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The work summarizes apprenticeship demographics, maps available occupations and programs by OOD region and county, and tests alignment between VR participants’ Individualized Plan for Employment (IPE) goals and apprenticeable occupations. Public apprenticeship.gov data and OOD VR data are processed with Excel, Python, and visualized in Tableau, with K-modes clustering used to identify region-by-industry opportunity hotspots.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-and-data-analysis-to-improve-vocational-rehabilitation-placement/125740/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-and-data-analysis-to-improve-vocational-rehabilitation-placement/125740.png","ImageObject",300,407,{"name":92,"@type":93},"Ava Thompson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-29","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the study?","Question",{"text":112,"@type":113},"The study identifies apprenticeship opportunities in Ohio and determines whether OOD VR participants have IPE job goals aligned with apprenticeable occupations.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What data sources and tools were used?",{"text":117,"@type":113},"It uses public apprenticeship.gov data plus OOD VR data available through a state internship, with Excel and Python for preprocessing and Tableau for visualizations.",{"name":119,"@type":110,"acceptedAnswer":120},"How are potential matches between VR participants and apprenticeships determined?",{"text":121,"@type":113},"The OOD VR participants are filtered in Tableau to keep only cases where an IPE matches an apprenticeship occupation, resulting in 2,455 matches.",{"name":123,"@type":110,"acceptedAnswer":124},"What does the clustering analysis reveal?",{"text":125,"@type":113},"K-modes clustering groups apprenticeship programs by OOD region and industry (using SOC code digits), highlighting top combinations and regional “hotspots” for opportunity.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},125740,1785900963,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":143,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":144,"faqs":145,"seo_title":146,"seo_description":67,"update_tm":133,"read_time":81},962084925782,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Machine Learning and Data Analysis to  \nImprove Vocational Rehabilitation Placement  \nCordelia Van der Veer, Advised by Dr. Joshua Hawley  \nJohn Glenn College of Public Affairs, Ohio Education Research Center Public Sector Data Science Internship  \nOverview of Apprenticeships  \nAccording to the Department of Labor,“Apprenticeship is an industry-driven, high-quality career pathway where employers can develop and prepare their future workforce, and individuals can obtain paid work experience, classroom instruction, and a portable, nationally-recognized credential.” You must have a high school diploma or equivalency to participate .  \nAs of 2023, Ohio has the third largest number of active apprentices after Texas and California .  \nOpportunities for Ohioans with Disabilities  \nOpportunities for Ohioans with Disabilities (OOD) is a state agency that provides individuals with disabilities assistance in finding accessible jobs through the federally-funded Vocational Rehabilitation (VR) program .  \nCurrently, there are no OOD VR participants who are active in apprenticeship programs . OOD wants to understand the current state of apprenticeship opportunities and learn if any of their participants have a job goal (IPE) aligned with apprenticeshipsin their area .  \nResearch Questions  \n• What are the demographics of apprentices in Ohio?  \n• What apprenticeship occupations and programs are available in each OOD region/county?  \n• Are there any VR participants who have an Individualized Plan for Employment (IPE) (job goal) aligned with an apprenticeship program(s)?  \n• What are the trends in geography and industry of apprentices’ occupations?  \nTableau Dashboard of Apprentice Demographics in Ohio  \n| Data and Methods\u003Cbr>I used public data [from apprenticeship.gov](from apprenticeship.gov), which is provided by the Department of Labor. Since I was a state employee, I also had access to the OOD VR data . I primarily used Excel and Python for data preprocessing and Tableau for visualizations . For the machine learning component, I exclusively used Python .\u003Cbr>Understanding Available Apprenticeships\u003Cbr>In the context of placing OOD participants, apprenticeship programs have two key qualities: location and occupation . I filtered to only include Ohio and displayed visualized the public apprenticeship data in Tableau\u003Cbr>Ohio Counties by Number of Apprenticeship Programs\u003Cbr>\u003Cbr>This chart shows Ohio counties with apprenticeship programs. The darker the county, the more apprenticeship programs.\u003Cbr>Most Common Occupations with Apprenticeship Programs\u003Cbr>\u003Cbr>This chart shows Ohio counties with apprenticeship programs. The darker the county, the more apprenticeship programs. | Finding Potential Matches\u003Cbr>To find potential OOD VR participants with apprenticeable IPEs, I added both the OOD data and the apprentice data into Tableau . I filtered the OOD data to only include participants whose IPE matched an apprenticeship occupation . There are 2,455 matches .\u003Cbr>Most Common VR Occupations with Apprenticeship Programs\u003Cbr>\u003Cbr>This shows the top apprenticeship occupations by number of VR participants who have the occupation as their IPE. In the future, these could be the occupations with the most VR participants in apprenticeships\u003Cbr>Machine Learning and Data Prep\u003Cbr>To preprocess the data, I assigned an OOD region (a group of counties OOD uses to segment Ohio) to each apprenticeship program and took the first two digits of the SOC code (represents the industry) . I then utilized Kmodes categorical cluster analysis to show the top ten combinations of OOD region and industry. This shows the top areas for apprenticeship opportunity.\u003Cbr>Cluster Analysis of Apprenticeship Region and Industry\u003Cbr>\u003Cbr>47 = Construction and Extraction Occupations, 37 = Building and Grounds Cleaning and Maintenance Occupations, 51 = Production Occupations, 33 = Protective Service Occupations, 49 = Installation, Maintenance, and Repair Occupations, 31 = Healthcare Support Occupati","cbCaiezdjLukwOBM","https://ap.wps.com/l/cbCaiezdjLukwOBM","pdf",691473,"English","# Overview of Apprenticeships\n## Opportunities for Ohioans with Disabilities\n# Research Questions\n# Data and Methods\n# Understanding Available Apprenticeships\n## Ohio Counties by Number of Apprenticeship Programs\n## Most Common Occupations with Apprenticeship Programs\n# Finding Potential Matches\n## Most Common VR Occupations with Apprenticeship Programs\n# Machine Learning and Data Prep\n## Cluster Analysis of Apprenticeship Region and Industry\n# Opportunities for Further Research\n# Conclusion\n# Acknowledgement\n# Contact","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"The study identifies apprenticeship opportunities in Ohio and determines whether OOD VR participants have IPE job goals aligned with apprenticeable occupations.\"},{\"question\":\"What data sources and tools were used?\",\"answer\":\"It uses public apprenticeship.gov data plus OOD VR data available through a state internship, with Excel and Python for preprocessing and Tableau for visualizations.\"},{\"question\":\"How are potential matches between VR participants and apprenticeships determined?\",\"answer\":\"The OOD VR participants are filtered in Tableau to keep only cases where an IPE matches an apprenticeship occupation, resulting in 2,455 matches.\"},{\"question\":\"What does the clustering analysis reveal?\",\"answer\":\"K-modes clustering groups apprenticeship programs by OOD region and industry (using SOC code digits), highlighting top combinations and regional “hotspots” for opportunity.\"}]","Machine Learning and Data Analysis to Improve Vocational Rehabilitation Placement | PDF"]