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.

This dataset provides Census 2021 estimates for National Statistics Socio-economic Classification (NS-SeC) by sex in Northern Ireland. The estimates are as at census day, 21 March 2021.

The census collected information on the usually resident population of Northern Ireland on census day (21 March 2021). Initial contact letters or questionnaire packs were delivered to every household and communal establishment, and residents were asked to complete online or return the questionnaire with information as correct on census day. Special arrangements were made to enumerate special groups such as students, members of the Travellers Community, HM Forces personnel etc. The Census Coverage Survey (an independent doorstep survey) followed between 12 May and 29 June 2021 and was used to adjust the census counts for under-enumeration.

The quality assurance report can be found here

According to an online survey conducted in February 2025 in the United States, ********* of LinkedIn users held a bachelor degree or equivalent. Additionally, ** percent of LinkedIn users in the U.S. held a masters degree or equivalent.

This dataset provides Census 2021 estimates that classify usual residents aged 3 and over in Northern Ireland by main language.

The census collected information on the usually resident population of Northern Ireland on census day (21 March 2021). Initial contact letters or questionnaire packs were delivered to every household and communal establishment, and residents were asked to complete online or return the questionnaire with information as correct on census day. Special arrangements were made to enumerate special groups such as students, members of the Travellers Community, HM Forces personnel etc. The Census Coverage Survey (an independent doorstep survey) followed between 12 May and 29 June 2021 and was used to adjust the census counts for under-enumeration.

This table reports the categories for which there are 10 or more usual residents. Where there are fewer than 10 usual residents for any category, these have been reported in a residual group which may or may not contain 10 or more usual residents in total.

Main language is reported as provided by respondents; those who stated 'Chinese' are recorded as 'Chinese (not otherwise specified)'. If a specific Chinese language has been stated, it is recorded separately.

Quality assurance report can be found here

Data were collected through an online survey and processed to create 95% CI using the BCA bootstrap confidence interval algorithm in MS EXCEL. Construction of confidence interval in MS EXCEL using the BCA bootstrap confidence interval algorithm is earlier not presented in any studies. The macro capabilities of MS EXCEL was utilized for the purpose stated.

In Montserrat, the distribution of stores across different platforms presents a dynamic picture of the market.

The pie chart showcases the distribution of app/software spending by store category in Montserrat, providing insights into how eCommerce stores allocate their resources on the app or software they utilize. Among the store categories, Apparel exhibits the highest spending, with a total expenditure of $1.00K units representing 100.00% of the overall spending. Following closely behind is Gifts & Special Events with a spend of $0.00 units, comprising <0.01% of the total. Sports also contributes significantly with a spend of $0.00 units, accounting for <0.01% of the overall app/software spending. This data sheds light on the investment patterns of eCommerce stores within each category, reflecting their priorities and resource allocation towards app or software solutions.

1. Provides public access to real-time instantatenous water level collected at over 1800 active locations in Canada. These data are collected under a national program jointly administered under federal-provincial and federal-territorial cost-sharing agreements; 2. Provides public access to archived daily water level for stations of interest using search criteria. These data include: daily and monthly mean, max and min of water levels. For some sites, annual peaks and extremes are also recorded. Archived water level data are disseminated online; 3. Provides public access to a MS Access database file containing archived daily water level that users can download to their desktop. These data include: daily and monthly mean, max and min of water level. For some sites, annual peaks and extremes are also recorded. MS Access file is updated quarterly; 4. Provides public access to water level statistics for stations of interest using search criteria.

This dataset provides summary statistics for case-detection and revenue generated from a TB social-enterprise model in Pakistan. This data was generated by collating statistics from the project partners HMIS that is available online. Summary statistics were complied using MS Excel.

Dataset

This dataset was created by swdmop

Released under GPL 2

Contents

This dataset provides Census 2021 estimates that classify usual residents in Northern Ireland by single year of age, and by sex. The estimates are as at census day, 21 March 2021.

The census collected information on the usually resident population of Northern Ireland on census day (21 March 2021). Initial contact letters or questionnaire packs were delivered to every household and communal establishment, and residents were asked to complete online or return the questionnaire with information as correct on census day. Special arrangements were made to enumerate special groups such as students, members of the Travellers Community, HM Forces personnel etc. The Census Coverage Survey (an independent doorstep survey) followed between 12 May and 29 June 2021 and was used to adjust the census counts for under-enumeration.

Abstract

High quality postgraduate training in science, technology, engineering and mathematics (STEM) related disciplines in sub-Saharan Africa (SSA) is important to strengthen research evidence to advance development and ensure countries achieve the Sustainable Development Goals (SDGs). Equally, participation of women in STEM careers is vital, to ensure that countries develop economies that work for all their citizens. However, women and girls remain underrepresented in STEM due to gender stereotyping, lack of visible role models, and unsupportive policies and work environments. Therefore, there is a need to consolidate information on participation and experiences of women in STEM related postgraduate training and careers in SSA to enhance their contribution to realizing the SDGs. The primary objective of this study is to examine the participation and experiences of women in postgraduate training, and their subsequent recruitment, retention and progression in STEM careers in East Africa. A secondary objective is to establish the gender gaps in training and career engagement in selected STEM related academic disciplines in East Africa. The descriptive study will employ a mixed methods approach, including a scoping review, qualitative interviews, and quantitative analysis of secondary data. We will synthesize results to inform the development of an effective gendered approach and framework to improve participation and experiences of women in STEM training and career engagements in SSA. We will conduct the study over a period of five years.

Geographic coverage

Regional coverage (East Africa Region)

Analysis unit

Individual Women in STEM

Universe

Qualitative data: Women in Science Technology Engineering and Mathematics (STEM) in postgraduate training and career Quantitative data: Postgraduate students, faculty, reseachers and supervisors (both men and women) in STEM in Inter-University Council for East Africa (IUCEA) member Universitiies

Sampling procedure

The study utilized a purposive sampling technique and targeted all universities that offered doctoral programs in applied sciences, technology, engineering, and mathematics. At the time, only 23 of the 74 universities in Kenya—equivalent to 30%—offered doctoral degrees in STEM. It was assumed that a similar or lower percentage would be found in the other five countries, namely Uganda, Tanzania, Rwanda, Burundi, and South Sudan.

Purposive sampling was used to recruit participants from purposively selected universities and national higher education commissions and agencies for the study. In universities, all students enrolled in doctoral programs in STEM were considered. Additionally, female and male students' lecturers, supervisors, mentors, and other faculty members and researchers in the identified institutions were also considered for participation in the study.

Purposive sampling of doctoral students, faculty, and early career researchers (post-doctoral fellows within the first six years since receiving their PhD) was conducted using the following inclusion criteria:

Inclusion criteria i. Worked in a STEM field/discipline ii. Enrolled in a doctoral program within a STEM field iii. Early career researchers in a STEM field in research organizations iv. Faculty in a STEM field at a university

Additionally, registrars, postgraduate training coordinators, heads of departments, and officials from national agencies and ministries related to postgraduate training and research were purposively selected from all the identified universities to provide input on existing policies, guidelines, and enrollment data. For each of the mentioned groups, 7-12 interviews were conducted, totaling 60 interviews.

Sampling deviation

Qualitative For the Key informant interviews one participant was interviewed from the engineers board despite the scope being Inter-University Council for East Africa (IUCEA) member Universities.

Quantitative The online survey was completed by some researchers not working/teaching in IUCEA member universities

Mode of data collection

Other [oth]

Research instrument

Quantitative data collection A. Online Survey This was carried out through an online survey questionnaire that was circulated via email and other digital platforms such as WhatsApp. The questionnaire had various parts: Part A - Participants characteristics This section mainly collected demographic details such as age, gender, nationality, residence, marital status, income, highest level of education completed, year of study, supervision and mentoship relationship, field of study in STEM (Science, Technology, Enginnering and Mathematics), mode of funding of postgraduate degree,

Part B - Status of Gender equality This section collected information on students enrollment and graduation in masters and PhD in STEM looking at gender distribution,

Part C - Factors that contribute to participation of women in STEM This section collected information on the factors or situations encountered while pursuing career in STEM in your specific discipline

Part D - Strategies for Optimizing Women's Engagement in STEM This section collected information on the strategies can maximize engagement of women in STEM training PhD level and subsequent careers

Part E - Effect of the COVID-19 pandemic on women's progression In this section collected information on COVID-19 pandemic affect on research progress or deadline for submission of thesis, COVID-19 pandemic affect on current research funding, COVID-19 pandemic caused researchers to work from home, working from affected progress in studies, any direct responsibilities caring for children, number of children being taken care of, change of domestic work responsibilities since the COVID-19 outbreak, change of domestic work responsibilities since the COVID-19 outbreak on studies, COVID-19 pandemic affect on access to these research tools which inlude: Computer or laptop, Reliable Internet, Assistive Technology, Laboratory equipment, University Library, Archives/special collections and Access to patients/research participants. It als collected information on: any benefits to COVID-19 pandemic for your work, some ways one thinks their supervisor or line manager could support or help one manage the impacts of COVID-19 on studies

The questionnaire was developed in English and was latertranslated into French to accommodate the French speaking countries i.e Burundi and Rwanda. The French questionnaire was backtlanslated to English to ensure the questions still maintained their original meaning. This work was done by an external consultant and the French questionnaires were reviewed by the research assistant from Burundi and tested among postgraduate students in Light University.

All questionnares and modules are provided as external resources.

Cleaning operations

Qualitative The data was collected through qualitative interviews (In-depth interviews) and focus group discussions. They were audio recorded and the recordings were transcribed on Ms Ofiice.The transcript were subjected to data quality checks and the clean transcripts were anonyzed for data protection.

QUANTITATIVE Secondary data The data was collected from the five countries in an Ms Excel designed data abstraction sheet. The data abstraction sheet helped the universities administrators and rergistrars to directly enter the data only in the required field and for the defined or specific variables. For the dataset that was in hardcopy format the data entry was also done using the data abstraction sheets. The data sets were subjected to data quality checks for data quality. We used a standard template to ensure data editing took place during data entry.

Online survey Data entry was in form of responding to the survey. Data editing was done while cleaning the data.

Response rate

Quantitaive The online survey link was circulated using contacts within universities and research institutions in East Africa via email and social media platforms such as WhatApp hence it is impossible to track those who received the survey and hence it is not possible t calculate the survey response rate.

Sampling error estimates

NA

This dataset provides Census 2021 estimates that classify usual residents in Northern Ireland by ethnic group.

The census collected information on the usually resident population of Northern Ireland on census day (21 March 2021). Initial contact letters or questionnaire packs were delivered to every household and communal establishment, and residents were asked to complete online or return the questionnaire with information as correct on census day. Special arrangements were made to enumerate special groups such as students, members of the Travellers Community, HM Forces personnel etc. The Census Coverage Survey (an independent doorstep survey) followed between 12 May and 29 June 2021 and was used to adjust the census counts for under-enumeration.

This table reports the categories for which there are 10 or more usual residents. Where there are fewer than 10 usual residents for any category, these have been reported in a residual group which may or may not contain 10 or more usual residents in total.

All ethnic groups are classified within one of the five groups: White, Asian, Black, Mixed, and Other. 'Irish Traveller' is included in 'Other'; this is changed from Census 2011 when 'Irish Traveller' was included in 'White'.

Quality assurance report can be found here

This chart offers an insightful look at the store count by category in Montserrat. Leading the way is Apparel, with 1 stores, which is 33.33% of the total stores in the region. Next is Gifts & Special Events, contributing 1 stores, or 33.33% of the region's total. Sports also has a notable presence, with 1 stores, making up 33.33% of the store count in Montserrat. This breakdown provides a clear picture of the diverse retail landscape in Montserrat, showcasing the variety and scale of stores across different categories.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in Northern Ireland by their highest level of qualification, and by broad age bands. The estimates are as at census day, 21 March 2021.

The census collected information on the usually resident population of Northern Ireland on census day (21 March 2021). Initial contact letters or questionnaire packs were delivered to every household and communal establishment, and residents were asked to complete online or return the questionnaire with information as correct on census day. Special arrangements were made to enumerate special groups such as students, members of the Travellers Community, HM Forces personnel etc. The Census Coverage Survey (an independent doorstep survey) followed between 12 May and 29 June 2021 and was used to adjust the census counts for under-enumeration.

'Age' is age at last birthday.

Qualification

• No qualifications: No academic or professional qualifications.
• Level 1: 1-4 GCSEs, O Levels, CSEs (any grades); NVQ level 1; or equivalent qualifications.
• Level 2: 5+ GCSEs (Grades A*-C, 9-4), O Levels (Passes), CSEs (Grade 1); 1 A Level, 2-3 AS Levels; NVQ level 2, BTEC General, City and Guilds Craft; or equivalent qualifications. Apprenticeship.
• Level 3: 2+ A Levels, 4+ AS Levels; NVQ Level 3, BTEC National, OND or ONC, City and Guilds Advanced Craft; or equivalent qualifications.
• Level 4+: Degree (for example BA, BSc), foundation degree, HND, HNC, NVQ Level 4-5, professional qualifications (for example teaching, nursing), or equivalent qualifications.
• Other: Any other qualifications, equivalent unknown.

Quality assurance report can be found here