How do I obtain a variance-covariance matrix?

I am trying to maintain the variance-covariance matrix of a 10 asset portfolio of stocks. The data is represented by a column of returns for 10 stocks. I.e.

Stock A Stock B

Return 1 Return 1

Return 2 Return 2

Etc Etc

When I upload my data I have tried the cov(data) function but it will not work. I keep receiving this message:

"Undefined function 'sum' for input arguments of type 'dataset'.

Error in cov (line 154) xc = bsxfun(@minus,x,sum(x,1)/m); % Remove meanUndefined function 'sum' for input arguments of type 'dataset'.

Error in cov (line 154) xc = bsxfun(@minus,x,sum(x,1)/m); % Remove mean"

My question is how do I proceed with finding the average returns of each stock and the variance-covariance matrix?

[jgg]

The issue seems to be the data type of your data. Is it imported as a dataset? You probably need to send it to an array instead.

It might also be a good idea not to use this data type, since it is deprecated with the implementation of table. Use that instead.

[Calum Crichton]

The imported file is an excel, xlsx, spreadsheet. Label names are only in the first row of every column, which Matlab recognises by default. So I'm unsure why I can't obtain the information I want

[jgg]

No, what is the data type of the information you've loaded in. It looks like you're passing the entire dataset object, instead of just the data, but I can't tell without seeing your code.