In this paper, we further develop the approach, originating in [14 (arXiv:1311.6765),20 (arXiv:1604.02576)], to "computation-friendly" hypothesis testing and statistical estimation via Convex Programming. Specifically, we focus on estimating a linear or quadratic form of an unknown "signal," known to belong to a given convex compact set, via noisy indirect observations of the signal. Most of the existing theoretical results on the subject deal with precisely stated statistical models and aim at designing statistical inferences and quantifying their performance in a closed analytic form. In contrast to this descriptive (and highly instructive) traditional framework, the approach we promote here can be qualified as operational -- the estimation routines and their risks are yielded by an efficient computation. All we know in advance is that under favorable circumstances to be specified below, the risk of the resulting estimate, whether high or low, is provably near-optimal under the circumstances. As a compensation for the lack of "explanatory power," this approach is applicable to a much wider family of observation schemes than those where "closed form descriptive analysis" is possible. The paper is a follow-up to our paper [20 (arXiv:1604.02576)] dealing with hypothesis testing, in what follows, we apply the machinery developed in this reference to estimating linear and quadratic forms.