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The Digital signal processing DSP consists of a digital signal which is a discrete time signal. For discrete time signal time and amplitude both have discrete values. We can say in other words that it takes samples which accept only values from a discrete set. 

It is a countable set which can be mapped one to one into a subset of integers. If the discrete set being , then discrete values can be represented with the help of digital words that are of a finite width. More commonly to say, these discrete values can be represented by floating point or by fixed point words, which are either proportional to the waveform values or companded.

Sampling produces a continuous valued discrete time signal. Quantization replaces each and every sample value by an approximation which is selected from a given discrete set by truncating or rounding. It can be shown that for signal frequencies strictly below the Nyquist limit that the original continuous-valued continuous-time signal can be almost perfectly reconstructed, down to the (often very low) limit set by the quantisation. 

The major courses included in Matlab Digital Signal Processing and Communications are Transform Techniques, Advanced Digital Signal Processing, Random Processes and Time Series Analysis, Wireless Communications and Networks , Radar Signal Processing ,VLSI Signal Processing , CPLD and FPGA Architectures and Application Embedded Real-time Operating Systems etc.