The statistic displays the most wanted data science skills in the United States as of April 2019. As of the measured period, 76.13 percent of data scientist job openings on LinkedIn required a knowledge of the programming language Python.

The interview data was gathered for a project that investigated the practices of instructors who use quantitative data to teach undergraduate courses within the Social Sciences. The study was undertaken by employees of the University of California, Santa Barbara (UCSB) Library, who participated in this research project with 19 other colleges and universities across the U.S. under the direction of Ithaka S+R. Ithaka S+R is a New York-based research organization, which, among other goals, seeks to develop strategies, services, and products to meet evolving academic trends to support faculty and students.

The field of Social Sciences has been notoriously known for valuing the contextual component of data and increasingly entertaining more quantitative and computational approaches to research in response to the prevalence of data literacy skills needed to navigate both personal and professional contexts. Thus, this study becomes particularly timely to identify current instructors’ practi..., The project followed a qualitative and exploratory approach to understand current practices of faculty teaching with data. The study was IRB approved and was exempt by the UCSB’s Office of Research in July 2020 (Protocol 1-20-0491).Â

The identification and recruitment of potential participants took into account the selection criteria pre-established by Ithaka S+R: a) instructors of courses within the Social Sciences, considering the field as broadly defined, and making the best judgment in cases the discipline intersects with other fields; b) instructors who teach undergraduate courses or courses where most of the students are at the undergraduate level; c) instructors of any rank, including adjuncts and graduate students; as long as they were listed as instructors of record of the selected courses; d) instructors who teach courses were students engage with quantitative/computational data.Â

The sampling process followed a combination of strategies to more easily identify instructo..., The data folder contains 10Â pdf files with de-identified transcriptions of the interviews and the pdf files with the recruitment email and the interview guide.Â

In the academic year of 2022, it is expected that 551,460 female and 331,530 male students will earn a Master’s degree in the United States. These figures are a significant increase from the academic year of 1950, when 16,980 female students and 41,220 male students earned a Master’s degree.

What is a Master’s degree?

A Master’s degree is an academic degree granted by universities after finishing a Bachelor’s degree. Master’s degrees focus in on a specific field and are more specialized than a Bachelor’s. A typical Master’s program is about two years long, with the final semester focusing on the thesis. Master’s degree programs are usually harder to get into than Bachelor’s degree programs, due to the rigor of the program. Because these programs are so competitive, those with a Master’s degree are typically paid more than those with a Bachelor’s degree.

Master’s degrees in the United States

The number of master’s degrees granted in the United States has steadily increased since the 1970s and is expected to continue to increase. In 2021, the Master’s degree program with the worst job prospects in the United States by mid-career median pay was counseling, while the program with the best job prospects was a physician's assistant.

The Speech to Text Software market has seen remarkable growth over recent years, driven by advancements in artificial intelligence, machine learning, and natural language processing technologies. This innovative software enables users across various industries to convert spoken language into written text with high a

The Investment Trading Software market has seen significant evolution over the years, driven by the increasing digitalization of financial services and the growing demand for real-time trading solutions. This software category includes a wide range of applications designed to facilitate the buying and selling of sec

Success.ai’s Education Industry Data provides access to comprehensive profiles of global professionals in the education sector. Sourced from over 700 million verified LinkedIn profiles, this dataset includes actionable insights and verified contact details for teachers, school administrators, university leaders, and other decision-makers. Whether your goal is to collaborate with educational institutions, market innovative solutions, or recruit top talent, Success.ai ensures your efforts are supported by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Education Industry Data? 1. Comprehensive Professional Profiles Access verified LinkedIn profiles of teachers, school principals, university administrators, curriculum developers, and education consultants. AI-validated profiles ensure 99% accuracy, reducing bounce rates and enabling effective communication. 2. Global Coverage Across Education Sectors Includes professionals from public schools, private institutions, higher education, and educational NGOs. Covers markets across North America, Europe, APAC, South America, and Africa for a truly global reach. 3. Continuously Updated Dataset Real-time updates reflect changes in roles, organizations, and industry trends, ensuring your outreach remains relevant and effective. 4. Tailored for Educational Insights Enriched profiles include work histories, academic expertise, subject specializations, and leadership roles for a deeper understanding of the education sector.

Data Highlights: 700M+ Verified LinkedIn Profiles: Access a global network of education professionals. 100M+ Work Emails: Direct communication with teachers, administrators, and decision-makers. Enriched Professional Histories: Gain insights into career trajectories, institutional affiliations, and areas of expertise. Industry-Specific Segmentation: Target professionals in K-12 education, higher education, vocational training, and educational technology.

Key Features of the Dataset: 1. Education Sector Profiles Identify and connect with teachers, professors, academic deans, school counselors, and education technologists. Engage with individuals shaping curricula, institutional policies, and student success initiatives. 2. Detailed Institutional Insights Leverage data on school sizes, student demographics, geographic locations, and areas of focus. Tailor outreach to align with institutional goals and challenges. 3. Advanced Filters for Precision Targeting Refine searches by region, subject specialty, institution type, or leadership role. Customize campaigns to address specific needs, such as professional development or technology adoption. 4. AI-Driven Enrichment Enhanced datasets include actionable details for personalized messaging and targeted engagement. Highlight educational milestones, professional certifications, and key achievements.

Strategic Use Cases: 1. Product Marketing and Outreach Promote educational technology, learning platforms, or training resources to teachers and administrators. Engage with decision-makers driving procurement and curriculum development. 2. Collaboration and Partnerships Identify institutions for collaborations on research, workshops, or pilot programs. Build relationships with educators and administrators passionate about innovative teaching methods. 3. Talent Acquisition and Recruitment Target HR professionals and academic leaders seeking faculty, administrative staff, or educational consultants. Support hiring efforts for institutions looking to attract top talent in the education sector. 4. Market Research and Strategy Analyze trends in education systems, curriculum development, and technology integration to inform business decisions. Use insights to adapt products and services to evolving educational needs.

Why Choose Success.ai? 1. Best Price Guarantee Access industry-leading Education Industry Data at unmatched pricing for cost-effective campaigns and strategies. 2. Seamless Integration Easily integrate verified data into CRMs, recruitment platforms, or marketing systems using downloadable formats or APIs. 3. AI-Validated Accuracy Depend on 99% accurate data to reduce wasted outreach and maximize engagement rates. 4. Customizable Solutions Tailor datasets to specific educational fields, geographic regions, or institutional types to meet your objectives.

Strategic APIs for Enhanced Campaigns: 1. Data Enrichment API Enrich existing records with verified education professional profiles to enhance engagement and targeting. 2. Lead Generation API Automate lead generation for a consistent pipeline of qualified professionals in the education sector. Success.ai’s Education Industry Data enables you to connect with educators, administrators, and decision-makers transforming global...

The Accounting Software for Small Businesses market has emerged as a crucial asset in the management and financial health of enterprises, facilitating streamlined accounting processes and providing vital insights that drive decision-making. With an increasing number of small businesses recognizing the value of preci

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 15 percent and 11 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 five. 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 77 thousand U.S. dollars, while in Germany, it totaled above 54 thousand U.S. dollars. The programming languages associated with the highest salaries worldwide in 2024 were Clojure and Erlang.

According to a 2023 survey conducted in the United States, ** percent of responding consumers said they purchased from the brand more frequently because they participated in a free loyalty program. Additionally, ** percent of U.S. consumers in a free loyalty program noted that they spent more time with the brand due to their participation in the program. A similar share (** percent) was recorded among consumers in a paid loyalty program.

Modern research is increasingly data-driven and reliant on bioinformatics software. Publication is a common way of introducing new software, but not all bioinformatics tools get published. Giving there are competing tools, it is important not merely to find the appropriate software, but have a metric for judging its usefulness. Journal's impact factor has been shown to be a poor predictor of software popularity; consequently, focusing on publications in high-impact journals limits user's choices in finding useful bioinformatics tools. Free and open source software repositories on popular code sharing platforms such as GitHub provide another venue to follow the latest bioinformatics trends. The open source component of GitHub allows users to bookmark and copy repositories that are most useful to them. This Perspective aims to demonstrate the utility of GitHub “stars,” “watchers,” and “forks” (GitHub statistics) as a measure of software impact. We compiled lists of impactful bioinformatics software and analyzed commonly used impact metrics and GitHub statistics of 50 genomics-oriented bioinformatics tools. We present examples of community-selected best bioinformatics resources and show that GitHub statistics are distinct from the journal's impact factor (JIF), citation counts, and alternative metrics (Altmetrics, CiteScore) in capturing the level of community attention. We suggest the use of GitHub statistics as an unbiased measure of the usability of bioinformatics software complementing the traditional impact metrics.

The most popular programming language used in the past 12 months by software developers worldwide is JavaScript as of 2024, according to 61 percent of the software developers surveyed. This is followed by Python at 57 percent of the respondents surveyed.

About this dataset

Context

The dataset tabulates the population of Round Top by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Round Top. The dataset can be utilized to understand the population distribution of Round Top by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Round Top. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Round Top.

Key observations

Largest age group (population): Male # 25-29 years (15) | Female # 50-54 years (13). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Round Top population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Round Top is shown in the following column.
• Population (Female): The female population in the Round Top is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Round Top for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Round Top Population by Gender. You can refer the same here

The artificial intelligence (AI) software industry is poised to dominate the Software-as-a-Service (SaaS) market by 2025, with an estimated *** million customers worldwide. This surge in AI adoption reflects the growing importance of intelligent technologies across various sectors, as businesses seek to leverage data-driven insights and automation to gain a competitive edge. Cloud adoption drives SaaS growth The rise of AI in SaaS is closely tied to the broader trend of cloud adoption. As of 2024, ** percent of enterprises have deployed hybrid cloud solutions, combining the benefits of public and private clouds. This shift towards flexible cloud infrastructure provides an ideal foundation for AI-powered SaaS applications, enabling businesses to scale their AI capabilities efficiently. The increasing popularity of public cloud services, with ** percent of enterprises adopting AWS, further supports the growth of AI and other SaaS offerings. Investment in cloud and SaaS continues to climb Organizations are demonstrating their commitment to cloud-based technologies through significant financial investments. In 2025, approximately ** percent of enterprises are expected to spend between *** million and *** million U.S. dollars annually on public cloud services. This substantial investment extends to SaaS industries, with financial services and AI software leading in total funding at nearly ** billion U.S. dollars each. The analytics software industry, closely related to AI, has secured 30 billion U.S. dollars in funding, underscoring the market's confidence in data-driven SaaS solutions.

This dataset provides the grade point distribution in the best five subjects related to admission to local undergraduate degree programmes for all candidates - Core subjects at 3322 or better, with one elective subject at level 2+. Male and female statistics are also provided. Please refer to the Examination Statistics for more information

About this Dataset

Data & Sons recently completed our analysis of top tier economics journal publications from 2014 to 2017 and is pleased to announce the world’s best Economics Departments based on faculty research productivity. We found the best Economics Departments in 2018. The complete list of departments publication totals and all economics publications in the top five economics journals are included. Methodology and Top 20 ranking are available in Documents.

Category

Social Sciences

Keywords

best economics department,best economics departments

Row Count

1647

Price

Free

Quick Stats is the National Agricultural Statistics Service's (NASS) online, self-service tool to access complete results from the 1997, 2002, 2007, and 2012 Censuses of Agriculture as well as the best source of NASS survey published estimates. The census collects data on all commodities produced on U.S. farms and ranches, as well as detailed information on expenses, income, and operator characteristics. The surveys that NASS conducts collect information on virtually every facet of U.S. agricultural production.

The Disk Partition Manager Software market is a crucial segment within the broader software industry, providing users with essential tools to manage disk partitions effectively. This software is widely utilized by IT professionals, system administrators, and individual users around the globe, allowing them to create

Introduction: In an age of increasingly face-to-face, blended, and online Health Professions Education, students have more selections of where they will receive a degree. For an applicant, oftentimes, the first step is to learn more about a program through its website. Websites allow programs to convey their unique voice and to share their mission and values with others, such as applicants, researchers, and academics. Additionally, as the number of Health Professions Education programs rapidly grows, websites can share the priorities of these programs. Methods: In this study, we conducted a website review of 158 Health Professions Education websites to explore their geographical distributions, missions, educational concentrations, and various programmatic components. Results: We compiled this information and synthesized pertinent aspects, such as program similarities and differences, or highlighted the omission of critical data. Conclusion: Given that websites are often the first point of contact for prospective applicants, curious collaborators, and potential faculty, the digital image of HPE programs matters. We believe our findings demonstrate opportunities for growth within institutions and assist the field in identifying the priorities of HPE programs. As programs begin to shape their websites with more intentionality, they can reflect their relative divergence/convergence compared to other programs as they see fit and, therefore, attract individuals to best match this identity. Periodic reviews of the breadth of programs, such as those undergone here, are necessary to capture diversifying goals, and serve to help advance the field of Health Professions Education as a whole. Methods Our team deduced that most HPE programs would have a website, and that this would serve as a representation of how individuals within the program choose to view themselves and hope to be viewed by others. Further, our team determined that these websites would be an efficient means of collecting programmatic information for the purposes of learning more about program growth, diversity, and values. We conducted the website review from August 2021 to April 2022 using a list of worldwide Health Professions Education programs, which was acquired from the Foundation of Advancement of International Medical Education and Research’s (FAIMER’s) website. FAIMER was chosen as the origin source of programs studied due to its use in another published study evaluating HPE programs. Each master's degree in HPE offered by a university was counted separately, allowing us to note the differences in course and time requirements across all programs. Only HPE master's programs were selected for this study. Certificate and Ph.D. programs were excluded. Next, we developed a data extraction tool. Categories were jointly identified for data collection by three of our authors (JS, SW, and HM). JS, SW, and HW worked independently through a set of three HPE programs, obtaining the data for our selected categories. Afterward, we cross-checked each other's work for verification purposes. For example, if JS obtained the information, SW or HM, who were blinded to JS’s findings, would independently find the answers to the same questions/ topics. This was performed until an agreement between pre and post-review information was above 95%. There was no discovered information that was not agreed upon after discussion. Once 100% agreement was reached with this method, the total number of HPE programs analyzed was split between JS and SW, and the raw data was obtained for the same categories. This data then underwent a review by the other two researchers to ensure high accuracy. This review consisted of information verification on individual program websites where it was originally obtained. For example, if JS found the information about a program, SW and HM (now not blinded) would both have to independently find the same information. Any identified discrepancies were rectified through discussion, and three-way agreement was mandatory for the team to move on to the next program.

The AI Video Analytics Software market has been experiencing significant growth as industries leverage advanced technologies to enhance their operational efficiency, security, and customer insights. As of 2023, the market size has reached approximately $3.4 billion, reflecting a robust compound annual growth rate (C

About this dataset

Context

The dataset tabulates the population of Good Hope by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Good Hope. The dataset can be utilized to understand the population distribution of Good Hope by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Good Hope. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Good Hope.

Key observations

Largest age group (population): Male # 65-69 years (25) | Female # 30-34 years (38). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Good Hope population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Good Hope is shown in the following column.
• Population (Female): The female population in the Good Hope is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Good Hope for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Good Hope Population by Gender. You can refer the same here