Data Science

Skills4Industry taxonomy was created to sufficiently empower artificial intelligence and data, with componential elements of over 3.6 million functional tasks structured layers of competencies rules and logic to guide machine learning.

Skills4Industry taxonomy organizes unstructured data into meaningful layers of actual information instances (specific objects) to ensure efficient retrieval and analytics for future forecasting.

To achieve this, we crawled occupational profile and job advertising websites to extract meaningful variable data to construct over 3.6 million tasks. A scoring function was developed to rank the tasks by their degree of impact on the task title, having being defined as competency required to perform tasks within the domain represented by the job title. Although, the job title containing several tasks is only a pointer to the work domain and doesn’t represent all distinguishable properties of the job domain where a particular task is performed. This ranking does a great job separating the important skills required for performing different tasks by rounding out the top from lesser. The data is meant to serve as the basis for determining the competencies required by individuals and machines to do a job to the level valued by employers.

The Skills4Industry taxonomy data collection experience is meant to drive our machine learning predictive models, whose tales we present under our future of learning stories. Every data tells a story and data scientist are storytellers. The Skills4Industry stories are true and follows a sequence of occurring events based on the resulting impact of our predictive models on industries, sectors, trades and domains (ISTD) in which individuals and machines apply our models.

The stories will improve in the retelling, and the dialogue will be punched up to make them more interactive for ISTD specific game environment. This is intended to honestly trace the process of applying competencies to different raw problems in different work domains. People can watch how their competencies unfolded and acquire the proficiency to stay on top of their competency game from levels 1 to 10 that is mapped to 5th grade to Ph.D.

Structuring & Cleaning Competency Data

Structuring competency data involves the collection and analyzing of data to tell stories about which competencies are needed for what tasks, given how many relevant competencies there are, and how much domain specific competencies are required to address specific tasks, using heterogeneous data. Heterogeneous data such as, those from work performances, academic knowledge and relationships are difficult to structure into simple matrix, where the rows of the matrix represent distinct items or records, and the columns represent distinct properties of these items. For example, a data set about an individual’s competencies might contain one row for each domain with columns representing features like state, population, natural resources, climate, industry cluster, laws, affluence and firm. However, confronted with unstructured data sources, such as a collection of occupational profiles and job descriptions from websites, our first step is generally to build a matrix to structure it. However, these sources tells less than half the story about all the different types of skills, knowledge and understanding employers need today. A competency model will thus, construct a matrix with a row for each task, and a column for each frequently used competency. Matrix entry M[i,j] then denotes the number of times task i contains competency j.

We use data rescaling to improve the quality of a dataset by reducing dimensions and avoiding the situation when some of the values overweight others.

Imagine running High School College Placement services and most of the attributes in your dataset are either categorical to depict college specialization and careers (arts and humanities, Social & Natural Sciences, STEM, etc.) or have 1-2 digit numbers, for instance, for years in college (2 years or 4 years) and work experience. But the tuition and other costs are 5-6 digit numbers ($50000 or $180000) and want to predict the average time for employment and the completion of student loan repayment based on the career pathway market value. Including, other characteristics (four year degree, data analyst and computer science degree, average entry salary, living standard, savings, etc.). While the entry salary wage is an important criterion, we don’t want it to overweight the other ones with a larger number.

In this case, we use min-max normalization. This method entails transforming numerical values to ranges, e.g. from 0.0 to 5.0 where 0.0 represents the minimal and 5.0 the maximum values to even out the weight of the wages attribute with other attributes in a dataset.

A bit simpler approach is decimal scaling. It consists of scaling data by moving a decimal point in either direction for the same purpose.

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