DataIQ has partnered up with Tech Nation UK to have a peek behind the curtain of a data project and find out exactly what takes place when interrogating a dataset to find insights. Diana Akanho takes us, step by step, through the stages of a data project.
Here is what Diana Akanho, senior insights manager at TechNation UK had to say.
The reason we are doing this project in the first place is we see there is demand for this type of information from clients, sponsors and partners and we seek to meet that demand.
The working title of this project is ‘Exploring the landscape of UK tech jobs and skills,’ which aims to help and inform employers, government policy makers and investors - but then also employees who may be seeking career opportunities or would like to upskill and want to understand the skills required for digital/tech roles.
The data, in this case, is a purchased dataset from Adzuna, a search engine for job advertisements. We don’t have to make any considerations for ethical use of the data or privacy because it was supplied to us in an aggregate format and there is no sensitive information included in it that could identify anyone.
First have a research question you want to answer.
The first step from a statistical analysis point of view is to have a research question you want to answer, based on background research. Then create a hypothesis that will be either validated or not through the analysis.
A possible hypothesis could be ‘the number of digital/tech roles within the UK, particularly in London, has grown over the past 5 years’.
At the start of a data project, you are looking for data to answer a question you already have, but at the same time, you are looking at the data and thinking about what conclusions you can draw from it.
When digging into the data, we’ll find the hypothesis true or false.
However, when digging into the data, we will find the hypothesis to be true or false - this will then lead to further questions we’d like to find from the data to either prove or disprove findings.
Before cleaning, processing or formatting the data, there are processes involved. First, I checked the size of the data. Then, I did a quick scan of the data initially to understand the type of variables I have. This is so I know the type of data I will be working with to start thinking about the methods I will use to analyse it. The data could be continuous, text-based data, categorical, etc.
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