Traditional solutions for career decision making use general rules (or heuristics) to assess an individual, leaving counselors to often rely on their intuition and experience to make recommendations. However, latest research in the field endorses non-linear models (data driven) and learning systems instead. A significant advantage of comparing profiles this way is that both strengths and weaknesses of decision makers are recognized and considered while recommending careers. And with O*NET data available for careers on several aspects, across the world of work, an analytical approach is feasible as well as logical to adopt.
Adopting the O*NET theoretical context, the Tucareers analytical model takes a further with the following value adds:
Better Descriptive Capabilities: For profile comparisons of an individual on a comprehensive set of variables covering the entire world of work, filtering and shortlisting careers that best suit him/her
Unique prediction model: For determining an individual’s satisfaction, tenure and life time earnings in different careers.
Prescriptive Model: For providing optimal and stable recommendations to assist in selection of streams/electives in school or deployment of a team in industry situations etc.
We also use artificial intelligence and machine learning algorithms (tested and proven) for recommendations