Research and development's adoption of data science and machine learning technologies is the fastest among enterprise departments, with around ** percent of respondents from R&D saying that they already deployed data science and machine learning in their work, as of 2019. The finance department lags behind in this respect.

Historical Dataset of Newark Sch Of Data Science And Information Technology is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2022-2023),Total Classroom Teachers Trends Over Years (2022-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio 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),Free Lunch Eligibility Comparison Over Years (2022-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2022-2023),Math Proficiency Comparison Over Years (2022-2023),Overall School Rank Trends Over Years (2022-2023)

As of 2019, most Italian large companies had adopted a decentralized approach to structure their data science teams. Over a third of the companies had scattered their data scientists across multiple business units, while another ** percent only had a few data scientists, mostly working within the IT department.

Context

The Bureau of Labor Statistics (BLS) is a unit of the United States Department of Labor. It is the principal fact-finding agency for the U.S. government in the broad field of labor economics and statistics and serves as a principal agency of the U.S. Federal Statistical System. The BLS is a governmental statistical agency that collects, processes, analyzes, and disseminates essential statistical data to the American public, the U.S. Congress, other Federal agencies, State and local governments, business, and labor representatives. Source: https://en.wikipedia.org/wiki/Bureau_of_Labor_Statistics

Content

Bureau of Labor Statistics including CPI (inflation), employment, unemployment, and wage data.

Update Frequency: Monthly

Querying BigQuery Tables

Fork this kernel to get started.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:bls

https://cloud.google.com/bigquery/public-data/bureau-of-labor-statistics

Dataset Source: http://www.bls.gov/data/

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Clark Young from Unsplash.

Inspiration

What is the average annual inflation across all US Cities? What was the monthly unemployment rate (U3) in 2016? What are the top 10 hourly-waged types of work in Pittsburgh, PA for 2016?

This dataset contains Department for Innovation and Skills annual report data from July 2014 (where available) onwards. The data displays: the quantity and nature of complaints, money spent on consultants and contractors, number of executives employed, Work Health and Safety performance and reports of fraud

Data Science Platform Market size was valued at USD 101.34 Billion in 2024 and is projected to reach USD 739.07 Billion by 2032 growing at a CAGR of 31.10% from 2026 to 2032.

Global Data Science Platform Market Drivers

AI and Machine Learning Integration: As AI and machine learning technologies become more widely adopted, demand for data science platforms grows. The United States Bureau of Labour Statistics predicts a 36% increase in data scientist jobs between 2021 and 2031, underlining the growing need for advanced platforms to develop and scale intelligent applications.

Demand for Business Intelligence and Analytics: As firms rely more on data-driven decision-making, there is a greater need for advanced analytics and business intelligence capabilities. Data science platforms provide critical tools for these roles, resulting in market growth, as evidenced by a predicted CAGR of 27.6% from 2022 to 2027.

This dataset contains Department for Industry, Innovation and Science annual report data from July 2022 (where available) onwards. The data displays: the quantity and nature of complaints, money spent on consultants and contractors, number of executives employed, Work Health and Safety performance and reports of fraud.

Students in statistics, data science, analytics, and related fields study the theory and methodology of data-related topics. Some, but not all, are exposed to experiential learning courses that cover essential parts of the life cycle of practical problem-solving. Experiential learning enables students to convert real-world issues into solvable technical questions and effectively communicate their findings to clients. We describe several experiential learning course designs in statistics, data science, and analytics curricula. We present findings from interviews with faculty from the U.S., Europe, and the Middle East and surveys of former students. We observe that courses featuring live projects and coaching by experienced faculty have a high career impact, as reported by former participants. However, such courses are labor-intensive for both instructors and students. We give estimates of the required effort to deliver courses with live projects and the perceived benefits and tradeoffs of such courses. Overall, we conclude that courses offering live-project experiences, despite being more time-consuming than traditional courses, offer significant benefits for students regarding career impact and skill development, making them worthwhile investments. Supplementary materials for this article are available online.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just * percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

Includes data files and supplemental information. Supplemental information includes a reproducible RMarkdown file, an Excel sheet with metadata, and complete webpage files. Please note that CCD nonfiscal documentation files have been downloaded manually.From the Common Core of Data website:The Common Core of Data (CCD) is the Department of Education's primary database on public elementary and secondary education in the United States. CCD is a comprehensive, annual, national database of all public elementary and secondary schools and school districts.Information on the Common Core of Data (CCD)The primary purpose of the CCD is to provide basic information on public elementary and secondary schools, local education agencies (LEAs), and state education agencies (SEAs) for each state, the District of Columbia, and the outlying territories with a U.S. relationship. CCD is composed of two components: Nonfiscal CCD and Fiscal CCD.

The statistical report, international indicators, and research reports for the Gender Mainstreaming Project administered by the National Science and Technology Commission.

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

This dataset is related to the following paper: van Borkulo, S. P., Chytas, C., Drijvers, P., Barendsen, E., & Tolboom, J. (2023). Spreadsheets in Secondary School Statistics Education: Using Authentic Data for Computational Thinking. Digital Experiences in Mathematics Education. https://doi.org/10.1007/s40751-023-00126-5

1. In order to understand and grasp the development of science and technology in our country, and to establish scientific and technological indicators for comparison with other countries as a reference for national science and technology development policies, the National Science and Technology Committee conducts the "National R&D Status Survey" regularly each year. This dataset is one of the statistical results of the "National R&D Status Survey." 2. "..." means no numerical value.

The National Sample Survey contains a variety of socio-economic data for India and is collected by the Ministry of Statistics and Programme Implementation for planning and policy formulation. The National Sample Survey Office (NSSO) conducts the Socio-Economic (SE) Surveys, nationwide sample surveys relating to various socio-economic topics. Surveys are conducted in the form of Rounds, each Round being normally of one-year duration and occasionally for a period of six months.The National Sample Survey website provides further information about the survey, coverages and methodology.

Graph and download economic data for All Employees, Professional, Scientific, and Technical Services (CES6054000001) from Jan 1990 to Jul 2025 about professional, establishment survey, business, services, employment, and USA.

These data sets contain the published Bureau of Labor Statistics (BLS) price data for the 4-digit (PR4BLS) and the 5-digit (PR5BLS) SIC classifications. The data are taken from a tape purchased from the BLS; see the BLS documentation for clearer definitions of the variables. The data covers the period 1957 to August, 1976.

Experiments with faster dissemination of research began in the 1960s, and in the 1990s first preprint servers emerged and became widely used in Physical Sciences and Economics. Since 2010, more than 30 new preprint servers have emerged and the number of deposited preprints has grown exponentially, with numerous journals now supporting posting of preprints and accepting preprints as submissions for journal peer review and publication. Research on preprints is, however, still scarce.

The goals of this project are: 1) Study preprint policies, submission requirements and addressing of transparency in reporting and research integrity topics of all know preprint servers that allow deposit of preprints to researchers regardless of their institutional affiliation or funding.
2) Study comments deposited on preprint servers’ platforms and social media and their relation to peer review and information exchange. 3) Study differences between preprint version(s) and version of record. 4) Living review of manuscript changes

Team Members (by first name alphabetical order):

Ana Jerončić,1 Gerben ter Riet,2,3 IJsbrand Jan Aalbersberg,4 John P.A. Ioannidis,5-9 Joseph Costello,10 Juan Pablo Alperin,11,12 Lauren A. Maggio,10 Lex Bouter,13,14 Mario Malički,5 Steve Goodman5-7

1 Department of Research in Biomedicine and Health, University of Split School of Medicine, Split, Croatia 2 Urban Vitality Centre of Expertise, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands 3 Amsterdam UMC, University of Amsterdam, Department of Cardiology, Amsterdam, The Netherlands 4 Elsevier, Amsterdam, The Netherlands 5 Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA 6 Department of Medicine, Stanford University School of Medicine, Stanford, California, USA 7 Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California, USA 8 Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, USA 9 Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, USA 10 Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA 11 Scholarly Communications Lab, Simon Fraser University, Vancouver, British Columbia, Canada 12 School of Publishing, Simon Fraser University, Vancouver, British Columbia, Canada 13 Department of Philosophy, Faculty of Humanities, Vrije Universiteit, Amsterdam, The Netherlands 14 Amsterdam UMC, Vrije Universiteit, Department of Epidemiology and Statistics, Amsterdam, The Netherlands

This data collection contains information from the first wave of High School and Beyond (HSB), a longitudinal study of American youth conducted by the National Opinion Research Center on behalf of the National Center for Education Statistics (NCES). Data were collected from 58,270 high school students (28,240 seniors and 30,030 sophomores) and 1,015 secondary schools in the spring of 1980. Many items overlap with the NCES's NATIONAL LONGITUDINAL STUDY OF THE CLASS OF 1972 (ICPSR 8085). The HSB study's data are contained in eight files. Part 1 (School Data) contains data from questionnaires completed by high school principals about various school attributes and programs. Part 2 (Student Data) contains data from surveys administered to students. Included are questionnaire responses on family and religious background, perceptions of self and others, personal values, extracurricular activities, type of high school program, and educational expectations and aspirations. Also supplied are scores on a battery of cognitive tests including vocabulary, reading, mathematics, science, writing, civics, spatial orientation, and visualization. To gather the data in Part 3 (Parent Data), a subsample of the seniors and sophomores surveyed in HSB was drawn, and questionnaires were administered to one parent of each of 3,367 sophomores and of 3,197 seniors. The questionnaires contain a number of items in common with the student questionnaires, and there are a number of items in common between the parent-of-sophomore and the parent-of-senior questionnaires. This is a revised file from the one originally released in Autumn 1981, and it includes 22 new analytically constructed variables imputed by NCES from the original survey data gathered from parents. The new data are concerned primarily with the areas of family income, liabilities, and assets. Other data in the file concentrate on financing of post-secondary education, including numerous parent opinions and projections concerning the educational future of the student, anticipated financial aid, student's plans after high school, expected ages for student's marriage and childbearing, estimated costs of post-secondary education, and government financial aid policies. Also supplied are data on family size, value of property and other assets, home financing, family income and debts, and the age, sex, marital, and employment status of parents, plus current income and expenses for the student. Part 4 (Language Data) provides information on each student who reported some non-English language experience, with data on past and current exposure to and use of languages. In Parts 5-6, there are responses from 14,103 teachers about 18,291 senior and sophomore students from 616 schools. Students were evaluated by an average of four different teachers who had the opportunity to express knowledge or opinions of HSB students whom they had taught during the 1979-1980 school year. Part 5 (Teacher Comment Data: Seniors) contains 67,053 records, and Part 6 (Teacher Comment Data: Sophomores) contains 76,560 records. Questions were asked regarding the teacher's opinions of their student's likelihood of attending college, popularity, and physical or emotional handicaps affecting school work. The sophomore file also contains questions on teacher characteristics, e.g., sex, ethnic origin, subjects taught, and time devoted to maintaining order. The data in Part 7 (Twins and Siblings Data) are from students in the HSB sample identified as twins, triplets, or other siblings. Of the 1,348 families included, 524 had twins or triplets only, 810 contained non-twin siblings only, and the remaining 14 contained both types of siblings. Finally, Part 8 (Friends Data) contained the first-, second-, and third-choice friends listed by each of the students in Part 2, along with identifying information allowing links between friendship pairs.