This community-built FAQ covers the “Variable Types” exercise from the lesson “Data Types and Quality”.
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FAQs on the exercise Variable Types
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An interesting course. But it is unclear who the data analyst is. Judging by the examples, this is a scientist, doctor or statistician. And what does the analyst have to do with it then?
Where does the analyst work? I worked in banks, in production, in audit. But there were no analysts there. Everyone worked with their own data. But you can’t call them all analysts. Therefore, the question is who are analysts and in what field do they work? Is it science, statistical organizations, or something else?
Data analysts are involved in organization, collection and analysis of data in order to draw conclusions, make informed decisions and predictions. They need not be statisticians or scientists as such but statisticians and scientists can take up a role of a data analyst. Also I should mention that there is someone called as a data scientist! who may already know the skills required for being a data analyst, but they are more into modelling and statistics, figuring out the unknowns, forming new questions regarding the data.
An analyst may try to solve existing questions or problems using data which can help in making business decisions, in finance or in any other field such as medical science for instance.
The statistical point of view on variable types is really interesting has interesting implications. What the course kind of misses at this point is what makes the scales so different.
E.g. nominal allows to say if something equals or doesn’t equal sth else. Nothing more.
Ordinal already allows to say which element is above > or below < another one. But the distance between the elements is not known!
Interval scale tells you about the distance between two elements - the first metric scale! But there is no natural point of zero - the zero exists but it has no special meaning. 20 degrees Celsius/Fahrenheit are not double as hot as 10 degrees. Relations are not expressed in the distance between the numbers.
Relational scale - now the zero comes in and provides a point of reference. 100 Kelvin are actually double as hot as 50 Kelvin because 0 Kelvin is absolute.