We plan to build on In Focus by next exploring the financial security angle, looking at the key drivers of pensioner poverty, and how these vary across sub-groups.
Independent Age has gained vastly from our experiences of both commissioning research partners and adopting a co-production element to some of our work, and we will continue to build on france rcs data both to support our work to improve people’s lives.
Once we have data at hand, pre-processing the data can involve various different tasks, including:
Data integration: integration of multiple databases, data cubes, or files;
Data cleaning: fill in missing values, smooth noisy data, identify or remove outliers and noisy data, and resolve inconsistencies;
Data reduction: obtain reduced representation in volume which produces the same or similar analytical results;
Data transformation: normalisation and aggregation;
Data discretisation (for numerical data) and generalisation.
Please bear in mind that there is no particular order for carrying out these tasks! There is currently no standardised method for data pre-processing; the tasks listed above are simply the most common ones. In fact, it is relatively common for some of these tasks to be repeated multiple times; for example, after reading the metadata, you may clean the data to remove all null values, however, joining tables creates new missing values, which requires further data cleaning.