Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist Sampling Theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. This paper gives a brief background on the origins of this idea, reviews the basic mathematical foundations of the sampling theory and compares the different reconstruction schemes. In our work, a signal is generated and sampled using the CS method. Then the original signal is reconstructed using three different reconstruction schemes namely Greedy Iterative (GI), Convex Relaxation (CR), and Iterative Thresholding (IT). The accuracy and the time taken for these three schemes are calculated and compared. It was found that Greedy Iterative took the least time for reconstruction with a lower error rate amongst the three schemes.