The Big Data Gap: Is There Congruence Between Data Skills Demanded By The Industry And Canadian Academic Preparation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis examines occupations in the Canadian Big Data industry. Through analysing Canadian job advertisements, the aim is to understand what skills a professional in a data occupation requires. Supplementing with a content analysis of graduate master‘s programs focusing in Data Science, Analytics or Big Data the study explores Canadian educational institution offerings which prepare students for jobs in the field. Using topic modeling methods and typologies results show a fit between universities‘ presented content and what skills are demanded by the industry. An updated framework of Todd et al. (1995) is presented for easier comparison and recommendations of creating undergraduate data programs are discussed. Contributions made by this study can aid universities in a structuring their curricula for ―Big Data‖ programs. Furthermore, this study contributes to the literature by explaining multiple job qualifications which allows for more standardized job descriptions, benefiting the employers, job seekers and universities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it