filter and resample or resample and smooth?
Could you describe what you want to find in your data -- peaks, trends, model ?
and what kind of noise there is -- $\infty$ possibilities ?
A plot would be good.
Without some more info, I can only say, with others, "try it and see".

As you may know,
a good way to smooth and resample (interpolate) in Python is with
scipy.ndimage.map_coordinates,
along the lines

import numpy as np
from scipy.ndimage import map_coordinates

def smooth_resample( y, newlen ):
    """ resample y to newlen, with B-spline smoothing """
    n = len(y)
    newgrid = np.linspace( 0, n - 1, newlen )
        # e.g. n = 3, newlen = 5 -&gt; [0 .5 1 1.5 2]
    return map_coordinates( y, newgrid, mode="nearest", prefilter=False )


map_coordinates also works for 2d images, 3d voxels ... hence the name "ndimage".
For more, stackoverflow has quite a few Q+A s that use it.

(Cubic-spline interpolation uses the nearest 4 points to each x in newgrid,
whereas dsp smoothing filters are usually much longer.
For smoothing that preserves peaks,
see Savitzky-Golay filters, e.g.
Numerical Recipes p. 771 ff.)