**Data Science**: Data Science is a field or domain which includes and involves working with a huge amount of data and uses it for building predictive, prescriptive and prescriptive analytical models. It’s about digging, capturing, (building the model) analyzing(validating the model) and utilizing the data(deploying the best model).

It is an intersection of Data and computing. It is a blend of the field of Computer Science, Business and Statistics together.

**Data Mining**: Data Mining is a technique to extract important and vital information and knowledge from a huge set/libraries of data. It derives insight by carefully extracting, reviewing, and processing the huge data to find out pattern and co-relations which can be important for the business. It is analogous to the gold mining where golds are extracted from rocks and sands.

Below is a table of differences between Data Science and Data Mining:

S.No. | Data Science | Data Mining |
---|---|---|

1 | Data Science is an area. | Data Mining is a technique. |

2 | It is about collection, processing, analyzing and utilizing of data into various operations. It is more conceptual. | It is about extracting the vital and valuable information from the data. |

3 | It is a field of study just like the Computer Science, Applied Statistics or Applied Mathematics. | It is a technique which is a part of the Knowledge Discovery in Data Base processes (KDD). |

4 | The goal is to build data-dominant products for a venture. | The goal is to make data more vital and usable i.e. by extracting only important information. |

5 | It deals with the all types of data i.e. structured, unstructured or semi-structured. | It mainly deals with the structured forms of the data. |

6 | It is a super set of Data Mining as data science consists of Data scrapping, cleaning, visualization, statistics and many more techniques. | It is a sub set of Data Science as mining activities which is in a pipeline of the Data science. |

7 | It is mainly used for scientific purposes. | It is mainly used for business purposes. |

8 | It broadly focuses on the science of the data. | It is more involved with the processes. |