Final answer:
To manage a very large dataset, a data analyst can use filtering, sampling, or aggregation to narrow the data, selecting relevant subsets that align with the research questions.
Step-by-step explanation:
A data analyst looking to manage a very large dataset can narrow the amount of data by several means to make the analysis more manageable. This process often involves selecting a subset of the dataset through various techniques like filtering, sampling, or aggregating data.
Filtering involves choosing only the data that is relevant to the question at hand. For example, if a database contains records spanning 20 years, the analyst might only look at the last 5 years to keep the dataset relevant and more manageable.
Sampling is selecting a representative subset of the dataset to perform analysis on. This could involve random sampling, stratified sampling, or other statistical sampling methods.
Lastly, aggregating data is the process of summarizing or combining data to show a bigger picture, which can significantly reduce the volume of data points. This could mean calculating averages, sums, or other metrics that encapsulate larger sets of data.
The analyst can justify the selection of the kind of data needed by correlating how each subset or manipulation of the dataset directly relates to the scientific questions posed in the analysis.