About this data

The line chart displays Google Trends search data for the specified job title over the past 10 years. Data was sampled as a quarterly average using three month periods starting in July.

The scores awarded by Google Trends are based on the absolute search volume for a term, relative to the number of searches received by Google. The resulting numbers are then scaled on a range of 0 to 100. This score is based on a term’s proportion to all searches on all topics, where 100 is peak popularity and 0 is little to no interest. You can read more on how Google adjusts there Trends data here.

Methodology

Job titles were selected based on four of REED's key industries that are specified in our yearly salary reports.

About this data

This chart displays data from the Open Skills Project API, an open project that seeks to increase the understanding of the job market by expanding upon the US Department of Labor’s O*NET database. The research is based on widespread and ongoing research of employer skills and job listings.

Each job returns a list of skills, and each skill has two scores attached: importance and level. For simplicity, we have renamed these skills as importance to job and frequency of use. A more thorough explanation of the difference between importance and level can be found here.

Methodology

Job titles were matched on a case-by-case basis. If a Reed job title did not exist word-for-word, our analysts selected the closest job title in the API from their descriptions. Due to the high amount of skills returned for each job, we applied a filter to the data to only look at the skills that were most important, or most essential, to each job. For the purposes of our analysis, we are only selecting skills that score 3.5 or above in the importance value as "most important". Each value is then assigned a value category (i.e. "Very high"). These categories are assigned based on the values of each skill, split into five equal ranges. It should also be noted that "importance to job" has a maximum raw value of 5, and "frequency of use" has a maximum raw value of 7.

Additionally, because we are assuming a UK audience, we have filtered out the skill “english language” so that the more nuanced and interesting skills can come to the front. Because we wanted to focus on professional skills that relate to our four industry sectors, we have also filtered out any skills that are physical in nature: "near vision", "far vision", "finger dexterity", "speech recognition", "speaking", and "speech clarity". In the "Tech" industry, we have filtered out the skill "computer & electronics" for these roles. As experts in recruiting for these roles, we wanted to pull out some of the more nuanced skills in this industry; for this purpose, we felt comfortable assuming that "computer & electronics" is a given for the roles we analysed.

About this data

The line chart displays Google Trends search data for key tools or technologies related to a job title over the past 10 years. Data was sampled as a quarterly average using three month periods starting in July.

When multiple lines appear on the same chart, these terms are being compared relative to one another. This means they are ranked using the same 0 - 100 scale, and do not represent the raw search volume of each term. You can read more on how Google adjusts there Trends data here.

Methodology

Relevant tools for each job were selected through a combination of data analysis and domain knowledge from REED experts in each industry. First, a list of relevant tools was obtained through the Open Skills Project API for each job title. Then, because Google Trends can only return data for a maximum of five queries, REED experts analysed the most timely and interesting tools to include for each of the job titles.