In addition to skills that are directly jobs-relevant, during the COVID-19 context of 2020, data from the online learning provider Coursera has been able to identify an increasing emphasis within learner reskilling and upskilling efforts on personal development and self-management skills. Figure 29 and 30 together demonstrate that it is common for individuals moving into Data and AI to lack key data science skills-but that individuals seeking to transition into such roles will be able to work towards the right skill set through mastery of skills such as statistical programming within a recommended time frame, in this case, 76 days of learning. Figure 30 presents the typical learning curriculum of Coursera learners who are targeting a transition into Data and AI and the distance from the optimal level of mastery in the relevant job cluster, and quantifies the days of learning needed for the average worker to gain that level of mastery. Figure 29 presents the set of high-growth, emerging roles that are currently covered by the Data and AI job cluster, and the typical skills gap between source and destination professions when workers have moved into those roles over the past five years. Figures 29 and 30 demonstrate those metrics. This report reveals in further granular detail the types of insights that can guide job transitions through to appropriate reskilling and upskilling. The emerging Cloud Computing job cluster is primarily populated by professionals transitioning from IT and Engineering. While emerging roles in Product Development draw professionals from a range of job families, emerging roles in People and Culture job cluster typically transition from the Human Resources job family. As illustrated in Figure 25 emerging job clusters are typically staffed by workers starting in a set of distinctive job families, but the diversity of those source job families varies by emerging profession. In contrast, 72% of Data and AI bound transitions originate from a different job family and 68% of transitions into emerging jobs within Sales. As presented in Figure 24 C only 19 % and 26% of job transitions into Engineering and People and Culture, respectively, come from outside the job family in which those roles are today. That is, the ‘skill penetration’ figure indicates the individuals from that occupation who list the specific skill as a share of all individuals employed in that occupation.īy analysing these career pivots-instances where professionals transition to wholly new occupations-it becomes apparent that some of these so-called ‘jobs of tomorrow’ present greater opportunities for workers looking to fully switch their job family and therefore present more options to reimagine one’s professional trajectory, while other emerging professions remain more fully bounded. To understand the skill profile of each occupation, analysts calculated the ‘skill penetration’ score for each skill associated with an occupation. This indicates the share of individual skills associated with that occupation that belong to a given skill group. To examine the extent to which certain skills groups of interest are associated with a particular occupation, a ‘skill penetration’ figure is calculated. To understand the skill profile of each occupation, analysts first identified a list of the most representative skills associated with an occupation, based on LinkedIn’s Skills Genome Metric which calculates the ‘most representative’ skills across roles, using the TF-IDF method. The researchers analysed when professionals transitioned into any new role as well as when they transitioned to a wholly new occupation-here called ‘pivots’. For this analysis the LinkedIn data science team analysed the job transitions of professionals who moved into emerging jobs over the period of 2015 to 2020. In this report we present a unique extension of this analysis which examines key learnings gleaned from job transitions into those emerging clusters using LinkedIn data gathered over the past five years.
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