Before diving into DP, let us first understand where do we use DP.
The core concept of DP is to avoid repeated work by remembering partial results (results of subproblems). This is very critical in terms of boosting performance and speed of algorithm. Most of the problems in computer science and real world can be solved using DP technique.

In real life scenarios, consider the example where I have to go from home to work everyday. For the first time, I can calculate the shortest path between home and work by considering all possible routes. But, it is not feasible to do the calculation every day. Hence, I will be memorizing that shortest path and will be following that route everyday. In computer science terms, Google Maps will be using DP algorithm to find the shortest paths between two points.

Largest Common Subsequence (LCS) problem  Basis of data comparison problems and to identify plagiarism in the contents.

Longest Increasing Subsequence problem  used in DNA Matching between two individuals. Generally, the DNAs are represented as strings and to form a match between DNAs of two individuals, the algorithm needs to find out the longest increasing sub sequence between them. In cases of DNA match, the longest common substring (LCS) is also found.

Knapsack Problem You have a bag of limited capacity and you decide to go on a challenging trek. Due to the capacity restriction, you can only carry certain items in optimum quantity. How do you select the materials and its quantity in efficient manner so that you don’t miss out on important items? That’s where DP comes into aid.

Apart from the above, DP has found its importance in various fields like Bioinformatics, Operations research, Decision Making, Image Processing, MATLAB, MS Word, MS Excel, Financial Optimisations, Genetics, XML indexing and querying and what not! Read More.