Programmers often confuse with these terms in relation or the understanding that Data Science, Deep learning are subset of machine learning or vice versa.

Let’s Start with definition of each:

**Data Science** is the science of Collecting, Storing, Processing, Describing and Modelling the data. It uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, which are both structured and unstructured.

**Machine learning** is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference which is obtained from data.

**Deep Learning** is the study of neural network algorithms with representation learning. Learning can be supervised, unsupervised. Deep learning uses multiple layers to progressively extract higher level features from the raw input.

*The* interdisciplinary field of data science uses key skills of a wide range of fields including machine learning, statistics, visualization etc. It enables us to identify meaning and appropriate information from huge volumes of data to make informed decisions in technology, science, business etc.

For a simpler view on the relation between these technologies, *artificial intelligence is applied based on machine learning* . And machine learning is a part of data science that draws features from algorithms and statistics to work on the data extracted from and produced by multiple resources. Thus, you can say data science merges together a bunch of algorithms obtained from machine learning to develop a solution, and during the process, lots of ideas from traditional domain expertise, statistics and mathematics are borrowed.