibLaTeX and .csl files for the referencing and bibliography (with APA 6th style). 5. A snippet of the R session info after running all codes in the R Markdown file.6. RStudio project file (.Rproj). Double click on this file to open an RStudio session associated with the content of this repository. See here and here for details on Project-based workflow in RStudio.7. A .docx template file following the basic stylesheet for Linguistik IndonesiaPut all these files in the same folder (including the downloaded Leipzig corpus file)!To render the R Markdown into MS Word document, we use the bookdown R package (Xie, 2018). Make sure this package is installed in R.Yihui Xie (2018). bookdown: Authoring Books and Technical Documents with R Markdown. R package version 0.6.

new lib/summary-stats.js script compiles a sorted CSV of all languages used with data on the number of repos the language appears in, the number for which it is recorded as the primary language, and the total lines of code in the language across all repos.

Dataset

This dataset was created by Wael Rahhal

Contents

In the fourth quarter 2024, the most popular programming languages in published job offers in Poland were ***********, and Java.

According to a survey conducted between late 2021 and early 2022, JavaScript is the most used programming language worldwide, with 56 percent of respondents reporting that they use the language. Python was the second most used language at 50.7 percent.

Study Tool and Dataset[Environment preparation]1. Python version: 3.6 or upper version2. Dependent libraries:progressbar, nltk, textblob, sklearn, matplotlib, plotly, fuzzywuzzy, statsmodels, corpora, etc.Utilize pip install [lib_name] to install the libraries.[Running the program]1. Command linecollect.py -- for data collection, vulnerability categorization and language interfacing classification.Type "collect.py -h" for help.2. comman parameters collect.pycollect.py -s collect -- grab raw repositories from github.collect.py -s repostats -- collect basic properies for each repository.collect.py -s langstats -- empirical analysis for language information: profile size, combinations, etc.collect.py -s cmmts -- collect commits for each project, and classify the commits with fuccywuzzy.collect.py -s nbr -- NBR analysis on the dataset.collect.py -s clone -- clone all projects to local storage.collect.py -s apisniffer -- classify the projects by language interface typesWe also provide the shell script for parallel execution in multiple processes to speed up the data collection and analysis.cmmts.sh [repository number]: execute the commit collection and classification in multiple processesclone.sh [repository number]: clone the repositories to local in multiple processessniffer.sh [repository number]: identify and category the repositories by langauge interfacing mechanisms in multiple processes3. Dataset Data/OriginData/Repository_List.csv: original repository profile grabbed from github. Data/CmmtSet: original commit data by repository, each file is named as the repository ID. Data/Issues: original issue information by repository. Data/StatData/CmmtSet: classified commit data by repository, each commit can be retrieved from github through 'sha' field. Data/StatData/ApiSniffer.csv: classified repositories by language interfacing mechanisms