This dataset provides comprehensive information about various Data Science and Analytics master's programs offered in the United States. It includes details such as the program name, university name, annual tuition fees, program duration, location of the university, and additional information about the programs.

Column Descriptions:

• Subject Name: The name or field of study of the master's program, such as Data Science, Data Analytics, or Applied Biostatistics.

• University Name: The name of the university offering the master's program.

• Per Year Fees: The tuition fees for the program, usually given in euros per year. For some programs, the fees may be listed as "full" or "full-time," indicating a lump sum for the entire program or for full-time enrollment, respectively.

• About Program: A brief description or overview of the master's program, providing insights into its curriculum, focus areas, and any unique features.

• Program Duration: The duration of the master's program, typically expressed in years or months.

• University Location: The location of the university where the program is offered, including the city and state.

• Program Name: The official name of the master's program, often indicating its degree type (e.g., M.Sc. for Master of Science) and format (e.g., full-time, part-time, online).

A file that holds the master records for all online training courses nominated for reimbursement.

Dataset

This dataset was created by swdmop

Released under GPL 2

Contents

About Dataset

This dataset was created during the Programming Language Ecosystem project from TU Wien using the code inside the repository https://github.com/ValentinFutterer/UsageOfProgramminglanguages2011-2023?tab=readme-ov-file.

The centerpiece of this repository is the usage_of_programming_languages_2011-2023.csv. This csv file shows the popularity of programming languages over the last 12 years in yearly increments. The repository also contains graphs created with the dataset. To get an accurate estimate on the popularity of programming languages, this dataset was created using 3 vastly different sources.

About Data collection methodology

The dataset was created using the github repository above. As input data, three public datasets where used.

github_metadata

Taken from https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars/ by Peter Elmers. It is licensed under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/. It shows metadata information (no code) of all github repositories with more than 5 stars.

PYPL_survey_2004-2023

Taken from https://github.com/pypl/pypl.github.io/tree/master, put online by the user pcarbonn. It is licensed under CC BY 3.0 https://creativecommons.org/licenses/by/3.0/. It shows from 2004 to 2023 for each month the share of programming related google searches per language.

stack_overflow_developer_survey

Taken from https://insights.stackoverflow.com/survey. It is licensed under Open Data Commons Open Database License (ODbL) v1.0 https://opendatacommons.org/licenses/odbl/1-0/. It shows from 2011 to 2023 the results of the yearly stackoverflow developer survey.

All these datasets were downloaded on the 12.12.2023. The datasets are all in the github repository above

Description of the data

The dataset contains a column for the year and then many columns for the different languages, denoting their usage in percent. Additionally, vertical barcharts and piecharts for each year plus a line graph for each language over the whole timespan as png's are provided.

The languages that are going to be considered for the project can be seen here:

- Python

- C

- C++

- Java

- C#

- JavaScript

- PHP

- SQL

- Assembly

- Scratch

- Fortran

- Go

- Kotlin

- Delphi

- Swift

- Rust

- Ruby

- R

- COBOL

- F#

- Perl

- TypeScript

- Haskell

- Scala

License

This project is licensed under the Open Data Commons Open Database License (ODbL) v1.0 https://opendatacommons.org/licenses/odbl/1-0/ license.

TLDR: You are free to share, adapt, and create derivative works from this dataser as long as you attribute me, keep the database open (if you redistribute it), and continue to share-alike any adapted database under the ODbl.

Acknowledgments

Thanks go out to

- stackoverflow https://insights.stackoverflow.com/survey for providing the data from the yearly stackoverflow developer survey.

- the PYPL survey, https://github.com/pypl/pypl.github.io/tree/master for providing google search data.

- Peter Elmers, for crawling metadata on github repositories and providing the data https://www.kaggle.com/datasets/pelmers/github-repository-metadata-with-5-stars/.

Users can view summary reports and interact with the databset to obtain information regarding registered nonprofit organizations on a state and national level. Topics include: registered nonprofit organizations, public charities, and private foundations in the United States and per state. Background The National Center for Charitable Statistics, managed by the Urban Institute, provides data on the nonprofit sector in the United States. Topics include registered nonprofit organizations, public charities and private foundations in the United States. User Functionality Users can view summary reports regarding the number and type of registered nonprofit organizations, public charities and private foundations in the U.S. and individual states. In additi on to viewing reports, users can also interact with several online analysis tools to view data on specific types of nonprofits and view in-depth state profiles. Users can purchase the dataset, or specific variables within the dataset for further analysis. The dataset can be downloaded into dbase, SAS, or SPSS statistical software or Microsoft Excel. Data Notes Statistics are derived from the Internal Revenue Service Master File. Data are available from 1995-2010 and are available on a state and national level.

The COVID-19 data sets and associated Jupyter Hub notebooks are support for a manuscript describing how data science was shown to be effective in developing a transdisciplinary team and the production of novel outputs in part due to the common learning process of all team members being part of an online professional data science and analytics master’s degree program. This online curriculum helped the team members to find a common process that allowed them learn in common (Kläy, Zimmermann, & Schneider, 2015), transdisciplinary learning a key component of transdisciplinary teamwork (Yeung, 2015). Our team's Jupyter Hub files with complete coding and data set explanations are uploaded to document this teamwork and the outputs of the team.

OVERVIEW

1. Title of Dataset: Arthralgia in female Master weightlifters

2. Author Information

Name: Marianne Huebner Institution: Michigan State University Address: East Lansing, MI 48824

1. Period of data collection: 27 April – 20 May 2022

2. Geographic region of data collection: Online survey in USA with participants from 30 countries in IWF regions Africa, Asia, Europe, Oceania, PanAmerican

LIST OF FILES

Dataset: wlmeno_oa.csv Data dictionary: wlmeno_oa_meta.xlsx

METHODOLOGICAL INFORMATION

1. Description of methods used for collection/generation of data: The survey was distributed by the Master Committee of the International Weightlifting Federation (IWF) to the National Master Chairs. They then used email or social media to communicate the study to the women weightlifters. The Survey was available in four languages (English, German, French, Spanish), translated and tested by native speakers. In addition, the survey was advertised in weightlifting interest groups via Facebook and Instagram. The survey was administered online via Qualtrics (Provo, UT, USA).

2. Methods for processing the data: Data were downloaded from Qualtrics (Provo, UT, USA) to Excel and then pre-processed in the statistical software R v. 4.3.0. (https://www.r-project.org)

   Variable formats (numeric, character) were checked and transformed, as appropriate.

3. Quality-assurance procedures performed on the data: Exclusion criteria were younger than 30 years (n=1), currently pregnant (n=3). To account for the possibility of male participants missing responses to age of menstruation or prior pregnancies (n=22), were also excluded. Since the focus was on active weightlifters, missing best snatch or clean and jerk in the last 6 months (n=18) were also exclusion criteria. This resulted in an analysis data set of 868 females. Univariate distributions were evaluated numerically and graphically.

DATA-SPECIFIC INFORMATION

1.Number of variables: 51

2.Number of cases/rows: 868

3.Variable List: wlmeno_oa.xlsx

4.Missing data codes: empty cells

We estimated the savings in CO₂ emissions by a cohort of master’s students who studied fully online from their home countries, rather than traveling to the UK and living there while attending university.

Data come from International Civil Aviation Organization (ICAO) carbon emissions calculator https://www.icao.int/environmental-protection/CarbonOffset/Pages/default.aspx; and CO₂ and Greenhouse Gas Emissions https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions

This dataset contains counts of live births for California as a whole based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

ABSTRACT The health care model based on the Family Health Strategy, created in the early 1990s, encouraged changes in health education, highlighting the need to create lato and stricto sensu postgraduate courses aimed at empowering professionals that foster comprehensive health care. Periodic evaluations are carried out and encouraged by Capes/MEC in order to maintain the quality of postgraduate courses, but evaluations of recently-introduced professional master’s degree courses in family health remain scarce. Objectives To describe the academic profile, contribution, motivations and expectations of graduates of a Professional Master’s in Family Health. Method Cross-sectional and quantitative study to analyze the results of 102 questionnaires answered by graduates of the Professional Master’s Degree in Family Health of the Estácio de Sá University (RJ), who had concluded the course between 2007 and 2012. The instrument consisted of open-ended and closed-ended questions, sent by e-mail and made available online through the electronic platform Survey Monkey. The study evaluated age, gender, regional origin, academic background, as well as the contributions, expectations and motivations related to the course. Results The survey sample was formed predominantly by female graduates, aged over 30, from 13 Brazilian states and, mainly from Medicine and Nursing courses. The contribution of the master’s degree to the graduate’s professional life was evaluated as excellent by 77% of the interviewees. The expectations regarding the course were positively evaluated and the main reasons for seeking the qualification were scientific-technical improvement and personal satisfaction, rather than better salaries or job stability. Conclusion The course was evaluated positively by the graduates, having exceeded their expectations and satisfied the interests that led them to it, thus producing changes to their personal and professional life. A longitudinal analysis of the impact of the professional master’s degree in the career of graduates will require a sequence of similar studies, as has been stimulated by Capes/MEC in recent years.

The global massive open online course (MOOC) market size is calculated to advance at a CAGR of 32% through 2034, which is set to increase its market value from US$ 13.2 billion in 2024 to US$ 212.7 billion by the end of 2034.

Report Attribute	Detail
MOOC Market Size (2024E)	US$ 13.2 Billion
Projected Market Value (2034F)	US$ 212.7 Billion
Global Market Growth Rate (2024 to 2034)	32% CAGR
China Market Value (2034F)	US$ 23.3 Billion
Japan Market Growth Rate (2024 to 2034)	32.6% CAGR
North America Market Share (2024E)	23.9%
East Asia Market Value (2034F)	US$ 49.1 Billion
Key Companies Profiled	Alison; Coursera Inc; edX Inc; Federica.EU; FutureLearn; Instructure; Intellipaat; iverity; Jigsaw Academy; Kadenze.

Country Wise Insights

Attribute	United States
Market Value (2024E)	US$ 1.4 Billion
Growth Rate (2024 to 2034)	32.5% CAGR
Projected Value (2034F)	US$ 23.6 Billion

Attribute	China
Market Value (2024E)	US$ 1.5 Billion
Growth Rate (2024 to 2034)	32% CAGR
Projected Value (2034F)	US$ 23.3 Billion

Category-wise Insights

Attribute	xMOOC
Segment Value (2024E)	US$ 9.3 Billion
Growth Rate (2024 to 2034)	30.8% CAGR
Projected Value (2034F)	US$ 136.1 Billion

Attribute	Degree & Master Programs
Segment Value (2024E)	US$ 6.4 Billion
Growth Rate (2024 to 2034)	30.2% CAGR
Projected Value (2034F)	US$ 89.3 Billion

Descriptive statistics-aggregate data.

Table View of Master_OP_EXP - Budgets and Actuals from FY 2016, 2017, 2018, 2019, and FYTD 2020. This View is the data source for Expense Dashboards. Update Schedule: Once per Month.

Abstract The aim of this paper is to understand students’ experience of research methodology teaching, through a survey with students enrolled in the Master’s Degree Program in Information Management, Libraries and Archives at the Complutense University of Madrid. The analyzed themes included the students’ perception of collaborative work, influence of emotions, implications of research competencies at the professional level, and the role of supervisors. Twenty-six master’s students, both online and on-site, were surveyed among those enrolled in the academic years 2014-2015 and 2013-2014. Results show the need to foster collaborative work with individual work in a balanced way. Furthermore, emotions, especially positive ones, appear to intertwine heavily with the learning experience. It is more difficult to appreciate the implications of research competencies for the professional sphere because of differences in the professional context of all students involved. The activities that students perceive as more creative include discussions of one’s own work (especially with the supervisor) as well as discussions of other students’ work (attendance at Master Thesis Defenses). Finally, supervisors stand out as important figure during the learning of research methodology, as their area of expertise is particularly relevant.

The data relates to the paper that analyses the determinants or factors that best explain student research skills and success in the honours research report module during the COVID-19 pandemic in 2021. The data used have been gathered through an online survey created on the Qualtrics software package. The research questions were developed from demographic factors and subject knowledge including assignments to supervisor influence and other factors in terms of experience or belonging that played a role (see anonymous link at https://unisa.qualtrics.com/jfe/form/SV_86OZZOdyA5sBurY. An SMS was sent to all students of the 2021 module group to make them aware of the survey. They were under no obligation to complete it and all information was regarded as anonymous. We received 39 responses. The raw data from the survey was processed through the SPSS statistical, software package. The data file contains the demographics, frequencies, descriptives, and open questions processed.

The study reported in this paper employed the mixed methods approach comprising a quantitative and qualitative analysis. The quantitative and econometric analysis of the dependent variable, namely, the final marks for the research report and the independent variables that explain it. The results show significance in terms of the assignments and existing knowledge marks in terms of their bachelor's average mark. We extended the analysis to a qualitative and quantitative survey, which indicated that the mean statistical feedback was above average and therefore strongly agreed/agreed except for library use by the student. Students, therefore, need more guidance in terms of library use and the open questions showed a need for a research methods course in the future. Furthermore, supervision tends to be a significant determinant in all cases. It is also here where supervisors can use social media instruments such as WhatsApp and Facebook to inform students further. This study contributes as the first to investigate the preparation and research skills of students for master's and doctoral studies during the COVID-19 pandemic in an online environment.

The Core Based Statistical Areas boundaries were defined by OMB based on the 2010 Census, and the dataset was updated on August 09, 2019 from the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan and Micropolitan Statistical Areas are together termed Core Based Statistical Areas (CBSAs) and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core (urbanized area or urban cluster) of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urbanized areas of 50,000 or more population; and Micropolitan Statistical Areas, based on urban clusters of at least 10,000 population but less than 50,000 population. The CBSA boundaries are those defined by OMB based on the 2010 Census, published in 2013, and updated in 2018.

In this article, we explore the use of two published datasets for teaching a wide range of students about regression models, with a particular focus on interaction terms. The two datasets come from recent psychology studies on beliefs about poverty and welfare, and about the dynamics of groups projects. Both datasets (and their original research papers) are accessible to students, and because of their context, students can learn about data collection, measurement, and the use of statistics when studying complex social topics, while using the data to learn about regression analysis. We have used these data for a range of in-class activities, journal paper discussions, exams, and extended projects, at the undergraduate, master’s, and doctoral levels. Supplementary materials for this article are available online.

In 2020, about ** percent of aspirant students stated that affordability was their main driving factor behind choosing an institution for an online education course. Online education had been accepted widely in the recent years and was further accelerated by the Covid-19 pandemic. Higher education In the wake of the COVID-19 pandemic, the higher education ecosystem in India witnessed an unprecedented makeover. During this period, all of India’s institutions were forced to adapt to digital tools and technologies that brought about a complete rehaul not just to the pedagogic aspects of online teaching, but also to the experiential and administrative functions of higher education. The challenges that stemmed as a result of this affected not just students but also teachers and other facilitators working in academia, calling for more flexible and adaptable systems. Remote education Dring this period, higher educational institutions in India together with relevant industries joined forces to create programs and curricula guaranteeing an exchange of essential knowledge while focusing on building the student’s competencies and capabilities to match the needs of the industry. Some of the hallmarks of this innovative paradigm of education are built on mobile technology, internet coverage and high-speed internet and social media platforms that afford the student flexibility while learning anytime and anywhere. Another key feature is that the role of the teachers pivoted to that of facilitators and mentors in this modern era of education.

The TIGER/Line Shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System. The MAF/TIGER System represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line Shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGERweb REST Services allows users to integrate the Census Bureau's Topologically Integrated Geographic Encoding and Referencing database (TIGER) data into their own GIS or custom web-based applications.For a more detailed description of the areas listed or terms below, refer to TIGER/Line documentation or the Geographic Areas Reference Manual, (GARM).This REST service contains Combined New England City and Town Area (CNECTA), Combined Statistical Area (CSA), Metropolitan Division, Core Based Statistical Areas (CBSA), and New England City and Town Area (NECTA) boundaries.Combined New England City and Town Areas (CNECTAs) consist of two or more adjacent NECTAs that have significant employment interchanges. The NECTAs that combine to create a CNECTA retain separate identities within the larger combined statistical areas.

Combined Statistical Areas (CSAs) consist of two or more adjacent CBSAs that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSAs.

Metropolitan Divisions are smaller groupings of counties or equivalent entities within a metropolitan statistical area that contains a single core with 2.5 million inhabitants.

Core Based Statistical Area Codes (CBSA) are the metropolitan statistical areas, micropolitan statistical areas, NECTAs, metropolitan divisions, and NECTA divisions use a 5-character code. Each metropolitan statistical area must have one urbanized area of 50,000 or more inhabitants. Each micropolitan statistical area must have one urban cluster of 10,000 to 49,999 inhabitants.

New England City and Town Area (NECTA) Divisions are smaller groupings of cities and towns within a NECTA that contains a single core with 2.5 million inhabitants. A NECTA Division consists of a main city or town that represents an employment center, as well as adjacent cities and towns associated with the main city or town through commuting ties. Each NECTA Division must contain a total population of 100,000 or more.

Additional resources to obtain Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas are listed below.

Combined New England City and Town Area (CNECTA) Shapefile - https://www2.census.gov/geo/tiger/TIGER2020/CNECTA/

Combined Statistical Area (CSA) Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/CSA/

Metropolitan Division Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/METDIV/

Core Based Statistical Areas (CBSA) Shapefile – https://www2.census.gov/geo/tiger/TIGER2020/CBSA/

New England City and Town Area (NECTA) Shapefile- https://www2.census.gov/geo/tiger/TIGER2020/NECTA/.

According to the survey, ** percent of the software developers had bachelor degrees and just over ** percent had attained a Master's degree of some form as of 2024. Additionally, around *** percent had obtained some sort of professional degree.