Examples of using Big data source in English and their translations into Greek
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Colloquial
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Official
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Medicine
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Ecclesiastic
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Financial
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Official/political
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Computer
Big data sources are both found and designed;
Table 2.3: Examples of natural experiments using big data sources.
Big data sources tend to have a number of characteristics in common;
Table 2.3: Examples of natural experiments using big data sources.
Measurement in big data sources is much less likely to change behavior.
Measurement is much less likely to change behavior in big data sources.
Far from distinctive, many big data sources have information that is sensitive.
Then, in Section 2.3,I describe ten common characteristics of big data sources.
The big data sources of today, and likely tomorrow, tend to have 10 characteristics.
Figure 3.12: Two ways to combine big data sources and survey data. .
In the next section,I will describe ten common characteristics of big data sources.
The big data sources of today-and likely tomorrow-will tend to have 10 characteristics.
Then, in Section 2.3,I describe ten common characteristics of big data sources.
In conclusion, the big data sources of today(and tomorrow) generally have ten characteristics.
In this case,the Social Security Administration is the always-on big data source.
The most widely discussed feature of big data sources is that they are BIG. .
Finally, I will describe two strategies for combining survey data and big data sources.
Two approaches that especially benefit from big data sources are natural experiments and matching.
In both cases, however,the researchers had to bring interesting questions to the big data source;
To conclude, many big data sources are not representative samples from some well-defined population.
Next, use that model to impute the survey answers of everyone in the big data source.
Even though each big data source is distinct, it is helpful to notice that there are certain characteristics that tend to occur over and over again.
Amplified asking using a predictive model to combine survey data from a few people with a big data source from many people.
In enriched asking,survey data builds context around a big data source that contains some important measurements but lack others.
In enriched asking, a big data source contains some important measurements but lacks other measurements so the researcher collects these missing measurements in a survey and then links the two data sources together.
First, for the people in both data sources, build a machine learning model that uses the big data source to predict survey answers.
The three sources of drift mean that any pattern in a big data source could be caused by an important change in the world, or it could be caused by some form of drift.
The ingredients are(1) a big data source that is wide but thin(i.e., it has many people but not the information that you need about each person) and(2) a survey that is narrow but thick(i.e., it has only a few people, but it does have the information that you need about those people).
In amplified asking,a researcher uses a predictive model to combine a small amount of survey data with a big data source in order to produce estimates at a scale or granularity that would not be possible with either data source individually.
Farber's study was close to a best-case scenario for a research using a big data source because the data that were collected by the city were pretty close to the data that Farber would have collected(one difference is that Farber would have wanted data on total wages-fares plus tips-but the city data only included tips paid by credit card).