Heterogeneous Data

Skills4Industry taxonomy dataset offers a discrete set of possibilities represented by a data cluster that is classified into occupations by Industry, Sectors, Trade, and Domains (ISTD) columns and competency (academic, relational, work and contextual domain culture) in rows across all occupations. In a functional analysis conducted on over 200,000 job positions crawled from different sites on the web. We analyzed the functions within job positions and defined the purpose of job titles relating to industry, sector, trade and cultural domain (community). A statistical best fit method was used to place the task definitions in their order of importance. The most important at the top, and down to the list important. The most critical tasks that spoke to trade (the smallest business groups) were grouped into the bottom, while those synonymous with large organizations placed at the top (industry). While the tasks were statistically fit into requirements for knowledge, work skills, work tools, relational and domain cultural context.

A weighted sum using excel power function and different correlation tools were used to determine the competency requirements to achieve each task, based on dependent variables within each competency (independent variables). It is noteworthy that a common thread found as we transition from level one to ten is that requirements for relational skills increases, which follow an increasing number of Skills4Industry business network nodes.

The occupations and competency data are classified into a rules-based Question Classification system designed to feed machine learning algorithms data structured within a Syntatic Map as a boundary.

Skills4Industry hierarchical competency models are explicitly descriptive, allowing anyone to trace a final decision back to the appropriate top-level sub-problem, and report how strongly it contributed to making the observed results of a student (work-based learning) or employee (lifelong learning) or work transition (lifelong) learner.

Competency: Skills4Industry defines competence as the mastery of academic knowledge, relational skills, work skills, domain contextual, and cultural skills by an individual.

Ranking Competencies: Competence provide a means to define movement (vertically or horizontally) within a job. To achieve this movement levels based on the degree of demonstrable competence are implicit. By analyzing occupational profile and jobs advertising websites to extract job titles. The job titles were decomposed into over 3.6 million tasks concepts, using the Syntactic Map as a pars through. Scoring functions were developed to rank these tasks by the amount of competency required to perform the task within a domain described under a job title. A job title contains several tasks, and it is only a pointer to the where the job is performed and doesn’t represent all distinguishable properties of the domain. We rolled these tasks under their different job titles into high groups (industry), middle group (sectors) and lower groups (trades). These occupations were ranked into ten levels starting at level one by the complexity of required competencies. As a result, each column contains a set of occupational titles, job tasks, work skills, work tools, relational and domain context, while each of the independent variables contains several dependent variables.