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Description: Dive into the world of college placements with this dataset designed to unravel the factors influencing student placement outcomes. The dataset comprises crucial parameters such as IQ scores, CGPA (Cumulative Grade Point Average), and placement status. Aspiring data scientists, researchers, and enthusiasts can leverage this dataset to uncover patterns and insights that contribute to a deeper understanding of successful college placements.
Project Idea 1: Predictive Modeling for College Placements Utilize machine learning algorithms to build a predictive model that forecasts a student's likelihood of placement based on their IQ scores and CGPA. Evaluate and compare the effectiveness of different algorithms to enhance prediction accuracy.
Project Idea 2: Feature Importance Analysis Conduct a feature importance analysis to identify the key factors that significantly influence placement outcomes. Gain insights into whether IQ, CGPA, or a combination of both plays a more dominant role in determining success.
Project Idea 3: Clustering Analysis of Placement Trends Apply clustering techniques to group students based on their placement outcomes. Explore whether distinct clusters emerge, shedding light on common characteristics or trends among students who secure placements.
Project Idea 4: Correlation Analysis with External Factors Investigate the correlation between the provided data (IQ, CGPA, placement) and external factors such as internship experience, extracurricular activities, or industry demand. Assess how these external factors may complement or influence placement success.
Project Idea 5: Visualization of Placement Dynamics Over Time Create dynamic visualizations to illustrate how placement trends evolve over time. Analyze trends, patterns, and fluctuations in placement rates to identify potential cyclical or seasonal influences on student placements.
IQ:
CGPA:
Placement:
These columns collectively provide a comprehensive snapshot of a student's intellectual abilities, academic performance, and their success in securing a placement. Analyzing this dataset can offer valuable insights into the dynamics of college placements and inform strategies for optimizing student outcomes.
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In some cases, first- and final-year students were at the same nursing home which explains why the total number is 21 and not 23.
About Employment ServiceEmployment Service (ES) is one component of the suite of services known as Employment Ontario (EO). ES provides Ontarians with access to all the employment services and supports they need in one location, so they can find and keep a job, apply for training, and plan a career that’s right for them. The goal of the ES program is to help Ontarians find sustainable employment.Employment Service is delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services provided by ES are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format.Employment Service has two broad categories: unassisted and assisted services.Unassisted services, or the Resource and Information (RI) service component, provides individuals with information on local training and employment opportunities, community service supports, and resources to support independent or “unassisted” job search. These services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. The RI component also helps employers to attract and recruit employees and skilled labour by posting positions and offering opportunities to participate in job fairs and other community events.This service component is available to all Ontarians as there are no eligibility or access requirements.Assisted services are offered to individuals who display the need for more intensive, structured, and/or one-on-one employment supports, and includes the following components:job search assistance (including individualized assistance in career goal setting, skills assessment, and interview preparation)job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)The service provider will develop with the assisted services client an ES service plan – and will monitor, evaluate, and adjust this plan over the duration of the service plan.To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.About ES Service Provider FundingService providers that deliver Employment Service sign agreements with MLTSD that cover individual fiscal years (defined as April 1st to March 31st). These agreements specify at which service delivery site(s) the service provider agrees to provide ES, the performance expectations for each service delivery site (SDS), and the funding that MLTSD will provide to the service provider to deliver ES at each SDS. Funding for ES is provided through two budget categories: operating funds and flow-through funds, with the latter further divided between Employment and Training Incentives for Employers and Employment and Training Supports for Clients/Participants. These three budget lines cover the normal costs of delivering all aspects of ES for both unassisted and assisted clients; for exception one-off expenditures, such as relocation, service providers can apply for Field Supports, which is the fourth and final budget line. Please see below for additional details on each of these four budget lines:2. Operating Funds are for the direct delivery of all of the components of ES (unassisted and assisted). Costs related to the provision of the ES that would be considered part of a service delivery site’s day-to-day operations include, but are not limited to:staff and management salaries;hiring and training of staff (including professional development);marketing (signage, paper/web ads, outreach, etc.);facilities (rent);facilities (mortgage payments) ONLY the interest portion of a mortgage payment is allowed as an Operating cost;other direct operating expenditures related to the delivery of the Employment Service.Service delivery sites are able to attribute no more than 15% of their operating funds for administrative overhead. Administrative overhead recognizes costs necessary for operating an organization but not directly associated with the delivery of the Employment Service. For example, a portion of the salaries/benefits of the Executive Director, IT, and/or financial staff who work for the entire organization but may spend a portion of their time dedicated to administrative functions that support ES. Note that Operating Funds cannot be used for termination and severance costs.2. Employment and Training Incentives for Employers are funds for employers to provide employment and on-the-job training opportunities in ES (up to $8,000 per person. The $8,000 is made up of a maximum of $6,000 for training incentives and an additional $2,000 for the Apprenticeship Employer Signing Bonus, if applicable).3. Employment and Training Supports for Clients/Participants are funds for Clients/Participants in assisted components (up to $500 per Client/Participant). These supports are determined based on family income and are intended, on a temporary basis, to help Clients/Participants address any financial barriers to participation in ES. Client eligibility for these supports is determined on the basis of need and the Low-Income Cut-offs (LICO) income value for the locality. Supports can cover costs such as:transportation;work clothing or clothing/grooming needed to achieve credibility;special equipment, supplies and equipment;certification charges (that may be applied to some short term courses);short term training costs such as books, materials;emergency or infrequent child care;language skills assessment/academic credential assessment;translation of academic documents (for internationally trained individuals);workplace accommodation needs for persons with disabilities.4. Field Supports are funds that may be provided through a formal in-year request to support ES Recipients with one-time exceptional expenditures not normally included as part of ongoing operations. Requests will be reviewed on a case by case basis and approved at the sole discretion of the Ministry. Purchases related to Field Support cannot be made without prior written approval from the Ministry.Service providers have discretion over the use of their funds within the following parameters:Operating funds are allocated against an identified level of service;In situations of co-location of ES with other programs and services, ES funds must only be used to cover costs directly related to the delivery of ES;Operating funds cannot be used for major capital expenditures, such as the purchase or construction of facilities. Purchase of equipment and furniture directly related to the effective delivery of the contracted program is allowable;A service provider must obtain prior written approval from the Ministry to shift funds between service delivery sites or communities;A service provider must not transfer funds between the four budget lines given above unless it obtains the prior written consent of the Ministry; andA service provider should not anticipate additional funds, although the Recipient should discuss any issues with the Ministry.A funding model is used to determine funding levels for the Operating Funds budget line. This model is based on the target number of assisted services clients that each service delivery site agrees to serve in that fiscal year. Note that no targeted funds are provided to deliver unassisted services; these are to be funded out of the allocation provided to service delivery sites on the basis of their target number of assisted services clients.The ES funding model allocates resources in five ranges based on the target amount of assisted services client the service delivery site is to achieve. For each range there is a sliding scale of possible funding amounts per assisted services client, and service delivery sites with higher assisted service client targets generally receive lower per client funding, on the basis that larger service delivery sites are able to achieve economies of scale. Also note that because of this graduated approach to ES funding it is possible that a service delivery site that has its assisted services client target increase may actually receive less overall funding if the target increase shifts it from one range to the next.The five funding ranges are:A/S Client TargetFunding Range per A/S ClientUp to 399$1,000 to $2,950400 to 899$925 to $2,100900 to 1,499$850 to $1,2001,500 to 1,999$795 to $1,0002,000 and Above$795The actual funding amount per assisted services client within each range is determined by reference to two groups of indicators: Location and Labour Market Environment. A service delivery site is assessed against each indicator, and within each group the number of indicators that are assessed as valid/true is totaled. The value, along with the assisted services client target, is then compared to a table to determine the funding value for Location and Labour Market Environment. The average of these two values is then multiplied by the assisted services client target to determine the amount of Operating Funds the service delivery site is to receive.The indicators for each group are below. Note that the Labour Market Environment indicators compare 2009 data for the Consolidated Municipal Service Manager area or planning zone in which the service delivery site is located with the 2009 provincial average.LocationIs the service delivery
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The Assessment of Physiotherapy Practice (APP) is a 20-item assessment instrument used to assess entry-level physiotherapy practice in Australia, New Zealand and other international locations. Initial APP reliability and validity evidence supported a unidimensional or single latent factor as the best representation of entry-level physiotherapy practice performance. However, there remains inconsistency in how the APP is interpreted and operationalised across Australian and New Zealand universities offering entry-level physiotherapy programs. In essence, the presumption that the psychometric integrity of the APP generalises across people, time, and contexts remains largely untested. This multi-site, archival replication study utilised APP assessment data from 8,979 clinical placement assessments, across 19 Australian and New Zealand universities, graduating entry-level physiotherapy students (n=1865) in 2019. Structural representation of APP scores were examined via confirmatory factor analysis and penalised structural equation models. Factor analyses indicated a 2-factor representation, with four items (1–4) for the professional dimension and 16 items (5–20) for the clinical dimension, is the best approximation of entry-level physiotherapy performance. Measurement invariance analyses supported the robustness of this 2-factor representation over time and across diverse practice areas in both penultimate and final years of study. The findings provide strong evidence for the psychometric integrity of the APP, and the 2-factor alternative interpretation and operationalisation is recommended. To meet entry-level standards students should be assessed as competent across both professional and clinical dimensions of physiotherapy practice.
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MIT Licensehttps://opensource.org/licenses/MIT
License information was derived automatically
Description: Dive into the world of college placements with this dataset designed to unravel the factors influencing student placement outcomes. The dataset comprises crucial parameters such as IQ scores, CGPA (Cumulative Grade Point Average), and placement status. Aspiring data scientists, researchers, and enthusiasts can leverage this dataset to uncover patterns and insights that contribute to a deeper understanding of successful college placements.
Project Idea 1: Predictive Modeling for College Placements Utilize machine learning algorithms to build a predictive model that forecasts a student's likelihood of placement based on their IQ scores and CGPA. Evaluate and compare the effectiveness of different algorithms to enhance prediction accuracy.
Project Idea 2: Feature Importance Analysis Conduct a feature importance analysis to identify the key factors that significantly influence placement outcomes. Gain insights into whether IQ, CGPA, or a combination of both plays a more dominant role in determining success.
Project Idea 3: Clustering Analysis of Placement Trends Apply clustering techniques to group students based on their placement outcomes. Explore whether distinct clusters emerge, shedding light on common characteristics or trends among students who secure placements.
Project Idea 4: Correlation Analysis with External Factors Investigate the correlation between the provided data (IQ, CGPA, placement) and external factors such as internship experience, extracurricular activities, or industry demand. Assess how these external factors may complement or influence placement success.
Project Idea 5: Visualization of Placement Dynamics Over Time Create dynamic visualizations to illustrate how placement trends evolve over time. Analyze trends, patterns, and fluctuations in placement rates to identify potential cyclical or seasonal influences on student placements.
IQ:
CGPA:
Placement:
These columns collectively provide a comprehensive snapshot of a student's intellectual abilities, academic performance, and their success in securing a placement. Analyzing this dataset can offer valuable insights into the dynamics of college placements and inform strategies for optimizing student outcomes.