Making the most of data: Data skills training in English universities
Notice bibliographique
Résumé
The collection and analysis of quantitative data is becoming increasingly important across a range of sectors.As business and research interest in data expands, so too does the demand for workers able to analyse and interpret datasets.The potential to utilise data hinges on the supply of skilled individuals.However, research suggests that employers are struggling to find suitable candidates for data roles. 1 In recognition of this challenge, the government asked Universities UK to 'review how data analytics skills are taught across different disciplines and assess whether more work is required to further embed these skills across disciplines.'This report aims to engage with both the immediate shortage of data analysts, and the need for greater data literacy.As organisations become more data driven there is a need for all workers to be able to interpret data and to undertake basic analysis.Taken together, this report and Nesta's report, Skills of the datavores: talent and the data revolution, set out a coherent picture of both the supply and the demand for data analysts and data-literate graduates.In a joint briefing statement in July 2015, Nesta and Universities UK will present findings, implications for policy makers, and recommendations. Findings1 Although the skills shortage is widely reported the skills that entry-level data analysts should have is not clearly set out.This has restricted positive action.In order to move forward these skills should be clearly set out, both by employers and by educators in their description of course content. 2The data skills shortage is not simply characterised by a lack of recruits with the right technical skills, but rather by a lack of recruits with the right combination of skills.The shortage of technical skills widely reported in the media is an over simplification of what is, in reality, a more complex issue.Employers report that there is a shortage of graduates with the right combination of skills.The combination of skills required includes a range of technical skills and domain knowledge, but also the ability to transform data outputs into something valuable to employers.3 Usually, a combination of technical skills is achieved through multi-disciplinary teams, with every team member possessing deep skills in several areas and basic knowledge in others.This shows that data skills needs cannot be boiled down to a simple list of skills that all undergraduates should acquire.Rather, there may be a number of core skills that should be shared by all members of a data team, and individual, specialist skills that may be developed in particular disciplines.The development of data teams emphasises the need for data analysts to possess strong teamwork and communication skills.4 There is no consistent method for identifying the extent of data analysis teaching within undergraduate programmes.A scheme to identify courses with significant data analysis components would provide valuable information to both prospective students and employers.5 Many undergraduate degree programmes teach the basic technical skills needed to understand and analyse data.Data can be gathered and analysed to enhance knowledge and understanding.This is largely reflected in undergraduate degree courses, where data analysis skills are taught across many programmes.This is also recognised by employers, who recruit data analysts from a range of subject areas, most commonly from those science, technology, engineering and mathematics (STEM) and social science courses where data analysis training is most prevalent and advanced.1 McKinsey Global Institute (2011) Big data: The next frontier for innovation, completion and productivity available at: http://www.mckinsey.com
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».