This statistic shows the program outcome data for online education providers that were the most requested by students in the United States in 2016. In 2016, ** percent of schools reported that students asked for placement and employment rates.

Business Software Alliance is a trade association that represents the world's leading software companies, including Autodesk, IBM, and Symantec. The organization's members are committed to promoting the use of legitimate software and ensuring the integrity of their intellectual property.

As a result, the data housed on BSA's website is rich in information related to the software industry, including software licensing, anti-piracy efforts, and digital piracy statistics. The data includes information on software usage, software development, and the impact of piracy on the technology industry. With its focus on promoting legitimate software use, the data on BSA's website provides valuable insights into the global software industry.

Investigator(s): Bureau of Justice Statistics Data in this collection examine the processing of federal offenders. The Cases Terminated files (Parts 1-3 and 25-28) contain information about defendants in criminal cases filed in the United States Federal District Court and terminated in the calendar year indicated. Defendants in criminal cases may either be individuals or corporations, and there is one record for each defendant in each case terminated. Information on court proceedings, date the case was filed, date the case was terminated, most serious charge, and reason for termination are included. The Docket and Reporting System files (Parts 4-7 and 31-34) include information on suspects in investigative matters that took an hour or more of a United States Attorney's time with one of the following outcomes: (1) the United States Attorney declined to prosecute, (2) the case was filed in Federal District Court, or (3) the matter was disposed by a United States magistrate. Codes for each disposition and change of status are also provided.The Pretrial Services data (Parts 8 and 22) present variables on the circuit, district, and office where the defendant was charged, type of action, year of birth and sex of the defendant, major offense charge, and results of initial and detention hearings. The Parole Decisions data (Part 9) contain information from various parole hearings such as court date, appeal action, reopening decision, sentence, severity, offense, and race and ethnicity of the defendant. The Offenders Under Supervision files (Parts 15-16 and 37-40) focus on convicted offenders sentenced to probation supervision and federal prisoners released to parole supervision. The Federal Prisoner files (Parts 18 and 20) supply data on when an offender entered and was released from confinement, as well as the amount of time served for any given offense.Years Produced: Annually.NACJD has prepared a resource guide for the Federal Justice Statistics Program.

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Online Data Science Training Programs Market Size 2025-2029

The online data science training programs market size is forecast to increase by USD 8.67 billion, at a CAGR of 35.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for data science professionals in various industries. The job market offers lucrative opportunities for individuals with data science skills, making online training programs an attractive option for those seeking to upskill or reskill. Another key driver in the market is the adoption of microlearning and gamification techniques in data science training. These approaches make learning more engaging and accessible, allowing individuals to acquire new skills at their own pace. Furthermore, the availability of open-source learning materials has democratized access to data science education, enabling a larger pool of learners to enter the field. However, the market also faces challenges, including the need for continuous updates to keep up with the rapidly evolving data science landscape and the lack of standardization in online training programs, which can make it difficult for employers to assess the quality of graduates. Companies seeking to capitalize on market opportunities should focus on offering up-to-date, high-quality training programs that incorporate microlearning and gamification techniques, while also addressing the challenges of continuous updates and standardization. By doing so, they can differentiate themselves in a competitive market and meet the evolving needs of learners and employers alike.

What will be the Size of the Online Data Science Training Programs Market during the forecast period?

Request Free SampleThe online data science training market continues to evolve, driven by the increasing demand for data-driven insights and innovations across various sectors. Data science applications, from computer vision and deep learning to natural language processing and predictive analytics, are revolutionizing industries and transforming business operations. Industry case studies showcase the impact of data science in action, with big data and machine learning driving advancements in healthcare, finance, and retail. Virtual labs enable learners to gain hands-on experience, while data scientist salaries remain competitive and attractive. Cloud computing and data science platforms facilitate interactive learning and collaborative research, fostering a vibrant data science community. Data privacy and security concerns are addressed through advanced data governance and ethical frameworks. Data science libraries, such as TensorFlow and Scikit-Learn, streamline the development process, while data storytelling tools help communicate complex insights effectively. Data mining and predictive analytics enable organizations to uncover hidden trends and patterns, driving innovation and growth. The future of data science is bright, with ongoing research and development in areas like data ethics, data governance, and artificial intelligence. Data science conferences and education programs provide opportunities for professionals to expand their knowledge and expertise, ensuring they remain at the forefront of this dynamic field.

How is this Online Data Science Training Programs Industry segmented?

The online data science training programs industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeProfessional degree coursesCertification coursesApplicationStudentsWorking professionalsLanguageR programmingPythonBig MLSASOthersMethodLive streamingRecordedProgram TypeBootcampsCertificatesDegree ProgramsGeographyNorth AmericaUSMexicoEuropeFranceGermanyItalyUKMiddle East and AfricaUAEAPACAustraliaChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Type Insights

The professional degree courses segment is estimated to witness significant growth during the forecast period.The market encompasses various segments catering to diverse learning needs. The professional degree course segment holds a significant position, offering comprehensive and in-depth training in data science. This segment's curriculum covers essential aspects such as statistical analysis, machine learning, data visualization, and data engineering. Delivered by industry professionals and academic experts, these courses ensure a high-quality education experience. Interactive learning environments, including live lectures, webinars, and group discussions, foster a collaborative and engaging experience. Data science applications, including deep learning, computer vision, and natural language processing, are integral to the market's growth. Data analysis, a crucial application, is gaining traction due to the increasing demand

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 data contain records of charges filed against defendants whose cases were filed by United States attorneys in United States district court during fiscal year 2010. The data are charge-level records, and more than one charge may be filed against a single defendant. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central Charge file. The charge-level data may be linked to defendant-level data (extracted from the EOUSA Central System file) through the CS_SEQ variable, and it should be noted that some defendants may not have any charges other than the lead charge appearing on the defendant-level record. The Central Charge and Central System data contain variables from the original EOUSA files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

The data contain records of suspects in federal criminal matters concluded by United States attorneys or United States magistrates during fiscal year 2000. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central System file. Records include suspects in criminal matters, and are limited to suspects whose matters were not declined immediately by the United States attorneys. According to the EOUSA, the United States attorneys conduct approximately 95 percent of the prosecutions handled by the Department of Justice. The Central System data contain variables from the original EOUSA files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 1.2-1.5. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

Web Scraper Software Market Valuation – 2024-2031

Web Scraper Software Market was valued at USD 568.2 Million in 2024 and is projected to reach USD 1628.6 Million by 2031, growing at a CAGR of 14.1% from 2024 to 2031.

Global Web Scraper Software Market Drivers

Data-Driven Decision Making: Businesses increasingly rely on data-driven insights to make informed decisions. Web scraping tools enable organizations to collect large amounts of structured and unstructured data from various websites, empowering them to analyze market trends, consumer behavior, and competitor activities.

Price Intelligence: E-commerce businesses utilize web scraping to monitor competitor pricing, identify pricing opportunities, and optimize their own pricing strategies.

Market Research and Analysis: Web scraping tools help researchers and analysts gather data on market trends, consumer sentiment, and industry benchmarks. This data is invaluable for conducting in-depth market research and analysis.

Global Web Scraper Software Market Restraints

Ethical and Legal Considerations: Web scraping can raise ethical and legal concerns, particularly when it violates website terms of service or copyright laws. It's crucial to adhere to ethical guidelines and respect website owners' rights.

Technical Challenges: Web scraping can be technically complex, requiring knowledge of programming languages like Python and libraries such as Beautiful Soup and Scrapy. Additionally, websites often implement anti-scraping measures, making data extraction challenging.

Introduction

Dark Web Statistics: The Dark Web refers to the encrypted portion of the internet that is not indexed by traditional search engines.

It exists as a hidden network that can only be accessed through specific software, configurations, and authorization protocols.

The primary technology used to access the Dark Web is the Tor network, which allows users to maintain anonymity and privacy while accessing websites and services.

The data contain records of defendants in federal criminal cases terminated in United States District Court during fiscal year 2015. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central System file. According to the EOUSA, the United States attorneys conduct approximately 95 percent of the prosecutions handled by the Department of Justice. The Central Charge and Central System data contain variables from the original EOUSA files as well as additional analysis variables. Variables containing identifying information (e.g., name, Social Security Number) were either removed, coarsened, or blanked in order to protect the identities of individuals. These data are part of a series designed by Abt and the Bureau of Justice Statistics. Data and documentation were prepared by Abt.

The data contain records of sentenced offenders committed to the custody of the Bureau of Prisons (BOP) during fiscal year 2000. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database, as well as "SAF" variables that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 7.9-7.16. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

This statistic shows the percentage of prospective and currently enrolled students who chose to enroll in online and brick-and-mortar colleges and programs in the United States in 2015. In 2015, about 41 percent respondents chose to attend on-location colleges and programs in the United States.

This dataset contains data from the National Center for Education Statistics' Academic Library Survey, which was gathered every two years from 1996 - 2014, and annually in IPEDS starting in 2014 (this dataset has continued to only merge data every two years, following the original schedule). This data was merged, transformed, and used for research by Starr Hoffman and Samantha Godbey.This data was merged using R; R scripts for this merge can be made available upon request. Some variables changed names or definitions during this time; a view of these variables over time is provided in the related Figshare Project. Carnegie Classification changed several times during this period; all Carnegie classifications were crosswalked to the 2000 classification version; that information is also provided in the related Figshare Project. This data was used for research published in several articles, conference papers, and posters starting in 2018 (some of this research used an older version of the dataset which was deposited in the University of Nevada, Las Vegas's repository).SourcesAll data sources were downloaded from the National Center for Education Statistics website https://nces.ed.gov/. Individual datasets and years accessed are listed below.[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Academic Libraries Survey (ALS) Public Use Data File, Library Statistics Program, (2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/surveys/libraries/aca_data.asp[dataset] U.S. Department of Education, National Center for Education Statistics, Institutional Characteristics component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Enrollment component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006, 2004, 2002, 2000, 1998, 1996), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Human Resources component, Integrated Postsecondary Education Data System (IPEDS), (2020, 2018, 2016, 2014, 2012, 2010, 2008, 2006), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Employees Assigned by Position component, Integrated Postsecondary Education Data System (IPEDS), (2004, 2002), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7[dataset] U.S. Department of Education, National Center for Education Statistics, Fall Staff component, Integrated Postsecondary Education Data System (IPEDS), (1999, 1997, 1995), https://nces.ed.gov/ipeds/datacenter/login.aspx?gotoReportId=7

Global demand for web scrapping software was valued at US$ 330 million in 2022 and is expected to reach US$ 363 million in 2023. The market is anticipated to grow at a CAGR of 15% to reach a valuation of US$ 1,469 million by 2033.

A tech stack represents a combination of technologies a company uses in order to build and run an application or project. The most popular technology skill in the web development tech stack in 2022 was HTML, chosen by nearly 57 percent of respondents. CSS ranked second, being preferred by almost a third of respondents.

Different science fair experiences of high school (HS) and post high school (PHS) students depending upon whether or not they received help from scientists.

Historical Dataset of Kansas Online Learning Program is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),Distribution of Students By Grade Trends,American Indian Student Percentage Comparison Over Years (2022-2023),Asian Student Percentage Comparison Over Years (2022-2023),Hispanic Student Percentage Comparison Over Years (2022-2023),Black Student Percentage Comparison Over Years (2022-2023),White Student Percentage Comparison Over Years (2022-2023),Diversity Score Comparison Over Years (2022-2023)

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.

The data contain records of defendants in federal criminal cases terminated in United States District Court during fiscal year 1999. The data were constructed from the Executive Office for United States Attorneys (EOUSA) Central System file. According to the EOUSA, the United States attorneys conduct approximately 95 percent of the prosecutions handled by the Department of Justice. The Central System data contain variables from the original EOUSA files as well as additional analysis variables, or "SAF" variables, that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.

The data contain records of sentenced offenders committed to the custody of the Bureau of Prisons (BOP) during fiscal year 2007. The data include commitments of United States District Court, violators of conditions of release (e.g., parole, probation, or supervised release violators), offenders convicted in other courts (e.g., military or District of Columbia courts), and persons admitted to prison as material witnesses or for purposes of treatment, examination, or transfer to another authority. These data include variables that describe the offender, such as age, race, citizenship, as well as variables that describe the sentences and expected prison terms. The data file contains original variables from the Bureau of Prisons' SENTRY database, as well as "SAF" variables that denote subsets of the data. These SAF variables are related to statistics reported in the Compendium of Federal Justice Statistics, Tables 7.9-7.16. Variables containing identifying information (e.g., name, Social Security Number) were replaced with blanks, and the day portions of date fields were also sanitized in order to protect the identities of individuals. These data are part of a series designed by the Urban Institute (Washington, DC) and the Bureau of Justice Statistics. Data and documentation were prepared by the Urban Institute.