Professional Certification Programs

A Database of Accreditations and Certifications in Professional Fields

By [source]

About this dataset

This dataset is brimming with knowledge on certifications and accreditations tailored to the work-seeking community. We are here to bring these resources closer so that job seekers can easily access resumes written by experienced professionals in fields like special education, foreign language teaching, technical training, and sport management. Every entry in this dataset comes with a unique identifier - allowing users to effortlessly browse certifications that reflect their employable qualities. With an array of magnified expert qualifications ranging from international organizations to renowned institutions, let this database be your go-to when optimizing your professional resume content!

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How to use the dataset

This dataset contains a list of certifications and accreditations related to professional fields, including special education, foreign language teaching, technical training, and sport management. It is a great resource for job seekers interested in finding resumes written by experienced professionals in these fields.

To use this dataset effectively, here are some steps you can take: - Examine the columns – ÿþ# and**ÿþ# – available within the dataset to get an understanding of what information is available for each certification or accreditation entry listed. - Read through the descriptions provided for each certification or accreditation entry to gain an understanding of each program’s specifics and relevance to your career field (if applicable). - Use the information gleaned from an individual entry’s description as well as other supporting data (like year of issue/release or last updated date) to determine which particular credentials would be most beneficial for furthering your career goals or job searches with professional and/or educational organizations relevant to your interests.
- Using this knowledge targeted towards you interests has been best covered through a resume writing service with qualifications in that field - ideally one that is familiar with trends related specifically to the applicable certification/accreditation program(s) associated with your desired position(s)

Research Ideas

• The dataset can be used to provide job seekers with personalized recommendations for qualified professionals that hold a specific certification or accreditation in their particular field of interest.
• It can be used to create an online interactive resource guide that showcases the different certifications and accreditations by using visualizations such as graphs, maps, and infographics.
• Employers can use this data to advertise job opportunities that require specialized certifications or accreditations not commonly found in their industry or local talent pool

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: batch_analysis_export.utf-16 (57).csv | Column name | Description | |:--------------|:--------------------------------------------------------------------| | ÿþ# | Unique identifier for each certification or accreditation (String). |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Online and Distance Education at Postsecondary Institutions, 2006-07 (PEQIS 16), is a study that is part of the Postsecondary Education Quick Information System (PEQIS) program; program data is available since 1997 at . PEQIS 16 (https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2009044) is a survey that collects data on the prevalence and delivery of distance education courses in the 2006-07 12-month academic year, including the number of courses and enrollment for online courses, hybrid/blended online courses, and all other distance education courses. The survey also collects information about numbers of degree or certificate programs designed to be completed entirely through distance education and the technologies used for the instructional delivery of credit-granting distance education courses. The study was conducted using paper and web surveys. The weighted response rate was 87 percent. Postsecondary institutions were sample for this study. Key statistics produced from PEQIS 16 relate to information on the prevalence, types, delivery, policies, and acquisition or development of distance education courses and programs.

Proportion of students who started in a college-level certificate program and graduated with a different educational qualification, within Canada, by demographic characteristics.

Proportion of students who started in a college-level certificate program and graduated with a different educational qualification, within the province or territory of first enrolment, by demographic characteristics.

This table contains 3226 series, with data for years 1992 - 2008 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Prince Edward Island; Nova Scotia; Newfoundland and Labrador ...), Program level (11 items: Total; program level; Trade/vocational and preparatory training certificate or diploma; Community college certificate or diploma and other community college level; Undergraduate level ...), Classification of Instructional Programs, Primary Grouping (CIP_PG) (14 items: Total; instructional programs; Personal improvement and leisure; Visual and performing arts; and communications technologies; Education ...), Sex (3 items: Both sexes; Females; Males ...).

Dataset

This dataset was created by Valerie Chase

Released under CC0: Public Domain

Contents

Increase the number of degrees and certificates obtained from 40,484 in 2014 to 47,284 by 2018.

Differences in highest level of educational attainment between persons with and without disabilities as well as how it varies by level of severity of disability, by age group and gender, Canada, provinces and territories.

Financial overview and grant giving statistics of Mtna Certification Program

Graph and download economic data for Unemployment Rate: College Graduates: Advanced Degree: Master's Degree and Higher, 25 years and over, Men (CGAD25OM) from Dec 2014 to Sep 2025 about master's degree, 25 years +, tertiary schooling, males, education, household survey, unemployment, rate, and USA.

What is this ? In this case study, I use a bike-share company data to evaluate the biking performance between members and casuals, determine if there are any trends or patterns, and theorize what are causing them. I am then able to develop a recommendation based on those findings.

Content: Hi. This is my first data analysis project and also my first time to use R in my work. They are the capstone project for Google Data Analysis Certificate Course offered in Coursera. (https://www.coursera.org/professional-certificates/google-data-analytics) It is about operation data analysis of a frictional bike-share company in Chicago. For detailed background story, please check the pdf file (Case 01.pdf) for reference.

In this case study, I use a bike-share company data to evaluate the biking performance between members and casuals, determine if there are any trends or patterns, and theorize what are causing them by descriptive analysis. I am then able to develop a recommendation based on those findings.

First I will make a background introduction, my business tasks and objectives, and how I obtain the data sources for analysis. Also, they are the R code I worked in RStudio for data processing, cleaning and generating graphs for next part analysis. Next, there are my analysis of bike data, with graphs and charts generated by R ggplot2. At the end, I also provide some recommendations to business tasks, based on the data finding.

I understand that I am just new to data analysis and the skills or code is very beginner level. But I am working hard to learn more in both R and data science field. If you have any idea or feedback. Please feel free to comment.

Stanley Cheng 2021-09-30

Percentage of Canadians' use of selected online activities, during the past three months.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

The online higher education market is experiencing robust growth, fueled by increasing accessibility, affordability demands, and technological advancements. The market's Compound Annual Growth Rate (CAGR) of 19.82% from 2019 to 2024 suggests a significant expansion, likely driven by factors such as the rising adoption of online learning platforms, flexible learning options catering to working professionals and geographically dispersed students, and the increasing recognition of online degrees by employers. The market segmentation, encompassing diverse types of online programs (e.g., bachelor's, master's, certificate programs) and applications across various fields (e.g., business, technology, healthcare), contributes to its broad appeal and expansion. Major players like American Public Education, Adtalem Global Education, and others are deploying competitive strategies focused on enhancing the learning experience, improving student support services, and expanding their program offerings to maintain a competitive edge. The geographic distribution indicates strong growth across North America and Asia-Pacific, driven by higher internet penetration and a growing young population seeking educational opportunities. However, challenges remain, including concerns about the perceived quality of online education compared to traditional institutions, the digital divide limiting access for certain demographics, and the need for continuous investment in technology and curriculum development to meet evolving learner needs. Looking ahead to 2033, the online higher education market is projected to maintain significant momentum, further expanding its reach and influence. Continued technological innovation, including advancements in virtual reality and artificial intelligence, will enhance the learning experience and attract a broader range of students. The growing importance of lifelong learning and upskilling will also drive demand for online courses and degree programs. Competitive pressures will likely lead to further innovation in pricing models, program offerings, and marketing strategies, fostering a dynamic and evolving market landscape. To fully capitalize on this growth, educational institutions must prioritize creating engaging and effective online learning environments, addressing concerns around quality and accessibility, and adapting to the ever-changing needs of students in a globally competitive market.

Dataset is a cleaned version of a public dataset provided in the Capstone section of the Google Data Analytics Certificate course.

Main tool used for cleaning is: Google Sheet.

Dashboard created from analysis of the Dataset can be viewed here: https://public.tableau.com/views/BellaBeat_16920165317070/Overlook?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

Context

Hi. This is my data analysis project and also try using R in my work. They are the capstone project for Google Data Analysis Certificate Course offered in Coursera. (https://www.coursera.org/professional-certificates/google-data-analytics) It is about operation data analysis of data from health monitoring device. For detailed background story, please check the pdf file (Case 02.pdf) for reference.

In this case study, I use personal health tracker data from Fitbit to evaluate the how the usage of health tracker device, and then determine if there are any trends or patterns.

My data analysis will be focus in 2 area: exercise activity and sleeping habit. Exercise activity will be a study of relationship between activity type and calories consumed, while sleeping habit will be identify any the pattern of user sleeping. In this analysis, I will also try to use some linear regression model, so that the data can be explain in a quantitative way and make prediction easier.

I understand that I am just new to data analysis and the skills or code is very beginner level. But I am working hard to learn more in both R and data science field. If you have any idea or feedback. Please feel free to comment.

Stanley Cheng 2021-10-07

Graph and download economic data for Unemployment Rate - Associate Degree - Academic Program, 65 years and over, Women (ADAP65OW) from Feb 2000 to Aug 2025 about 65 years +, associate degree, females, education, unemployment, rate, and USA.

The goal of this study was to conduct a national empirical assessment of post-release employment and recidivism effects based on legislative intent for inmates participating in Prison Industries Enhancement Certification Program (PIECP) as compared to participants in traditional industries (TI) and those involved in other than work (OTW) activities. The research design for this study was a quasi-experimental design using matched samples. The inmates were matched using six criteria. Exact matches were made on race, gender, crime type, and category matches on age, time served, and number of disciplinary reports. A cluster sampling strategy was used for site selection. This strategy resulted in a selection of five states which were not identified in the study. The researchers then collected data on 6,464 individuals by completing record reviews of outcomes for the 3 matched samples, each of approximately 2,200 inmates released from 46 prisons across 5 PIECP states between January 1, 1996, and June 30, 2001. Variables include demographic information, time incarcerated, number of disciplinary reports, crime type, number of major disciplinary reports reviewed, group type, number of quarters from release to employment, censored variables, number of quarters from employed to job loss, time from release variables, number of possible follow-up quarters, proportion of follow-up time worked, wage variables, number of quarters worked variables, no work ever, and cluster number of case.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Some college or associate degree: 25 years and over: Men (LEU0254930000Q) from Q1 2000 to Q2 2025 about no college, associate degree, second quartile, 25 years +, full-time, males, salaries, workers, earnings, education, wages, median, employment, and USA.