@Brian Gideon is right. So is @iamgopal. And I am too, when I say "it depends on the nature of the data being modeled and stored in the database".
If it is a list of days with certain attributes/properties for each day, then yes, I would call it denormalized -- and 9 times out of 10 (or more) this will probably be the case. (I recall a database with 13 columns, one for each month in the year and one for total, and at the end of the year the user added 13 more columns for the next year. "Mr. Database", we called him.)
If this is a description of, say, work hours within a week, where each and every time the data is queried you always require the information for each day in the week, then the row would represent one "unit" of data (each column dependant upon the primary key of the table and all that), and it would be counter-productive to split the data into smaller pieces.
And, of course, it might be a combination of the two -- data that was initially normalized down to one row per day, and then intentionally denormalized for performance reasons. Perhaps 9 times out of 10 they do need a weeks' worth of information, and analysis showed massive performance gains by concatenating that data into one row?
As it is, without further information on use and rational I'm siding with @iamgopal, and upvoting him.